OpenRaiser/Intern-Atlas huggingface dataset:
Bit Parallel 6T SRAM In-memory Computing with Reconfigurable Bit-Precision
Venue: arXiv cs.AR 2020
Year: 2020

This paper presents 6T SRAM cell-based bit-parallel in-memory computing (IMC) architecture to support various computations with reconfigurable bit-precision. In the proposed technique, bit-line computation is performed with a short WL followed by BL boosting circuits, which can reduce BL computing delays. By performing carry-propagation between each near-memory circuit, bit-parallel complex computations are also enabled by iterating operations with low latency. In addition, reconfigurable bit-precision is also supported based on carry-propagation size. Our 128KB in/near memory computing architecture has been implemented using a 28nm CMOS process, and it can achieve 2.25GHz clock frequency at 1.0V with 5.2% of area overhead. The proposed architecture also achieves 0.68, 8.09 TOPS/W for the parallel addition and multiplication, respectively. In addition, the proposed work also supports a wide range of supply voltage, from 0.6V to 1.1V.
Nyström Approximation with Nonnegative Matrix Factorization
Venue: arXiv cs.LG 2020
Year: 2020

Motivated by the needs of estimating the proximity clustering with partial distance measurements from vantage points or landmarks for remote networked systems, we show that the proximity clustering problem can be effectively formulated as the Nyström approximation problem, which solves the kernel K-means clustering problem in the complex space. We implement the Nyström approximation based on a landmark based Nonnegative Matrix Factorization (NMF) process. Evaluation results show that the proposed method finds nearly optimal clustering quality on both synthetic and real-world data sets as we vary the range of parameter choices and network conditions.
Security for People with Mental Illness in Telehealth Systems: A Proposal
Venue: arXiv cs.CY 2020
Year: 2020

A mental health crisis is looming large, and needs to be addressed. But across age groups, even just in the United States, more than 50% of people with any mental illness (AMI) did not seek or receive any service or treatment. The proliferation of telehealth and telepsychiatry tools and systems can help address this crisis, but outside of traditional regulatory aspects on privacy, e.g. Health Insurance Portability and Accountability Act (HIPPA), there does not seem to be enough attention on the security needs, concerns, or user experience of people with AMI using those telehealth systems. In this text, I try to explore some priority security properties for telehealth systems used by people with AMI for mental heath services (MHS). I will also suggest some key steps in a proposed process for designing and building security mechanisms into such systems, so that security is accessible and usable to patients with AMI, and these systems can achieve their goals of ameliorate this mental health crisis.
Opportunistic Cooperation Strategies for Multiple Access Relay Channels with Compute-and-Forward
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the application of compute-and-forward to multiple access relay channels (MARC). Despite its promising advantage of improving network throughput, it is not straightforward to apply compute-and-forward to the MARC. This paper proposes two efficient cooperation strategies for the MARC with compute-and-forward. Both proposed strategies are opportunistic in the sense that the cooperation between the relay and the destination are performed only when it is needed for the sake of high transmission efficiency. In the first strategy, the relay helps the destination by sending its local optimal integer coefficient vector without taking into account that the resulting integer coefficient matrix at the destination is full rank. In contrast, in the second strategy, the relay forwards its ``best'' coefficient vector that always ensures a full rank coefficient matrix at the destination. Both of the proposed strategies achieve twice the network throughput of the existing strategies at high signal-to-noise power ratio (SNR). They also have lower outage probability, independent of relay placement. Furthermore, the first strategy nearly achieves diversity gain of order two, and the second one achieves exactly the diversity gain of order two, which cannot be achieved by the existing strategies.
Parameterized Complexity of $(A,\ell)$-Path Packing
Venue: arXiv cs.DS 2020
Year: 2020

Given a graph $G = (V,E)$, $A \subseteq V$, and integers $k$ and $\ell$, the \textsc{$(A,\ell)$-Path Packing} problem asks to find $k$ vertex-disjoint paths of length $\ell$ that have endpoints in $A$ and internal points in $V \setminus A$. We study the parameterized complexity of this problem with parameters $|A|$, $\ell$, $k$, treewidth, pathwidth, and their combinations. We present sharp complexity contrasts with respect to these parameters. Among other results, we show that the problem is polynomial-time solvable when $\ell \le 3$, while it is NP-complete for constant $\ell \ge 4$. We also show that the problem is W[1]-hard parameterized by pathwidth${}+|A|$, while it is fixed-parameter tractable parameterized by treewidth${}+\ell$.
Cyrus 2D Simulation Team Description Paper2018
Venue: arXiv cs.RO 2020
Year: 2020

Cyrus 2D Soccer Simulation was established 2012 with the aim of research and develop in multi agents systems. This year we have joined with Ziziphus for collaboration and speed up our researches. This paper express a brief description of a method for predicting player's behavior in a multi agent system using neural network with a dataset in three level (low, mid, high). The dataset was obtained from log files of past years RoboCup's matches. Behavior Prediction is used in block, mark and defensive decisions. The base code that Cyrus used is agent 3.11.
RPT: Learning Point Set Representation for Siamese Visual Tracking
Venue: arXiv cs.CV 2020
Year: 2020

While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an effcient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.
A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning
Venue: arXiv cs.CR 2020
Year: 2020

Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy. It requires workers to arrive at a particular location for task fulfillment. Effective protection of location privacy is essential for workers' enthusiasm and valid task assignment. However, existing SC models with differential privacy usually perturb real-time location data for both partition and data publication. Such a way may produce large perturbations to counting queries that affect assignment success rate and allocation accuracy. This paper proposes a framework (R-HT) for protecting location privacy of workers taking advantage of both real-time and historical data. We simulate locations by sampling the probability distribution learned from historical data, use them for grid partition, and then publish real-time data under this partitioning with differential privacy. This realizes that most privacy budget is allocated to the worker count of each cell and yields an improved Private Spatial Decomposition approach. Moreover, we introduce some strategies for geocast region construction, including quality scoring function and local maximum geocast radius. A series of experimental results on real-world datasets shows that R-HT attains a stable success rate of task assignment, saves performance overhead and fits for dynamic assignment on crowdsourcing platforms.
Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach
Venue: arXiv cs.AI 2020
Year: 2020

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture. This manuscript is under consideration for acceptance in TPLP.
Single-Shot Two-Pronged Detector with Rectified IoU Loss
Venue: arXiv cs.CV 2020
Year: 2020

In the CNN based object detectors, feature pyramids are widely exploited to alleviate the problem of scale variation across object instances. These object detectors, which strengthen features via a top-down pathway and lateral connections, are mainly to enrich the semantic information of low-level features, but ignore the enhancement of high-level features. This can lead to an imbalance between different levels of features, in particular a serious lack of detailed information in the high-level features, which makes it difficult to get accurate bounding boxes. In this paper, we introduce a novel two-pronged transductive idea to explore the relationship among different layers in both backward and forward directions, which can enrich the semantic information of low-level features and detailed information of high-level features at the same time. Under the guidance of the two-pronged idea, we propose a Two-Pronged Network (TPNet) to achieve bidirectional transfer between high-level features and low-level features, which is useful for accurately detecting object at different scales. Furthermore, due to the distribution imbalance between the hard and easy samples in single-stage detectors, the gradient of localization loss is always dominated by the hard examples that have poor localization accuracy. This will enable the model to be biased toward the hard samples. So in our TPNet, an adaptive IoU based localization loss, named Rectified IoU (RIoU) loss, is proposed to rectify the gradients of each kind of samples. The Rectified IoU loss increases the gradients of examples with high IoU while suppressing the gradients of examples with low IoU, which can improve the overall localization accuracy of model. Extensive experiments demonstrate the superiority of our TPNet and RIoU loss.
Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation
Venue: arXiv cs.CV 2020
Year: 2020

Binary Convolutional Neural Networks (CNNs) can significantly reduce the number of arithmetic operations and the size of memory storage, which makes the deployment of CNNs on mobile or embedded systems more promising. However, the accuracy degradation of single and multiple binary CNNs is unacceptable for modern architectures and large scale datasets like ImageNet. In this paper, we proposed a Piecewise Approximation (PA) scheme for multiple binary CNNs which lessens accuracy loss by approximating full precision weights and activations efficiently and maintains parallelism of bitwise operations to guarantee efficiency. Unlike previous approaches, the proposed PA scheme segments piece-wisely the full precision weights and activations, and approximates each piece with a scaling coefficient. Our implementation on ResNet with different depths on ImageNet can reduce both Top-1 and Top-5 classification accuracy gap compared with full precision to approximately 1.0%. Benefited from the binarization of the downsampling layer, our proposed PA-ResNet50 requires less memory usage and two times Flops than single binary CNNs with 4 weights and 5 activations bases. The PA scheme can also generalize to other architectures like DenseNet and MobileNet with similar approximation power as ResNet which is promising for other tasks using binary convolutions. The code and pretrained models will be publicly available.
Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data
Venue: arXiv cs.NE 2020
Year: 2020

This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 sec) than PSO (average of 95.72 sec). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
Venue: arXiv cs.AI 2020
Year: 2020

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP.
From Rain Generation to Rain Removal
Venue: arXiv cs.CV 2020
Year: 2020

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper, to further improve the deraining performance, we novelly attempt to handle the SIRR task from the perspective of training datasets by exploring a more efficient way to synthesize rainy images. Specifically, we build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator with the input as some latent variables representing the physical structural rain factors, e.g., direction, scale, and thickness. To solve this model, we employ the variational inference framework to approximate the expected statistical distribution of rainy image in a data-driven manner. With the learned generator, we can automatically and sufficiently generate diverse and non-repetitive training pairs so as to efficiently enrich and augment the existing benchmark datasets. User study qualitatively and quantitatively evaluates the realism of generated rainy images. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution that not only helps significantly improve the deraining performance of current deep single image derainers, but also largely loosens the requirement of large training sample pre-collection for the SIRR task.
PolyScope: Multi-Policy Access Control Analysis to Triage Android Systems
Venue: arXiv cs.CR 2020
Year: 2020

Android filesystem access control provides a foundation for Android system integrity. Android utilizes a combination of mandatory (e.g., SEAndroid) and discretionary (e.g., UNIX permissions) access control, both to protect the Android platform from Android/OEM services and to protect Android/OEM services from third-party apps. However, OEMs often create vulnerabilities when they introduce market-differentiating features because they err when re-configuring this complex combination of Android policies. In this paper, we propose the PolyScope tool to triage the combination of Android filesystem access control policies to vet releases for vulnerabilities. The PolyScope approach leverages two main insights: (1) adversaries may exploit the coarse granularity of mandatory policies and the flexibility of discretionary policies to increase the permissions available to launch attacks, which we call permission expansion, and (2) system configurations may limit the ways adversaries may use their permissions to launch attacks, motivating computation of attack operations. We apply PolyScope to three Google and five OEM Android releases to compute the attack operations accurately to vet these releases for vulnerabilities, finding that permission expansion increases the permissions available to launch attacks, sometimes by more than 10X, but a significant fraction of these permissions (about 15-20%) are not convertible into attack operations. Using PolyScope, we find two previously unknown vulnerabilities, showing how PolyScope helps OEMs triage the complex combination of access control policies down to attack operations worthy of testing.
MODEL: Motif-based Deep Feature Learning for Link Prediction
Venue: arXiv cs.SI 2020
Year: 2020

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms by 20% and the state-of-the-art embedding-based algorithms by 19%.
A Flexible Pipeline for the Optimization of CSG Trees
Venue: arXiv cs.AI 2020
Year: 2020

CSG trees are an intuitive, yet powerful technique for the representation of geometry using a combination of Boolean set-operations and geometric primitives. In general, there exists an infinite number of trees all describing the same 3D solid. However, some trees are optimal regarding the number of used operations, their shape or other attributes, like their suitability for intuitive, human-controlled editing. In this paper, we present a systematic comparison of newly developed and existing tree optimization methods and propose a flexible processing pipeline with a focus on tree editability. The pipeline uses a redundancy removal and decomposition stage for complexity reduction and different (meta-)heuristics for remaining tree optimization. We also introduce a new quantitative measure for CSG tree editability and show how it can be used as a constraint in the optimization process.
Randomness Evaluation of a Genetic Algorithm for Image Encryption: A Signal Processing Approach
Venue: arXiv cs.CR 2020
Year: 2020

In this paper a randomness evaluation of a block cipher for secure image communication is presented. The GFHT cipher is a genetic algorithm, that combines gene fusion (GF) and horizontal gene transfer (HGT) both inspired from antibiotic resistance in bacteria. The symmetric encryption key is generated by four pairs of chromosomes with multi-layer random sequences. The encryption starts by a GF of the principal key-agent in a single block, then HGT performs obfuscation where the genes are pixels and the chromosomes are the rows and columns. A Salt extracted from the image hash-value is used to implement one-time pad (OTP) scheme, hence a modification of one pixel generates a different encryption key without changing the main passphrase or key. Therefore, an extreme avalanche effect of 99% is achieved. Randomness evaluation based on random matrix theory, power spectral density, avalanche effect, 2D auto-correlation, pixels randomness tests and chi-square hypotheses testing show that encrypted images adopt the statistical behavior of uniform white noise; hence validating the theoretical model by experimental results. Moreover, performance comparison with chaos-genetic ciphers shows the merit of the GFHT algorithm.
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks
Venue: arXiv cs.CL 2020
Year: 2020

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training. Gradient-based attacks, which are very efficient for images, are hard to be implemented for synonym substitution based text attacks due to the lexical, grammatical and semantic constraints and the discrete text input space. Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. We then incorporate FGPM with adversarial training and propose a text defense method called Adversarial Training with FGPM enhanced by Logit pairing (ATFL). Experiments show that ATFL could significantly improve the model robustness and block the transferability of adversarial examples.