OpenRaiser/Intern-Atlas huggingface dataset:
Understanding and Optimizing Persistent Memory Allocation
Venue: arXiv cs.DC 2020
Year: 2020

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich "in-memory" format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle "rolled into" each data structure, it is desirable to make it a separate abstraction. Toward this end, we introduce recoverability, a correctness criterion for persistent allocators, together with a nonblocking allocator, Ralloc, that satisfies this criterion. Ralloc is based on the LRMalloc of Leite and Rocha, with three key innovations. First, we persist just enough information during normal operation to permit correct reconstruction of the heap after a full-system crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, we introduce the notion of filter functions, which identify the locations of pointers within persistent blocks to mitigate the limitations of conservative GC. Third, to allow persistent regions to be mapped at an arbitrary address, we employ position-independent (offset-based) pointers for both data and metadata. Experiments show Ralloc to be performance-competitive with both Makalu, the state-of-the-art lock-based persistent allocator, and such transient allocators as LRMalloc and JEMalloc. In particular, reliance on GC and offline metadata reconstruction allows Ralloc to pay almost nothing for persistence during normal operation.
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Venue: arXiv cs.CV 2020
Year: 2020

Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be smooth or simple to ensure convergence. In this paper, we propose a novel gradient descent type algorithm, by leveraging the idea of residual learning and Nesterov's smoothing technique, to solve inverse problems consisting of general nonconvex and nonsmooth regularization with provable convergence. Moreover, we develop a neural network architecture intimating this algorithm to learn the nonlinear sparsity transformation adaptively from training data, which also inherits the convergence to accommodate the general nonconvex structure of this learned transformation. Numerical results demonstrate that the proposed network outperforms the state-of-the-art methods on a variety of different image reconstruction problems in terms of efficiency and accuracy.
Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem
Venue: arXiv cs.CV 2020
Year: 2020

Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given. When the correspondences between 2D and 3D points are not known a priori, the task becomes the much more challenging blind PnP problem. This paper proposes a deep CNN model which simultaneously solves for both the 6-DoF absolute camera pose and 2D--3D correspondences. Our model comprises three neural modules connected in sequence. First, a two-stream PointNet-inspired network is applied directly to both the 2D image keypoints and the 3D scene points in order to extract discriminative point-wise features harnessing both local and contextual information. Second, a global feature matching module is employed to estimate a matchability matrix among all 2D--3D pairs. Third, the obtained matchability matrix is fed into a classification module to disambiguate inlier matches. The entire network is trained end-to-end, followed by a robust model fitting (P3P-RANSAC) at test time only to recover the 6-DoF camera pose. Extensive tests on both real and simulated data have shown that our method substantially outperforms existing approaches, and is capable of processing thousands of points a second with the state-of-the-art accuracy.
Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game
Venue: arXiv cs.GT 2020
Year: 2020

Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents.
CoinMagic: A Differential Privacy Framework for Ring Signature Schemes
Venue: arXiv cs.CR 2020
Year: 2020

By allowing users to obscure their transactions via including "mixins" (chaff coins), ring signature schemes have been widely used to protect a sender's identity of a transaction in privacy-preserving blockchain systems, like Monero and Bytecoin. However, recent works point out that the existing ring signature scheme is vulnerable to the "chain-reaction" analysis (i.e., the spent coin in a given ring signature can be deduced through elimination). Especially, when the diversity of mixins is low, the spent coin will have a high risk to be detected. To overcome the weakness, the ring signature should be consisted of a set of mixins with high diversity and produce observations having "similar" distributions for any two coins. In this paper, we propose a notion, namely $ε$-coin-indistinguishability ($ε$-CI), to formally define the "similar" distribution guaranteed through a differential privacy scheme. Then, we formally define the CI-aware mixins selection problem with disjoint-superset constraint (CIA-MS-DS), which aims to find a mixin set that has maximal diversity and satisfies the constraints of $ε$-CI and the budget. In CIA-MS-DS, each ring signature is either disjoint with or the superset of its preceding ring signatures. We prove that CIA-MS-DS is NP-hard and thus intractable. To solve the CIA-MS-DS problem, we propose two approximation algorithms, namely the Progressive Algorithm and the Game Theoretic Algorithm, with theoretic guarantees. Through extensive experiments on both real data sets and synthetic data sets, we demonstrate the efficiency and the effectiveness of our approaches.
Can Celebrities Burst Your Bubble?
Venue: arXiv cs.SI 2020
Year: 2020

Polarization is a growing, global problem. As such, many social media based solutions have been proposed in order to reduce it. In this study, we propose a new solution that recommends topics to celebrities to encourage them to join a polarized debate and increase exposure to contrarian content - bursting the filter bubble. Using a state-of-the art model that quantifies the degree of polarization, this paper makes a first attempt to empirically answer the question: Can celebrities burst filter bubbles? We use a case study to analyze how people react when celebrities are involved in a controversial topic and conclude with a list possible research directions.
Memristive Learning Cellular Automata: Theory and Applications
Venue: arXiv cs.ET 2020
Year: 2020

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place.Their nanoscale dimensions and low power consumption enable the further design of various nanoelectronic processing circuits and corresponding computing architectures, like neuromorhpic, in memory, unconventional, etc.One of the possible ways to exploit the memristor's advantages is by combining them with Cellular Automata (CA).CA constitute a well known non von Neumann computing architecture that is based on the local interconnection of simple identical cells forming N-dimensional grids.These local interconnections allow the emergence of global and complex phenomena.In this paper, we propose a hybridization of the CA original definition coupled with memristor based implementation, and, more specifically, we focus on Memristive Learning Cellular Automata (MLCA), which have the ability of learning using also simple identical interconnected cells and taking advantage of the memristor devices inherent variability.The proposed MLCA circuit level implementation is applied on optimal detection of edges in image processing through a series of SPICE simulations, proving its robustness and efficacy.
How to Construct Mutually Orthogonal Complementary Sets with Non-Power-of-Two Lengths?
Venue: arXiv cs.IT 2020
Year: 2020

Mutually orthogonal complementary sets (MOCSs) have received significant research attention in recent years due to their wide applications in communications and radar. Existing MOCSs which are constructed based on generalized Boolean functions (GBFs) mostly have lengths of power-of-two. How to construct MOCSs with non-power-of-two lengths whilst having large set sizes is a largely open problem. With the aid of GBFs, in this paper, we present new constructions of such MOCSs and show that the maximal achievable set size is $1/2$ of the flock size of an MOCS.
Any-Shot Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Previous work on novel object detection considers zero or few-shot settings where none or few examples of each category are available for training. In real world scenarios, it is less practical to expect that 'all' the novel classes are either unseen or {have} few-examples. Here, we propose a more realistic setting termed 'Any-shot detection', where totally unseen and few-shot categories can simultaneously co-occur during inference. Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background. To address these challenges, we propose a unified any-shot detection model, that can concurrently learn to detect both zero-shot and few-shot object classes. Our core idea is to use class semantics as prototypes for object detection, a formulation that naturally minimizes knowledge forgetting and mitigates the class-imbalance in the label space. Besides, we propose a rebalanced loss function that emphasizes difficult few-shot cases but avoids overfitting on the novel classes to allow detection of totally unseen classes. Without bells and whistles, our framework can also be used solely for Zero-shot detection and Few-shot detection tasks. We report extensive experiments on Pascal VOC and MS-COCO datasets where our approach is shown to provide significant improvements.
AVR: Attention based Salient Visual Relationship Detection
Venue: arXiv cs.CV 2020
Year: 2020

Visual relationship detection aims to locate objects in images and recognize the relationships between objects. Traditional methods treat all observed relationships in an image equally, which causes a relatively poor performance in the detection tasks on complex images with abundant visual objects and various relationships. To address this problem, we propose an attention based model, namely AVR, to achieve salient visual relationships based on both local and global context of the relationships. Specifically, AVR recognizes relationships and measures the attention on the relationships in the local context of an input image by fusing the visual features, semantic and spatial information of the relationships. AVR then applies the attention to assign important relationships with larger salient weights for effective information filtering. Furthermore, AVR is integrated with the priori knowledge in the global context of image datasets to improve the precision of relationship prediction, where the context is modeled as a heterogeneous graph to measure the priori probability of relationships based on the random walk algorithm. Comprehensive experiments are conducted to demonstrate the effectiveness of AVR in several real-world image datasets, and the results show that AVR outperforms state-of-the-art visual relationship detection methods significantly by up to $87.5\%$ in terms of recall.
SHINE (Strategies for High-frequency INdoor Environments) with Efficient THz-AP Placement
Venue: arXiv cs.NI 2020
Year: 2020

The increasing demand for ultra-high throughput in ultra-dense networks might take a toll on 5G capacity. Moreover, with Internet-of-Things (IoT) and the growing use-cases for indoor killer-applications, it will be necessary to look beyond 5G technologies. One such promising technology is to move higher in the frequencies, such as the THz ($300$ $GHz$-$10$ $THz$) spectrum. THz has a massive number of greenfield-contiguous channels ranging from $10$ $GHz$ to $200$ $GHz$ (best case), which was not available in the traditional radio frequency (RF) or millimeter wave (mmWave) bands. Although THz has immense potential to cater to such demands, it comes with numerous challenges revolving around hardware, link budget, mobility, blockages, scheduling, and deployment. Terahertz access points (THz-APs) are sensitive to deployment and can critically impact a system\textquotesingle s dynamics (i.e., coverage, throughput, and efficiency). In this paper, we present Strategies for High-frequency INdoor Environments or SHINE, which focuses on efficient AP deployment in the THz spectrum and draws motivation from approaches used indoor to improve lighting conditions. Due to THz\textquotesingle s limited coverage area of a few meters, the number of THz-APs required to satisfy a densely populated room will be higher compared to today's single router/box/AP model. This increased number of THz-APs will not only increase the operational costs, but also (in some cases) can make the system inefficient. Through SHINE, we explore the deployment-related challenges and propose strategies to mitigate the same.
Deep Neural Review Text Interaction for Recommendation Systems
Venue: arXiv cs.IR 2020
Year: 2020

Users' reviews contain valuable information which are not taken into account in most recommender systems. According to the latest studies in this field, using review texts could not only improve the performance of recommendation, but it can also alleviate the impact of data sparsity and help to tackle the cold start problem. In this paper, we present a neural recommender model which recommends items by leveraging user reviews. In order to predict user rating for each item, our proposed model, named MatchPyramid Recommender System (MPRS), represents each user and item with their corresponding review texts. Thus, the problem of recommendation is viewed as a text matching problem such that the matching score obtained from matching user and item texts could be considered as a good representative of their joint extent of similarity. To solve the text matching problem, inspired by MatchPyramid (Pang, 2016), we employed an interaction-based approach according to which a matching matrix is constructed given a pair of input texts. The matching matrix, which has the property of hierarchical matching patterns, is then fed into a Convolutional Neural Network (CNN) to compute the matching score for the given user-item pair. Our experiments on the small data categories of Amazon review dataset show that our proposed model gains from 1.76% to 21.72% relative improvement compared to DeepCoNN model, and from 0.83% to 3.15% relative improvement compared to TransNets model. Also, on two large categories, namely AZ-CSJ and AZ-Mov, our model achieves relative improvements of 8.08% and 7.56% compared to the DeepCoNN model, and relative improvements of 1.74% and 0.86% compared to the TransNets model, respectively.
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
Venue: arXiv cs.CY 2020
Year: 2020

Background: The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this Infodemic requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. Objective: We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. Methods: We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. Results: A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot Satya increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. Conclusion: We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation.
Blockchain based Decentralized Applications: Technology Review and Development Guidelines
Venue: arXiv cs.DC 2020
Year: 2020

Blockchain or Distributed Ledger Technology is a disruptive technology that provides the infrastructure for developing decentralized applications enabling the implementation of novel business models even in traditionally centralized domains. In the last years it has drawn high interest from the academic community, technology developers and startups thus lots of solutions have been developed to address blockchain technology limitations and the requirements of applications software engineering. In this paper, we provide a comprehensive overview of DLT solutions analyzing the addressed challenges, provided solutions and their usage for developing decentralized applications. Our study reviews over 100 blockchain papers and startup initiatives from which we construct a 3-tier based architecture for decentralized applications and we use it to systematically classify the technology solutions. Protocol and Network Tier solutions address the digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scaling Tier solutions address the scalability problems in terms of storage size, transaction throughput, and computational capability. Finally, Federated Tier aggregates integrative solutions across multiple blockchain applications deployments. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design of traditional systems and implementing decentralized applications.
FragNet: Writer Identification using Deep Fragment Networks
Venue: arXiv cs.CV 2020
Year: 2020

Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet has two pathways: feature pyramid which is used to extract feature maps and fragment pathway which is trained to predict the writer identity based on fragments extracted from the input image and the feature maps on the feature pyramid. We conduct experiments on four benchmark datasets, which show that our proposed method can generate efficient and robust deep representations for writer identification based on both word and page images.
WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles
Venue: arXiv cs.RO 2020
Year: 2020

This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initialization-free for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of $61.7$ cm and the attitude error of $0.92^\circ$.
STITCHER: Correlating Digital Forensic Evidence on Internet-of-Things Devices
Venue: arXiv cs.CR 2020
Year: 2020

The increasing adoption of Internet-of-Things (IoT) devices present new challenges to digital forensic investigators and law enforcement agencies when investigation into cybercrime on these new platforms are required. However, there has been no formal study to document actual challenges faced by investigators and whether existing tools help them in their work. Prior issues such as the correlation and consistency problem in digital forensic evidence have also become a pressing concern in light of numerous evidence sources from IoT devices. Motivated by these observations, we conduct a user study with 39 digital forensic investigators from both public and private sectors to document the challenges they faced in traditional and IoT digital forensics. We also created a tool, STITCHER, that addresses the technical challenges faced by investigators when handling IoT digital forensics investigation. We simulated an IoT crime that mimics sophisticated cybercriminals and invited our user study participants to utilize STITCHER to investigate the crime. The efficacy of STITCHER is confirmed by our study results where 96.2% of users indicated that STITCHER assisted them in handling the crime, and 61.5% of users who used STITCHER with its full features solved the crime completely.