OpenRaiser/Intern-Atlas huggingface dataset:
Abstract Predicate Entailment over Points-To Heaplets is Syntax Recognition
Venue: arXiv cs.LO 2019
Year: 2019

Abstract predicates are considered in this paper as abstraction technique for heap-separated configurations, and as genuine Prolog predicates which are translated straight into a corresponding formal language grammar used as validation scheme for intermediate heap states. The approach presented is rule-based because the abstract predicates are rule-based, the parsing technique can be interpreted as an automated fold/unfold of the corresponding heap graph.
Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approach
Venue: arXiv cs.IT 2019
Year: 2019

The line-of-sight dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAVs exploit a novel interference transmission scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB to cancel their resultant interference at the UAV. To balance between the CB gains for UAV signal enhancement and interference cancellation, we aim to maximize the UAV's receive signal-to-interference-plus-noise ratio by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in a special case and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. As the optimal solution requires centralized implementation with exorbitant message/channel information exchanges among the BSs, we further propose a distributed algorithm that is amenable to practical implementation, based on a new divide-and-conquer approach, whereby each co-channel BS divides its perceived interference to the UAV into multiple portions, each to be canceled by a different serving BS of the UAV with its best effort. Numerical results show that the proposed centralized and distributed CB schemes with interference transmission and cancellation (ITC) can both significantly outperform the conventional CB without applying ITC.
Approximate Quantiles for Datacenter Telemetry Monitoring
Venue: arXiv cs.DB 2019
Year: 2019

Datacenter systems require efficient troubleshooting and effective resource scheduling so as to minimize downtimes and to efficiently utilize limited resources. In doing so, datacenter operators employ streaming analytics for collecting and processing datacenter telemetry over a temporal window. The quantile operator is key to these systems as it can summarize the typical and abnormal behavior of the monitored system. Computing quantiles in real-time is resource-intensive as it requires processing hundreds of millions of events in seconds while providing high accuracy. We overcome these challenges in real-time quantile computation through workload-driven approximation, motivated by three insights in our study: (i) values are dominated by a set of recurring small values, (ii) distribution of small values is consistent across different time scales, and (iii) tail values are dominated by a small set of large values. That is, we propose AOMG, an efficient and accurate quantile approximation algorithm that capitalizes on these insights. AOMG minimizes memory footprint of the quantile operator via compression and frequency-based summarization of small values. While these summaries are stored and processed at sub-window granularity for memory efficiency, they can extend to compute quantiles on user-defined temporal windows. Low value error for tail quantiles is achieved by retaining a few tail values per subwindow. AOMG estimates quantiles with high throughput and less than 5% relative value error across a wide range of use cases while state-of-the-art algorithms either have a high relative value error (9.3-137.0%) or deliver lower throughput (15-92%).
Proportional Fair RAT Aggregation in HetNets
Venue: arXiv cs.NI 2019
Year: 2019

Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.
NeuralDivergence: Exploring and Understanding Neural Networks by Comparing Activation Distributions
Venue: arXiv cs.LG 2019
Year: 2019

As deep neural networks are increasingly used in solving high-stake problems, there is a pressing need to understand their internal decision mechanisms. Visualization has helped address this problem by assisting with interpreting complex deep neural networks. However, current tools often support only single data instances, or visualize layers in isolation. We present NeuralDivergence, an interactive visualization system that uses activation distributions as a high-level summary of what a model has learned. NeuralDivergence enables users to interactively summarize and compare activation distributions across layers, classes, and instances (e.g., pairs of adversarial attacked and benign images), helping them gain better understanding of neural network models.
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat
Venue: arXiv cs.LG 2019
Year: 2019

While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed models. The prediction framework is deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to other social app platforms.
Effect of two forms of feedback on the performance of the Rate Control Protocol (RCP)
Venue: arXiv cs.NI 2019
Year: 2019

The Rate Control Protocol (RCP) uses explicit feedback from routers to control network congestion. RCP estimates it's fair rate from two forms of feedback: rate mismatch and queue size. An important design question that remains open in RCP is whether the presence of queue size feedback is helpful, given the presence of feedback from rate mismatch. The feedback from routers to end-systems is time delayed, and may introduce instabilities and complex non-linear dynamics. Delay dynamical systems are often modeled using delay differential equations to facilitate a mathematical analysis of their performance and dynamics. The RCP models with and without queue size feedback give rise to two distinct non-linear delay differential equations. Earlier work on this design question was based on methods of linear systems theory. For further progress, it is quite natural to employ nonlinear techniques. In this study, we approach this design question using tools from control and bifurcation theory. The analytical results reveal that the removal of queue feedback could enhance both stability and convergence properties. Further, using Poincaré normal forms and center manifold theory, we investigate two nonlinear properties, namely, the type of Hopf bifurcation and the asymptotic stability of the bifurcating limit cycles. We show that the presence of queue feedback in the RCP can lead to a sub-critical Hopf bifurcation, which would give rise either to the onset of large amplitude limit cycles or to unstable limit cycles. Whereas, in the absence of queue feedback, the Hopf bifurcation is always super-critical and the bifurcating limit cycles are stable. The analysis is complemented with computations and some packet-level simulations as well. In terms of design, our study suggests that the presence of both forms of feedback may be detrimental to the performance of RCP.
Mobile Edge Intelligence and Computing for the Internet of Vehicles
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.
Mining Data from the Congressional Record
Venue: arXiv cs.IR 2019
Year: 2019

We propose a data storage and analysis method for using the US Congressional record as a policy analysis tool. We use Amazon Web Services and the Solr search engine to store and process Congressional record data from 1789 to the present, and then query Solr to find how frequently language related to tax increases and decreases appears. This frequency data is compared to six economic indicators. Our preliminary results indicate potential relationships between incidence of tax discussion and multiple indicators. We present our data storage and analysis procedures, as well as results from comparisons to all six indicators.
Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach
Venue: arXiv cs.IT 2019
Year: 2019

We consider an underlay cognitive radio network where the secondary user (SU) harvests energy from the environment. We consider a slotted-mode of operation where each slot of SU is used for either energy harvesting or data transmission. Considering block fading with memory, we model the energy arrival and fading processes as a stationary Markov process of first order. We propose a harvest-or-transmit policy for the SU along with optimal transmit powers that maximize its expected throughput under three different settings. First, we consider a learning-theoretic approach where we do not assume any apriori knowledge about the underlying Markov processes. In this case, we obtain an online policy using Q-learning. Then, we assume that the full statistical knowledge of the governing Markov process is known apriori. Under this assumption, we obtain an optimal online policy using infinite horizon stochastic dynamic programming. Finally, we obtain an optimal offline policy using the generalized Benders decomposition algorithm. The offline policy assumes that for a given time deadline, the energy arrivals and channel states are known in advance at all the transmitters. Finally, we compare all policies and study the effects of various system parameters on the system performance.
An overview of memristive cryptography
Venue: arXiv cs.ET 2019
Year: 2019

Smaller, smarter and faster edge devices in the Internet of things era demands secure data analysis and transmission under resource constraints of hardware architecture. Lightweight cryptography on edge hardware is an emerging topic that is essential to ensure data security in near-sensor computing systems such as mobiles, drones, smart cameras, and wearables. In this article, the current state of memristive cryptography is placed in the context of lightweight hardware cryptography. The paper provides a brief overview of the traditional hardware lightweight cryptography and cryptanalysis approaches. The contrast for memristive cryptography with respect to traditional approaches is evident through this article, and need to develop a more concrete approach to developing memristive cryptanalysis to test memristive cryptographic approaches is highlighted.
Discovering Neural Wirings
Venue: arXiv cs.LG 2019
Year: 2019

The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity patterns are largely constrained. In this work we propose a method for discovering neural wirings. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training -- as we learn the network parameters we also learn the structure itself. Our experiments demonstrate that our learned connectivity outperforms hand engineered and randomly wired networks. By learning the connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs. Moreover, we show that our method generalizes to recurrent and continuous time networks. Our work may also be regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning. As NAS becomes more fine grained, finding a good architecture is akin to finding a sparse subnetwork of the complete graph. Accordingly, DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run. Though we only ever use a small percentage of the weights during the forward pass, we still play the so-called initialization lottery with a combinatorial number of subnetworks. Code and pretrained models are available at https://github.com/allenai/dnw while additional visualizations may be found at https://mitchellnw.github.io/blog/2019/dnw/.
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Venue: arXiv cs.CV 2019
Year: 2019

Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability. Digital pathology images are typically of extremely high resolution, making tilewise analysis necessary for deep learning applications. It has been shown that image generators with instance normalization can cause a tiling artifact when a large image is reconstructed from the tilewise analysis. We introduce a novel perceptual embedding consistency loss significantly reducing the tiling artifact created in the reconstructed whole slide image (WSI). We validate our results by comparing virtually stained slide images with consecutive real stained tissue slide images. We also demonstrate that our model is more robust to contrast, color and brightness perturbations by running comparative sensitivity analysis tests.
How Much Does Audio Matter to Recognize Egocentric Object Interactions?
Venue: arXiv cs.CV 2019
Year: 2019

Sounds are an important source of information on our daily interactions with objects. For instance, a significant amount of people can discern the temperature of water that it is being poured just by using the sense of hearing. However, only a few works have explored the use of audio for the classification of object interactions in conjunction with vision or as single modality. In this preliminary work, we propose an audio model for egocentric action recognition and explore its usefulness on the parts of the problem (noun, verb, and action classification). Our model achieves a competitive result in terms of verb classification (34.26% accuracy) on a standard benchmark with respect to vision-based state of the art systems, using a comparatively lighter architecture.
Kandinsky Patterns
Venue: arXiv cs.AI 2019
Year: 2019

Kandinsky Figures and Kandinsky Patterns are mathematically describable, simple self-contained hence controllable test data sets for the development, validation and training of explainability in artificial intelligence. Whilst Kandinsky Patterns have these computationally manageable properties, they are at the same time easily distinguishable from human observers. Consequently, controlled patterns can be described by both humans and computers. We define a Kandinsky Pattern as a set of Kandinsky Figures, where for each figure an "infallible authority" defines that the figure belongs to the Kandinsky Pattern. With this simple principle we build training and validation data sets for automatic interpretability and context learning. In this paper we describe the basic idea and some underlying principles of Kandinsky Patterns and provide a Github repository to invite the international machine learning research community to a challenge to experiment with our Kandinsky Patterns to expand and thus make progress in the field of explainable AI and to contribute to the upcoming field of explainability and causability.
CNN-based Steganalysis and Parametric Adversarial Embedding: a Game-Theoretic Framework
Venue: arXiv cs.MM 2019
Year: 2019

CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can be restored by retraining the CNN with adversarial stego images. A problem with this model is that, arguably, at training time the steganalizer is not aware of the exact parameters used by the steganograher for adversarial embedding and, vice versa, the steganographer does not know how the images that will be used to train the steganalyzer are generated. In order to exit this apparent deadlock, we introduce a game theoretic framework wherein the problem of setting the parameters of the steganalyzer and the steganographer is solved in a strategic way. More specifically, a non-zero sum game is first formulated to model the problem, and then instantiated by considering a specific adversarial embedding scheme setting its operating parameters in a game-theoretic fashion. Our analysis shows that the equilibrium solution of the non zero-sum game can be conveniently found by solving an associated zero-sum game, thus reducing greatly the complexity of the problem. Then we run several experiments to derive the optimum strategies for the steganographer and the staganalyst in a game-theoretic sense, and to evaluate the performance of the game at the equilibrium, characterizing the loss with respect to the conventional non-adversarial case. Eventually, by leveraging on the analysis of the equilibrium point of the game, we introduce a new strategy to improve the reliability of the steganalysis, which shows the benefits of addressing the security issue in a game-theoretic perspective.
An Adaptive Training-less System for Anomaly Detection in Crowd Scenes
Venue: arXiv cs.CV 2019
Year: 2019

Anomaly detection in crowd videos has become a popular area of research for the computer vision community. Several existing methods generally perform a prior training about the scene with or without the use of labeled data. However, it is difficult to always guarantee the availability of prior data, especially, for scenarios like remote area surveillance. To address such challenge, we propose an adaptive training-less system capable of detecting anomaly on-the-fly while dynamically estimating and adjusting response based on certain parameters. This makes our system both training-less and adaptive in nature. Our pipeline consists of three main components, namely, adaptive 3D-DCT model for multi-object detection-based association, local motion structure description through saliency modulated optic flow, and anomaly detection based on earth movers distance (EMD). The proposed model, despite being training-free, is found to achieve comparable performance with several state-of-the-art methods on the publicly available UCSD, UMN, CHUK-Avenue and ShanghaiTech datasets.
Encryption Scheme Based on Expanded Reed-Solomon Codes
Venue: arXiv cs.CR 2019
Year: 2019

We present a code-based public-key cryptosystem, in which we use Reed-Solomon codes over an extension field as secret codes and disguise it by considering its shortened expanded code over the base field. Considering shortened expanded codes provides a safeguard against distinguisher attacks based on the Schur product. Moreover, without using a cyclic or a quasi-cyclic structure we obtain a key size reduction of nearly $45 \%$ compared to the classic McEliece cryptosystem proposed by Bernstein et al.