OpenRaiser/Intern-Atlas huggingface dataset:
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a data-free method of extracting Impressions of each class from the classifier's memory. The Deep Learning regime empowers classifiers to extract distinct patterns (or features) of a given class from training data, which is the basis on which they generalize to unseen data. Before deploying these models on critical applications, it is advantageous to visualize the features considered to be essential for classification. Existing visualization methods develop high confidence images consisting of both background and foreground features. This makes it hard to judge what the crucial features of a given class are. In this work, we propose a saliency-driven approach to visualize discriminative features that are considered most important for a given task. Another drawback of existing methods is that confidence of the generated visualizations is increased by creating multiple instances of the given class. We restrict the algorithm to develop a single object per image, which helps further in extracting features of high confidence and also results in better visualizations. We further demonstrate the generation of negative images as naturally fused images of two or more classes.
A Novel Emergency Light Based Smart Building Solution: Design, Implementation and Use Cases
Venue: arXiv cs.NI 2020
Year: 2020

Deployment of Internet of Things (IoT) in smart buildings has received considerable interest from both the academic community and commercial sectors. Unfortunately, widespread adoption of current smart building solutions is inhibited by the high costs associated with installation and maintenance. Moreover, different types of IoT devices from different manufacturers typically form distinct networks and data silos. There is a need to use a common backbone network that facilitates interoperability and seamless data exchange in a uniform way. In this paper, we present EMIoT, a novel solution for smart buildings that breaks these barriers by leveraging existing emergency lighting systems. In EMIoT, we embed a wireless LoRa module in each emergency light to turn them into wireless routers. EMIoT has been deployed in more than 50 buildings of different types in Sydney Australia and has been successfully running over two years. We present the design and implementation of EMIoT in this paper. Moreover, we use the deployment in a residential building as a use case to show the performance of EMIoT in real-world environments and share lessons learned. Finally, we discuss the advantages and disadvantages of EMIoT. This paper provides practical insights for IoT deployment in smart buildings for practitioners and solution providers.
MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs
Venue: arXiv cs.SE 2020
Year: 2020

Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies. However, directly applying grey-box fuzzing to input-dependent multithreaded programs can be extremely inefficient. In practice, multithreading-relevant bugs are usually buried in sophisticated program flows. Meanwhile, the existing grey-box fuzzing techniques do not stress thread-interleavings which affect execution states in multithreaded programs. Therefore, mainstream grey-box fuzzers cannot effectively test problematic segments in multithreaded programs despite they might obtain high code coverage statistics. To this end, we propose MUZZ, a new grey-box fuzzing technique that hunts for bugs in multithreaded programs. MUZZ owns three novel thread-aware instrumentations, namely coverage-oriented instrumentation, thread-context instrumentation, and schedule-intervention instrumentation. During fuzzing, these instrumentations engender runtime feedback to stress execution states caused by thread interleavings. By leveraging the feedback in the dynamic seed selection and execution strategies, MUZZ preserves more valuable seeds that expose bugs in a multithreading context. We evaluate MUZZ on 12 real-world software programs. Experiments show that MUZZ outperforms AFL in both multithreading-relevant seed generation and concurrency-vulnerability detection. Further, by replaying the target programs against the generated seeds, MUZZ also reveals more concurrency-bugs (e.g., data-races, thread-leaks) than AFL. In total, MUZZ detected 8 new concurrency-vulnerabilities and 19 new concurrency-bugs. At the time of writing, 4 CVE IDs have been assigned to the reported issues.
A Consensus Protocol for e-Democracy
Venue: arXiv cs.SI 2020
Year: 2020

Given that Proof-of-Work (PoW) and Proof-of-Stake (PoS) are plutocratic, and other common consensus protocols are mostly permission-based, we look for a consensus protocol that will suit the needs of e-Democracy. In particular, what we need is a distributed ledger that will record and, to the possible extent, execute the public will. We propose a combination of any given permission-based protocol together with a trust graph between the nodes, which supplies the required permission for new nodes. As a result, the consensus protocol reaches consensus at every iteration between a known list of agents and then updates this list between iterations. This paper is based on prior work that shows the conditions under which a community can grow while maintaining a bounded number of byzantines. It combines a permission-based consensus protocol (such as pBFT) with a community expansion algorithm (such as the one in the prior work) to arrive at a consensus protocol in which the set of agents can change in time, while being sybil-resilient.
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching
Venue: arXiv cs.CV 2020
Year: 2020

Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time. Current feature matching pipelines either rely solely on the descriptive capabilities of the feature extractor or need computationally complex optimization schemes. We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures. The theoretical backbone of the method lies within a probabilistic formulation of feature matching and the respective study of physically motivated constraints. A dynamically adaptable local motion model encapsulates groups of features in an efficient data structure. Temporal constraints transfer information of the local motion model across time, thus additionally reducing the search space complexity for matching. DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods while being computationally more efficient.
Neural Composition: Learning to Generate from Multiple Models
Venue: arXiv cs.CL 2020
Year: 2020

Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and biasing of some components to the user's personal preferences. Conventionally, contextual and personalized adaptation for language models, are achieved through class-based factorization, which requires class-annotated data, or through biasing to individual phrases which is limited in scale. In this paper, we propose a system that combines model-defined components, by learning when to activate the generation process from each individual component, and how to combine probability distributions from each component, directly from unlabeled text data.
Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way. GANs work well with differentiable data such as images. However, a suffix is a sequence of categorical items. To this end, we use the Gumbel-Softmax distribution to get a differentiable continuous approximation. The training works by putting one neural network against the other in a two-player game (hence the "adversarial" nature), which leads to generating suffixes close to the ground truth. From the experimental evaluation it emerges that the approach is superior to the baselines in terms of the accuracy of the predicted suffixes and corresponding remaining times, despite using a naive feature encoding and only engineering features based on control flow and events completion time.
The Effect of TCP Variants on the Coexistence of MMORPG and Best-Effort Traffic
Venue: arXiv cs.NI 2020
Year: 2020

We study TCP flows coexistence between Massive Multiplayer Online Role Playing Games (MMORPGs) and other TCP applications, by taking World of Warcraft (WoW) and a file transfer application based on File Transfer Protocol (FTP) as an example. Our focus is on the effects of the sender buffer size and FTP cross-traffic on the queuing delay experienced by the (MMORPG) game traffic. A network scenario corresponding to a real life situation in an ADSL access network has been simulated by using NS2. Three TCP variants, namely TCP SACK, TCP New Reno, and TCP Vegas, have been considered for cross-traffic. The results show that TCP Vegas is able to maintain a constant rate while competing with the game traffic, since it prevents packet loss and high queuing delays by not increasing the sender window size. TCP SACK and TCP New Reno, on the other hand, tend to continuously increase the sender window size, thus potentially allowing higher packet loss and causing undesired delays for the game traffic. In terms of buffer size, we have established that smaller buffers are better for MMORPG applications, while larger buffers contribute to a higher overall delay.
Stellar Resolution: Multiplicatives
Venue: arXiv cs.LO 2020
Year: 2020

We present a new asynchronous model of computation named Stellar Resolution based on first-order unification. This model of computation is obtained as a formalisation of Girard's transcendental syntax programme, sketched in a series of three articles. As such, it is the first step towards a proper formal treatment of Girard's proposal to tackle first-order logic in a proofs-as-program approach. After establishing formal definitions and basic properties of stellar resolution, we explain how it generalises traditional models of computation, such as logic programming and combinatorial models such as Wang tilings. We then explain how it can represent multiplicative proof-structures, their cut-elimination and the correctness criterion of Danos and Regnier. Further use of realisability techniques lead to dynamic semantics for Multiplicative Linear Logic, following previous Geometry of Interaction models.
Toward Campus Mail Delivery Using BDI
Venue: arXiv cs.MA 2020
Year: 2020

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system.
Computer and Network Security
Venue: arXiv cs.CR 2020
Year: 2020

In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis and storage of such humongous volume of data, several new challenges are faced in protecting privacy of sensitive data and securing systems by designing novel schemes for secure authentication, integrity protection, encryption and non-repudiation. Lightweight symmetric key cryptography and adaptive network security algorithms are in demand for mitigating these challenges. This book presents some of the state-of-the-art research work in the field of cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduate and doctoral students who are working in the field of cryptography, network security and security and privacy issues in the Internet of Things (IoT), and machine learning application in security. It will also be useful for faculty members of graduate schools and universities.
Diet deep generative audio models with structured lottery
Venue: arXiv cs.LG 2020
Year: 2020

Deep learning models have provided extremely successful solutions in most audio application fields. However, the high accuracy of these models comes at the expense of a tremendous computation cost. This aspect is almost always overlooked in evaluating the quality of proposed models. However, models should not be evaluated without taking into account their complexity. This aspect is especially critical in audio applications, which heavily relies on specialized embedded hardware with real-time constraints. In this paper, we build on recent observations that deep models are highly overparameterized, by studying the lottery ticket hypothesis on deep generative audio models. This hypothesis states that extremely efficient small sub-networks exist in deep models and would provide higher accuracy than larger models if trained in isolation. However, lottery tickets are found by relying on unstructured masking, which means that resulting models do not provide any gain in either disk size or inference time. Instead, we develop here a method aimed at performing structured trimming. We show that this requires to rely on global selection and introduce a specific criterion based on mutual information. First, we confirm the surprising result that smaller models provide higher accuracy than their large counterparts. We further show that we can remove up to 95% of the model weights without significant degradation in accuracy. Hence, we can obtain very light models for generative audio across popular methods such as Wavenet, SING or DDSP, that are up to 100 times smaller with commensurate accuracy. We study the theoretical bounds for embedding these models on Raspberry Pi and Arduino, and show that we can obtain generative models on CPU with equivalent quality as large GPU models. Finally, we discuss the possibility of implementing deep generative audio models on embedded platforms.
Thermal Analysis of a 3D Stacked High-Performance Commercial Microprocessor using Face-to-Face Wafer Bonding Technology
Venue: arXiv cs.AR 2020
Year: 2020

3D integration technologies are seeing widespread adoption in the semiconductor industry to offset the limitations and slowdown of two-dimensional scaling. High-density 3D integration techniques such as face-to-face wafer bonding with sub-10 $μ$m pitch can enable new ways of designing SoCs using all 3 dimensions, like folding a microprocessor design across multiple 3D tiers. However, overlapping thermal hotspots can be a challenge in such 3D stacked designs due to a general increase in power density. In this work, we perform a thorough thermal simulation study on sign-off quality physical design implementation of a state-of-the-art, high-performance, out-of-order microprocessor on a 7nm process technology. The physical design of the microprocessor is partitioned and implemented in a 2-tier, 3D stacked configuration with logic blocks and memory instances in separate tiers (logic-over-memory 3D). The thermal simulation model was calibrated to temperature measurement data from a high-performance, CPU-based 2D SoC chip fabricated on the same 7nm process technology. Thermal profiles of different 3D configurations under various workload conditions are simulated and compared. We find that stacking microprocessor designs in 3D without considering thermal implications can result in maximum die temperature up to 12°C higher than their 2D counterparts under the worst-case power-indicative workload. This increase in temperature would reduce the amount of time for which a power-intensive workload can be run before throttling is required. However, logic-over-memory partitioned 3D CPU implementation can mitigate this temperature increase by half, which makes the temperature of the 3D design only 6$^\circ$C higher than the 2D baseline. We conclude that using thermal aware design partitioning and improved cooling techniques can overcome the thermal challenges associated with 3D stacking.
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Venue: arXiv cs.SI 2020
Year: 2020

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However, the process of aligning every pair of nodes between relatively large networks is time-consuming and resource-intensive. In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem. G-CREWE uses node embeddings to align the networks on two levels of resolution, a fine resolution given by the original network and a coarse resolution given by a compressed version, to achieve an efficient and effective network alignment. The framework first extracts node features and learns the node embedding via a Graph Convolutional Network (GCN). Then, node embedding helps to guide the process of graph compression and finally improve the alignment performance. As part of G-CREWE, we also propose a new compression mechanism called MERGE (Minimum dEgRee neiGhbors comprEssion) to reduce the size of the input networks while preserving the consistency in their topological structure. Experiments on all real networks show that our method is more than twice as fast as the most competitive existing methods while maintaining high accuracy.
TA-Dash: An Interactive Dashboard for Spatial-Temporal Traffic Analytics -- Demo Paper
Venue: arXiv cs.HC 2020
Year: 2020

In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard ( TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.
From Data to Knowledge to Action: Enabling the Smart Grid
Venue: arXiv cs.CY 2020
Year: 2020

Our nation's infrastructure for generating, transmitting, and distributing electricity - "The Grid" - is a relic based in many respects on century-old technology. It consists of expensive, centralized generation via large plants, and a massive transmission and distribution system. It strives to deliver high-quality power to all subscribers simultaneously - no matter what their demand - and must therefore be sized to the peak aggregate demand at each distribution point. Ultimately, the system demands end-to-end synchronization, and it lacks a mechanism for storing ("buffering") energy, thus complicating sharing among grids or independent operation during an "upstream" outage. Recent blackouts demonstrate the existing grid's problems - failures are rare but spectacular. Moreover, the structure cannot accommodate the highly variable nature of renewable energy sources such as solar and wind. Many people are pinning their hopes on the "smart grid" - i.e., a more distributed, adaptive, and market-based infrastructure for the generation, distribution, and consumption of electrical energy. This new approach is designed to yield greater efficiency and resilience, while reducing environmental impact, compared to the existing electricity distribution system. Initial plans for the smart grid suggest it will make extensive use of existing information technology. In particular, recent advances in data analytics - i.e., data mining, machine learning, etc. - have the potential to greatly enhance the smart grid and, ultimately, amplify its impact, by helping us make sense of an increasing wealth of data about how we use energy and the kinds of demands that we are placing upon the current energy grid. Here we describe what the electricity grid could look like in 10 years, and specifically how Federal investment in data analytics approaches are critical to realizing this vision.
The Multiplicative-Additive Lambek Calculus with Subexponential and Bracket Modalities
Venue: arXiv cs.LO 2020
Year: 2020

We give a proof-theoretic and algorithmic complexity analysis for systems introduced by Morrill to serve as the core of the CatLog categorial grammar parser. We consider two recent versions of Morrill's calculi, and focus on their fragments including multiplicative (Lambek) connectives, additive conjunction and disjunction, brackets and bracket modalities, and the ! subexponential modality. For both systems, we resolve issues connected with the cut rule and provide necessary modifications, after which we prove admissibility of cut (cut elimination theorem). We also prove algorithmic undecidability for both calculi, and show that categorial grammars based on them can generate arbitrary recursively enumerable languages.
Neural Architecture Search in Graph Neural Networks
Venue: arXiv cs.NE 2020
Year: 2020

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sentences, there is no notion of sequence in networks. Nodes (and edges) follow no absolute order, and it is hard for traditional machine learning (ML) algorithms to recognize a pattern and generalize their predictions on this type of data. Graph Neural Networks (GNN) successfully tackled this problem. They became popular after the generalization of the convolution concept to the graph domain. However, they possess a large number of hyperparameters and their design and optimization is currently hand-made, based on heuristics or empirical intuition. Neural Architecture Search (NAS) methods appear as an interesting solution to this problem. In this direction, this paper compares two NAS methods for optimizing GNN: one based on reinforcement learning and a second based on evolutionary algorithms. Results consider 7 datasets over two search spaces and show that both methods obtain similar accuracies to a random search, raising the question of how many of the search space dimensions are actually relevant to the problem.