OpenRaiser/Intern-Atlas huggingface dataset:
NNgSAT: Neural Network guided SAT Attack on Logic Locked Complex Structures
Venue: arXiv cs.CR 2020
Year: 2020

The globalization of the IC supply chain has raised many security threats, especially when untrusted parties are involved. This has created a demand for a dependable logic obfuscation solution to combat these threats. Amongst a wide range of threats and countermeasures on logic obfuscation in the 2010s decade, the Boolean satisfiability (SAT) attack, or one of its derivatives, could break almost all state-of-the-art logic obfuscation countermeasures. However, in some cases, particularly when the logic locked circuits contain complex structures, such as big multipliers, large routing networks, or big tree structures, the logic locked circuit is hard-to-be-solved for the SAT attack. Usage of these structures for obfuscation may lead a strong defense, as many SAT solvers fail to handle such complexity. However, in this paper, we propose a neural-network-guided SAT attack (NNgSAT), in which we examine the capability and effectiveness of a message-passing neural network (MPNN) for solving these complex structures (SAT-hard instances). In NNgSAT, after being trained as a classifier to predict SAT/UNSAT on a SAT problem (NN serves as a SAT solver), the neural network is used to guide/help the actual SAT solver for finding the SAT assignment(s). By training NN on conjunctive normal forms (CNFs) corresponded to a dataset of logic locked circuits, as well as fine-tuning the confidence rate of the NN prediction, our experiments show that NNgSAT could solve 93.5% of the logic locked circuits containing complex structures within a reasonable time, while the existing SAT attack cannot proceed the attack flow in them.
Visualizing a Large Spatiotemporal Collection of Historic Photography with a Generous Interface
Venue: arXiv cs.HC 2020
Year: 2020

Museums, libraries, and other cultural institutions continue to prioritize and build web-based visualization systems that increase access and discovery to digitized archives. Prominent examples exist that illustrate impressive visualizations of a particular feature of a collection. For example, interactive maps showing geographic spread or timelines capturing the temporal aspects of collections. By way of a case study, this paper presents a new web-based visualization system that allows users to simultaneously explore a large collection of images along several different dimensions---spatial, temporal, visual, textual, and through additional metadata fields including the photographer name---guided by the concept of generous interfaces. The case study is a complete redesign of a previously released digital, public humanities project called Photogrammar (2014). The paper highlights the redesign's interactive visualizations that are now possible by the affordances of newly available software. All of the code is open-source in order to allow for re-use of the codebase to other collections with a similar structure.
Firmware over-the-air programming techniques for IoT networks -- A survey
Venue: arXiv cs.NI 2020
Year: 2020

The devices forming the Internet-of-Things (IoT) networks need to be re-programmed over-the-air, so that new features are added, software bugs or security vulnerabilities are resolved and their applications can be re-purposed. The limitations of IoT devices, such as installation in locations with limited physical access, resource-constraint nature, large scale and high heterogeneity, should be taken into consideration for designing an efficient and reliable pipeline for over-the-air programming (OTAP). In this work, we present a survey of OTAP techniques, which can be applied to IoT networks. We highlight the main challenges and limitations of OTAP for IoT devices and analyse the essential steps of firmware update process, along with different approaches and techniques that implement them. In addition, we discuss schemes that focus on securing the OTAP process. Finally, we present a collection of state-of-the art open-source and commercial platforms that integrate secure and reliable OTAP.
SlackQ : Approaching the Qubit Mapping Problem with A Slack-aware Swap Insertion Scheme
Venue: arXiv cs.ET 2020
Year: 2020

The rapid progress of physical implementation of quantum computers paved the way for the design of tools to help users write quantum programs for any given quantum device. The physical constraints inherent in current NISQ architectures prevent most quantum algorithms from being directly executed on quantum devices. To enable two-qubit gates in the algorithm, existing works focus on inserting SWAP gates to dynamically remap logical qubits to physical qubits. However, their schemes lack consideration of the execution time of generated quantum circuits. In this work, we propose a slack-aware SWAP insertion scheme for the qubit mapping problem in the NISQ era. Our experiments show performance improvement by up to 2.36X at maximum, by 1.62X on average, over 106 representative benchmarks from RevLib, IBM Qiskit , and ScaffCC.
Running Neural Networks on the NIC
Venue: arXiv cs.DC 2020
Year: 2020

In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications, with low overhead. This is particularly important as the data transfer costs to the host system and dedicated machine learning accelerators, e.g., GPUs, can be more expensive than the processing task itself. We design and implement our system -- N3IC -- on two different NICs and we show that it can greatly benefit three different network monitoring use cases that require machine learning inference as first-class-primitive. N3IC can perform inference for millions of network flows per second, while forwarding traffic at 40Gb/s. Compared to an equivalent solution implemented on a general purpose CPU, N3IC can provide 100x lower processing latency, with 1.5x increase in throughput.
"Reduction of Monetary Cost in Cloud Storage System by Using Extended Strict Timed Causal Consistency"
Venue: arXiv cs.DC 2020
Year: 2020

Cloud storage systems have been introduced to provide a scalable, secure, reliable, and highly available data storage environment for the organizations and end-users. Therefore, the service provider should grow in a geographical extent. Consequently, extensive storage service provision requires a replication mechanism. Replication imposes many costs on the cloud storage, including the synchronization, communications, storage, etc., costs among the replicas. Moreover, the synchronization process among replicas is a major challenge in cloud storage. Therefore, consistency can be defined as the coordination among the replicas. In this paper, we propose an extension to the strict timed causal consistency by adding the considerations for the monetary costs and the number of violations in the cloud storage systems and call it the extended strict timed causal consistency. Our proposed supports monotonic read, read your write, monotonic write, and write follow read, models by taking into account the causal relations between users' operations, at the client-side. Besides, it supports timed causal at the server-side. We employed the Cassandra cloud database that supports various consistencies such as all, one, quorum, etc. Our method performs better in reducing staleness rate, the severity of violations, and monetary cost in comparison with all, one, quorum, and causal.
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration
Venue: arXiv cs.AR 2020
Year: 2020

Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). Exploiting data sparsity is a common approach to further accelerate GEMM for CNN inference, and in particular, structural sparsity has the advantages of predictable load balancing and very low index overhead. In this paper, we address a key architectural challenge with structural sparsity: how to provide support for a range of sparsity levels while maintaining high utilization of the hardware. We describe a time unrolled formulation of variable density-bound block (VDBB) sparsity that allows for a configurable number of non-zero elements per block, at constant utilization. We then describe a systolic array microarchitecture that implements this scheme, with two data reuse optimizations. Firstly, we increase reuse in both operands and partial products by increasing the number of MACs per PE. Secondly, we introduce a novel approach of moving the IM2COL transform into the hardware, which allows us to achieve a 3x data bandwidth expansion just before the operands are consumed by the datapath, reducing the SRAM power consumption. The optimizations for weight sparsity, activation sparsity and data reuse are all interrelated and therefore the optimal combination is not obvious. Therefore, we perform an design space evaluation to find the pareto-optimal design characteristics. The resulting design achieves 16.8 TOPS/W in 16nm with modest 50% model sparsity and scales with model sparsity up to 55.7TOPS/W at 87.5%. As well as successfully demonstrating the variable DBB technique, this result significantly outperforms previously reported sparse CNN accelerators.
Visualizing the Loss Landscape of Actor Critic Methods with Applications in Inventory Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Continuous control is a widely applicable area of reinforcement learning. The main players of this area are actor-critic methods that utilize policy gradients of neural approximators as a common practice. The focus of our study is to show the characteristics of the actor loss function which is the essential part of the optimization. We exploit low dimensional visualizations of the loss function and provide comparisons for loss landscapes of various algorithms. Furthermore, we apply our approach to multi-store dynamic inventory control, a notoriously difficult problem in supply chain operations, and explore the shape of the loss function associated with the optimal policy. We modelled and solved the problem using reinforcement learning while having a loss landscape in favor of optimality.
A Deep Learning Approach to Tongue Detection for Pediatric Population
Venue: arXiv cs.CV 2020
Year: 2020

Children with severe disabilities and complex communication needs face limitations in the usage of access technology (AT) devices. Conventional ATs (e.g., mechanical switches) can be insufficient for nonverbal children and those with limited voluntary motion control. Automatic techniques for the detection of tongue gestures represent a promising pathway. Previous studies have shown the robustness of tongue detection algorithms on adult participants, but further research is needed to use these methods with children. In this study, a network architecture for tongue-out gesture recognition was implemented and evaluated on videos recorded in a naturalistic setting when children were playing a video-game. A cascade object detector algorithm was used to detect the participants' faces, and an automated classification scheme for tongue gesture detection was developed using a convolutional neural network (CNN). In evaluation experiments conducted, the network was trained using adults and children's images. The network classification accuracy was evaluated using leave-one-subject-out cross-validation. Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 99% in predicting tongue-out gestures. Moreover, we demonstrated that using only children data for training the classifier yielded better performance than adult's one supporting the need for pediatric tongue gesture datasets.
A General Framework for Fairness in Multistakeholder Recommendations
Venue: arXiv cs.AI 2020
Year: 2020

Contemporary recommender systems act as intermediaries on multi-sided platforms serving high utility recommendations from sellers to buyers. Such systems attempt to balance the objectives of multiple stakeholders including sellers, buyers, and the platform itself. The difficulty in providing recommendations that maximize the utility for a buyer, while simultaneously representing all the sellers on the platform has lead to many interesting research problems.Traditionally, they have been formulated as integer linear programs which compute recommendations for all the buyers together in an \emph{offline} fashion, by incorporating coverage constraints so that the individual sellers are proportionally represented across all the recommended items. Such approaches can lead to unforeseen biases wherein certain buyers consistently receive low utility recommendations in order to meet the global seller coverage constraints. To remedy this situation, we propose a general formulation that incorporates seller coverage objectives alongside individual buyer objectives in a real-time personalized recommender system. In addition, we leverage highly scalable submodular optimization algorithms to provide recommendations to each buyer with provable theoretical quality bounds. Furthermore, we empirically evaluate the efficacy of our approach using data from an online real-estate marketplace.
GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder
Venue: arXiv cs.CV 2020
Year: 2020

Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position and velocity signals and train separate deep temporal convolutional autoencoders. The autoencoders learn micro-scale and macro-scale representations that correspond to the fast and slow features of eye movements. We evaluate the joint representations with a linear classifier fitted on various classification tasks. Our work accurately discriminates between gender and age groups, and outperforms previous works on biometrics and stimuli clasification. Further experiments highlight the validity and generalizability of this method, bringing eye tracking research closer to real-world applications.
Bio-inspired Structure Identification in Language Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

Word embeddings are a popular way to improve downstream performances in contemporary language modeling. However, the underlying geometric structure of the embedding space is not well understood. We present a series of explorations using bio-inspired methodology to traverse and visualize word embeddings, demonstrating evidence of discernible structure. Moreover, our model also produces word similarity rankings that are plausible yet very different from common similarity metrics, mainly cosine similarity and Euclidean distance. We show that our bio-inspired model can be used to investigate how different word embedding techniques result in different semantic outputs, which can emphasize or obscure particular interpretations in textual data.
Preserving Minority Structures in Graph Sampling
Venue: arXiv cs.SI 2020
Year: 2020

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
Venue: arXiv cs.LG 2020
Year: 2020

We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited memory. The state-of-the-art deterministic sketching algorithm for streaming AMM is the co-occurring directions (COD), which has much smaller approximation errors than randomized algorithms and outperforms other deterministic sketching methods empirically. In this paper, we provide a tighter error bound for COD whose leading term considers the potential approximate low-rank structure and the correlation of input matrices. We prove COD is space optimal with respect to our improved error bound. We also propose a variant of COD for sparse matrices with theoretical guarantees. The experiments on real-world sparse datasets show that the proposed algorithm is more efficient than baseline methods.
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
Venue: arXiv cs.IR 2020
Year: 2020

Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.
Participatory Budgeting with Cumulative Votes
Venue: arXiv cs.MA 2020
Year: 2020

In participatory budgeting we are given a set of projects---each with a cost, an available budget, and a set of voters who in some form express their preferences over the projects. The goal is to select---based on voter preferences---a subset of projects whose total cost does not exceed the budget. We propose several aggregation methods based on the idea of cumulative votes, e.g., for the setting when each voter is given one coin and she specifies how this coin should be split among the projects. We compare our aggregation methods based on (1) axiomatic properties, and (2) computer simulations. We identify one method, Minimal Transfers over Costs, that demonstrates particularly desirable behavior. In particular, it significantly improves on existing methods, satisfies a strong notion of proportionality, and, thus, is promising to be used in practice.
HPSGD: Hierarchical Parallel SGD With Stale Gradients Featuring
Venue: arXiv cs.LG 2020
Year: 2020

While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers. To alleviate this problem, a novel Hierarchical Parallel SGD (HPSGD) strategy is proposed based on the observation that the data synchronization phase can be paralleled with the local training phase (i.e., Feed-forward and back-propagation). Furthermore, an improved model updating method is unitized to remedy the introduced stale gradients problem, which commits updates to the replica (i.e., a temporary model that has the same parameters as the global model) and then merges the average changes to the global model. Extensive experiments are conducted to demonstrate that the proposed HPSGD approach substantially boosts the distributed DNN training, reduces the disturbance of the stale gradients and achieves better accuracy in given fixed wall-time.
Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication
Venue: arXiv cs.IT 2020
Year: 2020

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naive quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1-3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of 3.9x and 5.8x, respectively.
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data
Venue: arXiv cs.LG 2020
Year: 2020

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w.r.t. training time, accuracy, etc., comparing to idealized non-private VFL. Our work builds on the recent advances in VFL-based collaborative training among different organizations which rely on protocols like Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC) to secure computation and training. In particular, we analyze how VFL's intermediate result (IR) can leak private information of the training data during communication and design a DP-based privacy-preserving algorithm to ensure the data confidentiality of VFL participants. We mathematically prove that our algorithm not only provides utility guarantees for VFL, but also offers multi-level privacy, i.e. DP w.r.t. IR and joint differential privacy (JDP) w.r.t. model weights. Experimental results demonstrate that our work, under adequate privacy budgets, is quantitatively and qualitatively similar to GLMs, learned in idealized non-private VFL setting, rather than the increased cost in memory and processing time in most prior works based on HE or MPC. Our codes will be released if this paper is accepted.