OpenRaiser/Intern-Atlas huggingface dataset:
DS3: A System-Level Domain-Specific System-on-Chip Simulation Framework
Venue: arXiv cs.AR 2020
Year: 2020

Heterogeneous systems-on-chip (SoCs) are highly favorable computing platforms due to their superior performance and energy efficiency potential compared to homogeneous architectures. They can be further tailored to a specific domain of applications by incorporating processing elements (PEs) that accelerate frequently used kernels in these applications. However, this potential is contingent upon optimizing the SoC for the target domain and utilizing its resources effectively at runtime. To this end, system-level design - including scheduling, power-thermal management algorithms and design space exploration studies - plays a crucial role. This paper presents a system-level domain-specific SoC simulation (DS3) framework to address this need. DS3 enables both design space exploration and dynamic resource management for power-performance optimization of domain applications. We showcase DS3 using six real-world applications from wireless communications and radar processing domain. DS3, as well as the reference applications, is shared as open-source software to stimulate research in this area.
EchoLock: Towards Low Effort Mobile User Identification
Venue: arXiv cs.HC 2020
Year: 2020

User identification plays a pivotal role in how we interact with our mobile devices. Many existing authentication approaches require active input from the user or specialized sensing hardware, and studies on mobile device usage show significant interest in less inconvenient procedures. In this paper, we propose EchoLock, a low effort identification scheme that validates the user by sensing hand geometry via commodity microphones and speakers. These acoustic signals produce distinct structure-borne sound reflections when contacting the user's hand, which can be used to differentiate between different people based on how they hold their mobile devices. We process these reflections to derive unique acoustic features in both the time and frequency domain, which can effectively represent physiological and behavioral traits, such as hand contours, finger sizes, holding strength, and gesture. Furthermore, learning-based algorithms are developed to robustly identify the user under various environments and conditions. We conduct extensive experiments with 20 participants using different hardware setups in key use case scenarios and study various attack models to demonstrate the performance of our proposed system. Our results show that EchoLock is capable of verifying users with over 90% accuracy, without requiring any active input from the user.
A Migratory Near Memory Processing Architecture Applied to Big Data Problems
Venue: arXiv cs.DB 2020
Year: 2020

Servers produced by mainstream vendors are inefficient in processing Big Data queries due to bottlenecks inherent in the fundamental architecture of these systems. Current server blades contain multicore processors connected to DRAM memory and disks by an interconnection chipset. The multicore processor chips perform all the computations while the DRAM and disks store the data but have no processing capability. To perform a database query, data must be moved back and forth between DRAM and a small cache as well as between DRAM and disks. For Big Data applications this data movement in onerous. Migratory Near Memory Servers address this bottleneck by placing large numbers of lightweight processors directly into the memory system. These processors operate directly on the relations, vertices and edges of Big Data applications in place without having to shuttle large quantities of data back and forth between DRAM, cache and heavyweight multicore processors. This paper addresses the application of such an architecture to relational database SELECT and JOIN queries. Preliminary results indicate end-to-end orders of magnitude speedup.
It's All in the Timing: Principles of Transient Distraction Illustrated with Vibrotactile Tasks
Venue: arXiv cs.HC 2020
Year: 2020

Vibration is an efficient way of conveying information from a device to its user, and it is increasingly used for wrist or finger-worn devices such as smart rings. Unexpected vibrations or sounds from the environment may disrupt the perception of such information. Although disruptive effects have been systematically explored in vision and audition, they have been less examined in the haptic domain. Here we briefly review the relevant literature from HCI and psychology, distilling principles of when distraction is likely. We then investigate these principles through four experiments, examining how the timing and modality of relatively rare or unexpected stimuli (surprise distractors) affects the detection and recognition of vibrotactile target patterns. At short distractor-target delays (< 350 ms), both auditory and vibrotactile surprise distractors impaired performance. At a longer delay (1050 ms), performance was not affected overall, even being improved with repeated exposure to the vibrotactile distractors. We discuss the importance of our findings in the context of HCI and cognitive psychology, and we provide design guidelines for mitigating the effects of distraction on haptic devices.
3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose an end-to-end deep learning network named 3dDepthNet, which produces an accurate dense depth image from a single pair of sparse LiDAR depth and color image for robotics and autonomous driving tasks. Based on the dimensional nature of depth images, our network offers a novel 3D-to-2D coarse-to-fine dual densification design that is both accurate and lightweight. Depth densification is first performed in 3D space via point cloud completion, followed by a specially designed encoder-decoder structure that utilizes the projected dense depth from 3D completion and the original RGB-D images to perform 2D image completion. Experiments on the KITTI dataset show our network achieves state-of-art accuracy while being more efficient. Ablation and generalization tests prove that each module in our network has positive influences on the final results, and furthermore, our network is resilient to even sparser depth.
DeepFake Detection: Current Challenges and Next Steps
Venue: arXiv cs.CV 2020
Year: 2020

High quality fake videos and audios generated by AI-algorithms (the deep fakes) have started to challenge the status of videos and audios as definitive evidence of events. In this paper, we highlight a few of these challenges and discuss the research opportunities in this direction.
GraphChallenge.org Triangle Counting Performance
Venue: arXiv cs.DC 2020
Year: 2020

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, $T_{\rm tri}$, is a strong function of the number of edges in the graph, $N_e$, which improved significantly from 2017 ($T_{\rm tri} \approx (N_e/10^8)^{4/3}$) to 2018 ($T_{\rm tri} \approx N_e/10^9$) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.
Exploring Inherent Properties of the Monophonic Melody of Songs
Venue: arXiv cs.SD 2020
Year: 2020

Melody is one of the most important components in music. Unlike other components in music theory, such as harmony and counterpoint, computable features for melody is urgently in need. These features are highly demanded as data-driven methods dominating the fields such as musical information retrieval and automatic music composition. To boost the performance of deep-learning-related musical tasks, we propose a set of interpretable features on monophonic melody for computational purposes. These features are defined not only in mathematical form, but also with some considerations on composers 'intuition. For example, the Melodic Center of Gravity can reflect the sentence-wise contour of the melody, the local / global melody dynamics quantifies the dynamics of a melody that couples pitch and time in a sentence. We found that these features are considered by people universally in many genres of songs, even for atonal composition practices. Hopefully, these melodic features can provide nov el inspiration for future researchers as a tool in the field of MIR and automatic composition.
Dynamic Averaging Load Balancing on Cycles
Venue: arXiv cs.DS 2020
Year: 2020

We consider the following dynamic load-balancing process: given an underlying graph $G$ with $n$ nodes, in each step $t\geq 0$, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks a random neighbor, and the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on $n$ and on the graph structure. Similar variants of the above graphical balanced allocation process have been studied by Peres, Talwar, and Wieder, and by Sauerwald and Sun for regular graphs. These authors left as open the question of characterizing the gap in the case of \emph{cycle graphs} in the \emph{dynamic} case, where weights are created during the algorithm's execution. For this case, the only known upper bound is of $\mathcal{O}( n \log n )$, following from a majorization argument due to Peres, Talwar, and Wieder, which analyzes a related graphical allocation process. In this paper, we provide an upper bound of $\mathcal{O} ( \sqrt n \log n )$ on the expected gap of the above process for cycles of length $n$. We introduce a new potential analysis technique, which enables us to bound the difference in load between $k$-hop neighbors on the cycle, for any $k \leq n / 2$. We complement this with a "gap covering" argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We provide analytical and experimental evidence that our upper bound on the gap is tight up to a logarithmic factor.
İnsansız Araçlarla Düzlemsel Olmayan Araçların Taranması
Venue: arXiv cs.AI 2020
Year: 2020

The importance of area coverage with unmanned vehicles, in other words, traveling an area with an unmanned vehicle such as a robot or a UAV completely or partially with minimum cost, is increasing with the increase in usage of such vehicles today. Area coverage with unmanned vehicles is used today in the exploration of an area with UAVs, sweeping mines with robots, cleaning ground with robots in large shopping malls, mowing lawn in a large area etc. The problem has versions such as area coverage with a single unmanned vehicle, area coverage with multiple unmanned vehicles, on-line area coverage (The map of the area that will be covered is not known before starting the coverage) with unmanned vehicles etc. In addition, the area may have obstacles that the vehicles cannot move over. Naturally, many researches are working on the problem and a lot of researches have been done on the problem until today. Spanning tree coverage is one of the major approaches to the problem. In this approach, at the basic level, the planar area is divided into identical squares according to the range of sight of the vehicle, and centers of these squares are assumed to be vertexes of a graph. The vertexes of this graph are connected with the edges with unit costs and after finding the minimum spanning tree of the graph, the vehicle strolls around the spanning tree. The method we propose suggests a way to cover a non-planar area with unmanned vehicles. The method we propose also takes advantage of the spanning-tree coverage approach, but instead of assigning unit costs to the edges, we assigned a weight to each edge using slopes between vertexes those the edges connect. We have gotten noticeably better results than the results we got when we did not consider the slope between two squares and used the classical spanning tree approach.
Detection of Information Hiding at Anti-Copying 2D Barcodes
Venue: arXiv cs.CR 2020
Year: 2020

This paper concerns the problem of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes are either heuristicbased or Machine Learning (ML) based. The key limitation of prior heuristics-based schemes is that they do not answer the fundamental question of why the information hidden at a 2D barcode can be detected. The key limitation of prior MLbased information schemes is that they lack robustness because a printed 2D barcode is very much environmentally dependent, and thus an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes at the existing anti-copying 2D barcodes. The first scheme is to directly use the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred as to the Pixel Distance Based Detection (PDBD) scheme. The second scheme is first to calculate the variance of the raw signal and the covariance between the recovered signal and the raw signal, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred as to the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced IC attacks to evaluate the security of two existing anti-copying 2D barcodes. We implemented our schemes and conducted extensive performance comparison between our schemes and prior schemes under different capturing devices, such as a scanner and a camera phone. Our experimental results show that the PVBD scheme can correctly detect the existence of the hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the probability of successfully attacking of our IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.
Crime Prediction Using Spatio-Temporal Data
Venue: arXiv cs.LG 2020
Year: 2020

A crime is a punishable offence that is harmful for an individual and his society. It is obvious to comprehend the patterns of criminal activity to prevent them. Research can help society to prevent and solve crime activates. Study shows that only 10 percent offenders commits 50 percent of the total offences. The enforcement team can respond faster if they have early information and pre-knowledge about crime activities of the different points of a city. In this paper, supervised learning technique is used to predict crimes with better accuracy. The proposed system predicts crimes by analyzing data-set that contains records of previously committed crimes and their patterns. The system stands on two main algorithms - i) decision tree, and ii) k-nearest neighbor. Random Forest algorithm and Adaboost are used to increase the accuracy of the prediction. Finally, oversampling is used for better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
Storing and retrieving wavefronts with resistive temporal memory
Venue: arXiv cs.ET 2020
Year: 2020

We extend the reach of temporal computing schemes by developing a memory for multi-channel temporal patterns or "wavefronts." This temporal memory re-purposes conventional one-transistor-one-resistor (1T1R) memristor crossbars for use in an arrival-time coded, single-event-per-wire temporal computing environment. The memristor resistances and the associated circuit capacitances provide the necessary time constants, enabling the memory array to store and retrieve wavefronts. The retrieval operation of such a memory is naturally in the temporal domain and the resulting wavefronts can be used to trigger time-domain computations. While recording the wavefronts can be done using standard digital techniques, that approach has substantial translation costs between temporal and digital domains. To avoid these costs, we propose a spike timing dependent plasticity (STDP) inspired wavefront recording scheme to capture incoming wavefronts. We simulate these designs with experimentally validated memristor models and analyze the effects of memristor non-idealities on the operation of such a memory.
Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
Venue: arXiv cs.RO 2020
Year: 2020

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inexpensive compared to alternative state-of-the-art approaches such as simultaneous localization and mapping, making it especially suited for resource-constrained aerial robots. Many commercially-available ultra-wideband radios, however, provide inaccurate, biased range measurements. In this article, we propose a bias correction framework compatible with both two-way ranging and time difference of arrival ultra-wideband localization. Our method comprises of two steps: (i) statistical outlier rejection and (ii) a learning-based bias correction. This approach is scalable and frugal enough to be deployed on-board a nano-quadcopter's microcontroller. Previous research mostly focused on two-way ranging bias correction and has not been implemented in closed-loop nor using resource-constrained robots. Experimental results show that, using our approach, the localization error is reduced by ~18.5% and 48% (for TWR and TDoA, respectively), and a quadcopter can accurately track trajectories with position information from UWB only.
Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation
Venue: arXiv cs.CV 2020
Year: 2020

Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade, growing efforts have been devoted to investigating the problem on instructional video analysis. However, the most existing datasets in this area have limitations in diversity and scale, which makes them far from many real-world applications where more diverse activities occur. To address this, we present a large-scale dataset named as "COIN" for COmprehensive INstructional video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated efficiently with a series of step labels and the corresponding temporal boundaries. In order to provide a benchmark for instructional video analysis, we evaluate plenty of approaches on the COIN dataset under five different settings. Furthermore, we exploit two important characteristics (i.e., task-consistency and ordering-dependency) for localizing important steps in instructional videos. Accordingly, we propose two simple yet effective methods, which can be easily plugged into conventional proposal-based action detection models. We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community. Our dataset, annotation toolbox and source code are available at http://coin-dataset.github.io.
Efficient Oblivious Database Joins
Venue: arXiv cs.DB 2020
Year: 2020

A major algorithmic challenge in designing applications intended for secure remote execution is ensuring that they are oblivious to their inputs, in the sense that their memory access patterns do not leak sensitive information to the server. This problem is particularly relevant to cloud databases that wish to allow queries over the client's encrypted data. One of the major obstacles to such a goal is the join operator, which is non-trivial to implement obliviously without resorting to generic but inefficient solutions like Oblivious RAM (ORAM). We present an oblivious algorithm for equi-joins which (up to a logarithmic factor) matches the optimal $O(n\log n)$ complexity of the standard non-secure sort-merge join (on inputs producing $O(n)$ outputs). We do not use use expensive primitives like ORAM or rely on unrealistic hardware or security assumptions. Our approach, which is based on sorting networks and novel provably-oblivious constructions, is conceptually simple, easily verifiable, and very efficient in practice. Its data-independent algorithmic structure makes it secure in various different settings for remote computation, even in those that are known to be vulnerable to certain side-channel attacks (such as Intel SGX) or with strict requirements for low circuit complexity (like secure multiparty computation). We confirm that our approach is easily realizable through a compact implementation which matches our expectations for performance and is shown, both formally and empirically, to possess the desired security characteristics.
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
Venue: arXiv cs.LG 2020
Year: 2020

This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints. Despite its success in many domains, reinforcement learning is challenging to apply to problems with hard constraints, especially if both the state variables and actions are constrained. Previous works seeking to ensure constraint satisfaction, or safety, have focused on adding a projection step to a learned policy. Yet, this approach requires solving an optimization problem at every policy execution step, which can lead to significant computational costs. To tackle this problem, this paper proposes a new approach, termed Vertex Networks (VNs), with guarantees on safety during exploration and on learned control policies by incorporating the safety constraints into the policy network architecture. Leveraging the geometric property that all points within a convex set can be represented as the convex combination of its vertices, the proposed algorithm first learns the convex combination weights and then uses these weights along with the pre-calculated vertices to output an action. The output action is guaranteed to be safe by construction. Numerical examples illustrate that the proposed VN algorithm outperforms vanilla reinforcement learning in a variety of benchmark control tasks.