OpenRaiser/Intern-Atlas huggingface dataset:
Hybrid Nano Communications
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we propose a hybrid nano communication (HNC) system that integrates three possible modes of nano communications: terahertz communication (TC), molecular communication (MC), and neural communication (NC). The paper provides some insights into how the required four key building blocks, namely, terahertz to molecular (T2M) relay interface, molecular to terahertz (M2T) interface, molecular to neural (M2N) relay, and neural to molecular (N2M) interface can be designed to implement the HNC system. In addition, the capacity analysis for the hybrid channel is provided. Sub-channels' capacities are also calculated. Finally, numerical results are presented to comprehend the data transmission capacity of the proposed HNC system.
Flow: Separating Consensus and Compute -- Execution Verification
Venue: arXiv cs.DC 2019
Year: 2019

Throughput limitations of existing blockchain architectures are well documented and are one of the most significant hurdles for their wide-spread adoption. In our previous proof-of-concept work, we have shown that separating computation from consensus can provide a significant throughput increase without compromising security. In our architecture, Consensus Nodes only define the transaction order but do not execute transactions. Instead, computing the block result is delegated to compute-optimized Execution Nodes, and dedicated Verification Nodes check the computation result. During normal operation, Consensus Nodes do not inspect the computation but oversee that participating nodes execute their tasks with due diligence and adjudicate potential result challenges. While the architecture can significantly increase throughput, Verification Nodes still have to duplicate the computation fully. In this paper, we refine the architecture such that result verification is distributed and parallelized across many Verification Nodes. The full architecture significantly increases throughput and delegates the computation work to the specialized Execution Nodes and the onus of checking it to a variety of less powerful Verification Nodes. We provide a full protocol specification of the verification process, including challenges to faulty computation results and the resulting adjudication process. Furthermore, we formally prove liveness and safety of the system.
Finding Generalizable Evidence by Learning to Convince Q&A Models
Venue: arXiv cs.CL 2019
Year: 2019

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage. Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans. Given its general nature, this approach improves QA in a robust manner: using agent-selected evidence (i) humans can correctly answer questions with only ~20% of the full passage and (ii) QA models can generalize to longer passages and harder questions.
Unsupervised Image Regression for Heterogeneous Change Detection
Venue: arXiv cs.CV 2019
Year: 2019

Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.
Deterministic Parallel Fixpoint Computation
Venue: arXiv cs.PL 2019
Year: 2019

Abstract interpretation is a general framework for expressing static program analyses. It reduces the problem of extracting properties of a program to computing an approximation of the least fixpoint of a system of equations. The de facto approach for computing this approximation uses a sequential algorithm based on weak topological order (WTO). This paper presents a deterministic parallel algorithm for fixpoint computation by introducing the notion of weak partial order (WPO). We present an algorithm for constructing a WPO in almost-linear time. Finally, we describe PIKOS, our deterministic parallel abstract interpreter, which extends the sequential abstract interpreter IKOS. We evaluate the performance and scalability of PIKOS on a suite of 1017 C programs. When using 4 cores, PIKOS achieves an average speedup of 2.06x over IKOS, with a maximum speedup of 3.63x. When using 16 cores, PIKOS achieves a maximum speedup of 10.97x.
Tracking Down the Bad Guys: Reset and Set Make Feasibility for Flip-Flop Net Derivatives NP-complete
Venue: arXiv cs.CC 2019
Year: 2019

Boolean Petri nets are differentiated by types of nets $τ$ based on which of the interactions nop, inp, out, set, res, swap, used, and free they apply or spare. The synthesis problem relative to a specific type of nets $τ$ is to find a boolean $τ$-net $N$ whose reachability graph is isomorphic to a given transition system $A$. The corresponding decision version of this search problem is called feasibility. Feasibility is known to be polynomial for all types of flip flop derivates that contain at least the interactions nop, swap and an arbitrary selection of inp, out, used, free. In this paper, we replace inp and out by res and set, respectively, and show that feasibility becomes NP-complete for the types that contain nop, swap and a non empty selection of res, set and a non empty selection of used, free. The reduction guarantees a low degree for A's states and, thus, preserves hardness of feasibility even for considerable input restrictions.
Rusty Variation: Deadlock-free Sessions with Failure in Rust
Venue: arXiv cs.PL 2019
Year: 2019

Rusty Variation (RV) is a library for session-typed communication in Rust which offers strong compile-time correctness guarantees. Programs written using RV are guaranteed to respect a specified protocol, and are guaranteed to be free from deadlocks and races.
Debreach: Mitigating Compression Side Channels via Static Analysis and Transformation
Venue: arXiv cs.CR 2019
Year: 2019

Compression is an emerging source of exploitable side-channel leakage that threatens data security, particularly in web applications where compression is indispensable for performance reasons. Current approaches to mitigating compression side channels have drawbacks in that they either degrade compression ratio drastically or require too much effort from developers to be widely adopted. To bridge the gap, we develop Debreach, a static analysis and program transformation based approach to mitigating compression side channels. Debreach consists of two steps. First, it uses taint analysis to soundly identify flows of sensitive data in the program and uses code instrumentation to annotate data before feeding them to the compressor. Second, it enhances the compressor to exploit the freedom to not compress of standard compression protocols, thus removing the dependency between sensitive data and the size of the compressor's output. Since Debreach automatically instruments applications and does not change the compression protocols, it has the advantage of being non-disruptive and compatible with existing systems. We have evaluated Debreach on a set of web server applications written in PHP. Our experiments show that, while ensuring leakage-freedom, Debreach can achieve significantly higher compression performance than state-of-the-art approaches.
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

We present a mobile application made to recognize food items of multi-object meal from a single image in real-time, and then return the nutrition facts with components and approximate amounts. Our work is organized in two parts. First, we build a deep convolutional neural network merging with YOLO, a state-of-the-art detection strategy, to achieve simultaneous multi-object recognition and localization with nearly 80% mean average precision. Second, we adapt our model into a mobile application with extending function for nutrition analysis. After inferring and decoding the model output in the app side, we present detection results that include bounding box position and class label in either real-time or local mode. Our model is well-suited for mobile devices with negligible inference time and small memory requirements with a deep learning algorithm.
Deep Learned Path Planning via Randomized Reward-Linked-Goals and Potential Space Applications
Venue: arXiv cs.LG 2019
Year: 2019

Space exploration missions have seen use of increasingly sophisticated robotic systems with ever more autonomy. Deep learning promises to take this even a step further, and has applications for high-level tasks, like path planning, as well as low-level tasks, like motion control, which are critical components for mission efficiency and success. Using deep reinforcement end-to-end learning with randomized reward function parameters during training, we teach a simulated 8 degree-of-freedom quadruped ant-like robot to travel anywhere within a perimeter, conducting path plan and motion control on a single neural network, without any system model or prior knowledge of the terrain or environment. Our approach also allows for user specified waypoints, which could translate well to either fully autonomous or semi-autonomous/teleoperated space applications that encounter delay times. We trained the agent using randomly generated waypoints linked to the reward function and passed waypoint coordinates as inputs to the neural network. Such applications show promise on a variety of space exploration robots, including high speed rovers for fast locomotion and legged cave robots for rough terrain.
Optimisation of Air-Ground Swarm Teaming for Target Search, using Differential Evolution
Venue: arXiv cs.NE 2019
Year: 2019

This paper presents a swarm teaming perspective that enhances the scope of classic investigations on survivable networks. A target searching generic context is considered as test-bed, in which a swarm of ground agents and a swarm of UAVs cooperate so that the ground agents reach as many targets as possible in the field while also remaining connected as much as possible at all times. To optimise the system against both these objectives in the same time, we use an evolutionary computation approach in the form of a differential evolution algorithm. Results are encouraging, showing a good evolution of the fitness function used as part of the differential evolution, and a good performance of the evolved dual-swarm system, which exhibits an optimal trade-off between target reaching and connectivity.
Deep Joint Embeddings of Context and Content for Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

This paper proposes a deep learning-based method for learning joint context-content embeddings (JCCE) with a view to context-aware recommendations, and demonstrate its application in the television domain. JCCE builds on recent progress within latent representations for recommendation and deep metric learning. The model effectively groups viewing situations and associated consumed content, based on supervision from 2.7 million viewing events. Experiments confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Furthermore, the approach shows meaningful structures in the learned representations that can be used to gain valuable insights of underlying factors in the relationship between contextual settings and content properties.
Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.
A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems
Venue: arXiv cs.MA 2019
Year: 2019

Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of a number of distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that is able to explicitly model a problem containing continuous variables. Nevertheless, the state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm Based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.
A Neural Approach to Irony Generation
Venue: arXiv cs.CL 2019
Year: 2019

Ironies can not only express stronger emotions but also show a sense of humor. With the development of social media, ironies are widely used in public. Although many prior research studies have been conducted in irony detection, few studies focus on irony generation. The main challenges for irony generation are the lack of large-scale irony dataset and difficulties in modeling the ironic pattern. In this work, we first systematically define irony generation based on style transfer task. To address the lack of data, we make use of twitter and build a large-scale dataset. We also design a combination of rewards for reinforcement learning to control the generation of ironic sentences. Experimental results demonstrate the effectiveness of our model in terms of irony accuracy, sentiment preservation, and content preservation.
Reasoning about call-by-value: a missing result in the history of Hoare's logic
Venue: arXiv cs.LO 2019
Year: 2019

We provide a sound and relatively complete Hoare-like proof system for reasoning about partial correctness of recursive procedures in presence of local variables and the call-by-value parameter mechanism, and in which the correctness proofs are linear in the length of the program. We argue that in spite of the fact that Hoare-like proof systems for recursive procedures were intensively studied, no such proof system has been proposed in the literature.
Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm
Venue: arXiv cs.FL 2019
Year: 2019

Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things, systems are more and more often being monitored via advanced sensor systems. These sensors produce large amounts of data about the components' failure behaviour, and can, therefore, be fruitfully exploited to learn reliability models automatically. This paper presents an effective algorithm for learning a prominent class of reliability models, namely fault trees, from observational data. Our algorithm is evolutionary in nature; i.e., is an iterative, population-based, randomized search method among fault-tree structures that are increasingly more consistent with the observational data. We have evaluated our method on a large number of case studies, both on synthetic data, and industrial data. Our experiments show that our algorithm outperforms other methods and provides near-optimal results.
Crank up the volume: preference bias amplification in collaborative recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.
Learning Household Task Knowledge from WikiHow Descriptions
Venue: arXiv cs.CL 2019
Year: 2019

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly rule- and template-based, recent advances in deep learning provide the possibility of acquiring such knowledge directly from natural language sources. As a first step in this direction, we propose a model to learn embeddings for tasks, as well as the individual steps that need to be taken to solve them, based on WikiHow articles. We learn these embeddings such that they are predictive of both step relevance and step ordering. We also experiment with the use of integer programming for inferring consistent global step orderings from noisy pairwise predictions.
Towards A Robot Explanation System: A Survey and Our Approach to State Summarization, Storage and Querying, and Human Interface
Venue: arXiv cs.RO 2019
Year: 2019

As robot systems become more ubiquitous, developing understandable robot systems becomes increasingly important in order to build trust. In this paper, we present an approach to developing a holistic robot explanation system, which consists of three interconnected components: state summarization, storage and querying, and human interface. To find trends towards and gaps in the development of such an integrated system, a literature review was performed and categorized around those three components, with a focus on robotics applications. After the review of each component, we discuss our proposed approach for robot explanation. Finally, we summarize the system as a whole and review its functionality.
A Random Network Model for the Analysis of Blockchain Designs with Communication Delay
Venue: arXiv cs.DC 2019
Year: 2019

This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. The model is parametric on two probability distribution functions governing block production and communication delay, which are key to capture the complexity of the mechanism used to synchronize the many distributed local copies of a blockchain. The proposed model is equipped with simulation algorithms for both bounded and unbounded number of distributed copies of the blockchain. They are used to study fast blockchain systems, i.e., blockchains in which the average time of block production can match the average time of message broadcasting used for blockchain synchronization. In particular, the model and the algorithms are useful to understand efficiency criteria associated with fast blockchains for identifying, e.g., when increasing the block production will have negative impact on the stability of the distributed data structure given the network's broadcast delay.
Optimization on the Surface of the (Hyper)-Sphere
Venue: arXiv cs.CG 2019
Year: 2019

Thomson problem is a classical problem in physics to study how $n$ number of charged particles distribute themselves on the surface of a sphere of $k$ dimensions. When $k=2$, i.e. a 2-sphere (a circle), the particles appear at equally spaced points. Such a configuration can be computed analytically. However, for higher dimensions such as $k \ge 3$, i.e. the case of 3-sphere (standard sphere), there is not much that is understood analytically. Finding global minimum of the problem under these settings is particularly tough since the optimization problem becomes increasingly computationally intensive with larger values of $k$ and $n$. In this work, we explore a wide variety of numerical optimization methods to solve the Thomson problem. In our empirical study, we find stochastic gradient based methods (SGD) to be a compelling choice for this problem as it scales well with the number of points.
HyEdge: Optimal Request Scheduling in Hybrid Edge Computing Environment
Venue: arXiv cs.DC 2019
Year: 2019

With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been done to provision some merging public network services at the edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this paper, we propose the framework of hybrid edge computing to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers, so as to minimize the response delay. This problem is further modeled as a mixed integer non-linear problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our methods. The results indicate that the proposed method achieves elegant performance with low computation complexity.