OpenRaiser/Intern-Atlas huggingface dataset:
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Venue: arXiv cs.CV 2020
Year: 2020

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M3VSNet establishes the state-of-the-arts unsupervised method and achieves comparable performance with previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks and Temples benchmark with effective improvement. Our code is available at https://github.com/whubaichuan/M3VSNet.
Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting
Venue: arXiv cs.SI 2020
Year: 2020

The reports of Russian interference in the 2016 United States elections brought into the center of public attention concerns related to the ability of foreign actors to increase social discord and take advantage of personal user data for political purposes. It has raised questions regarding the ways and the extent to which data can be used to create psychographical profiles to determine what kind of advertisement would be most effective to persuade a particular person in a particular location for some political event. In this work, we study the political ads dataset collected by ProPublica, an American nonprofit newsroom, using a network of volunteers in the period before the 2018 US midterm elections. We first describe the main characteristics of the data and explore the user attributes including age, region, activity, and more, with a series of interactive illustrations. Furthermore, an important first step towards understating of political manipulation via user targeting is to identify politically related ads, yet manually checking ads is not feasible due to the scale of social media advertising. Consequently, we address the challenge of automatically classifying between political and non-political ads, demonstrating a significant improvement compared to the current text-based classifier used by ProPublica, and study whether the user targeting attributes are beneficial for this task. Our evaluation sheds light on questions, such as how user attributes are being used for political ads targeting and which users are more prone to be targeted with political ads. Overall, our contribution of data exploration, political ad classification and initial analysis of the targeting attributes, is designed to support future work with the ProPublica dataset, and specifically with regard to the understanding of political manipulation via user targeting.
Energy Efficiency Analysis of Intelligent Reflecting Surface System with Hardware Impairments
Venue: arXiv cs.IT 2020
Year: 2020

Recently, as explosive growth of mobile data traffic, the performance of wireless communication systems requires to be enhanced significantly in future. Intelligent reflecting surface (IRS) can be used as a promising way to improve the energy efficiency of wireless communications with less complexity and hardware cost. Most existing studies consider the cases with ideal hardware, however, both physical transceiver and IRS suffer from non-negligible hardware impairments which may greatly degrade the system performance. In this paper, by taking hardware impairments into consideration, we focus on the energy efficiency analysis of IRS system. Our first contribution is to derive the optimal receive combining and transmit beamforming vectors. After that, we characterize the asymptotic channel capacity. With the derived asymptotic channel capacity and the power consumption model, the analytical upper and lower bounds of energy efficiency are provided. Our results show that an IRS system can achieve both high spectral efficiency and high energy efficiency with moderate number of antennas. This observation is encouraging for that there is no need to cost a lot on expensive high-quality antennas, which corresponds to the requirements of new communication paradigms.
Commutative automata networks
Venue: arXiv cs.CC 2020
Year: 2020

Automata networks are mappings of the form f : Q Z $\rightarrow$ Q Z , where Q is a finite alphabet and Z is a set of entities; they generalise Cellular Automata and Boolean networks. An update schedule dictates when each entity updates its state according to its local function fi : Q Z $\rightarrow$ Q. One major question is to study the behaviour of a given automata networks under different update schedules. In this paper, we study automata networks that are invariant under many different update schedules. This gives rise to two definitions, locally commutative and globally commu-tative networks. We investigate the relation between commutativity and different forms of locality of update functions; one main conclusion is that globally commutative networks have strong dynamical properties, while locally commutative networks are much less constrained. We also give a complete classification of all globally commutative Boolean networks.
Fast and Robust Registration of Aerial Images and LiDAR data Based on Structrual Features and 3D Phase Correlation
Venue: arXiv cs.CV 2020
Year: 2020

Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods
A Quantitative Exploration of the 9-Factor Theory: Distribution of Leadership Roles between Scrum Master and Agile Team
Venue: arXiv cs.SE 2020
Year: 2020

A number of qualitative studies find that team leadership is one essential success factor for evolving into a mature agile team. One such qualitative study suggests the 9-Factor Theory of Scrum Master roles, which claims that the Scrum Master performs a set of 9 leadership roles which are transferred to the team over time (Spiegler et al., 2019). We aimed at conducting a quantitative exploration that examines the presence and change of the 9-Factory Theory in relation to team maturity. We conducted an online survey with 67 individuals at the conglomerate Robert Bosch GmbH. Descriptive statistics reveal that the Scrum Master and the agile team score differently on the 9 factors and that the Scrum Master role is most often distributed in teams that had been working between 3 and 5 months in an agile manner. Yet, we also find that the leadership roles predominantly remain with one dedicated Scrum Master. Based on our results we suggest to group the 9-Factor Theory into three clusters: the Scrum Master is rather linked to psychological team factors (1), while the team tends to be linked to rather product-related factors (2). Organizational factors (3) are less often present. Our practical implications suggest an extension of the Scrum Master description. Furthermore, our study lays groundwork for future quantitative testing of leadership in agile teams.
Egel -- Graph Rewriting with a Twist
Venue: arXiv cs.PL 2020
Year: 2020

Egel is an untyped eager combinator toy language. Its primary purpose is to showcase an abstract graph-rewriting semantics allowing a robust memory-safe construction in C++. Though graph rewriters are normally implemented by elaborate machines, this can mostly be avoided with a change in the representation of term graphs. With an informal inductive argument, that representation is shown to always form directed acyclic graphs. Moreover, this graph semantics can trivially be extended to allow exception handling and cheap concurrency. Egel, the interpreter, exploits this semantics with a straight-forward mapping from combinators to reference-counted C++ objects.
Spectral and Energy Efficiency of IRS-Assisted MISO Communication with Hardware Impairments
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we analyze the spectral and energy efficiency of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) downlink system with hardware impairments. An extended error vector magnitude (EEVM) model is utilized to characterize the impact of radio-frequency (RF) impairments at the access point (AP) and phase noise is considered for the imperfect IRS. We show that the spectral efficiency is limited due to the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large, which is in contrast with the conventional case with ideal hardware. Moreover, the performance degradation at high SNR is shown to be mainly affected by the AP hardware impairments rather than the phase noise of IRS. We further obtain the optimal transmit power in closed form for energy efficiency maximization. Simulation results are provided to verify these results.
ICT Intervention in the Containment of the Pandemic Spread of COVID-19: An Exploratory Study
Venue: arXiv cs.CY 2020
Year: 2020

The objective of this article is to explore the Information and Communication Technology (ICT) interventions and its strengths, weaknesses, opportunities and threats for the containment of the pandemic spread of novel Coronavirus. The research adopted a qualitative research approach, while the study data were collected through online content review and Focus Group Discussion (FGD). Starting with a preliminary set of about 1200 electronic resources or contents, 56 were selected for review study, applying an inclusion and exclusion criteria. The review study revealed ICT interventions that include websites and dashboards, mobile applications, robotics and drones, artificial intelligence (AI), data analytic, wearable and sensor technology, social media and learning tools, and interactive voice response (IVR) as well as explored their respective usages to combat the pandemic spread of COVID-19. Later, the FGD was replicated with 22 participants and explored the possible strengths, weaknesses, opportunities, and threats (SWOT) of deploying such technologies to fight against the COVID-19 pandemic. This research not only explores the exiting status of ICT interventions to fight with the COVID-19 pandemic but also provides a number of implications for the government, practitioners, doctors, policymakers and researchers for the effective utilization of the existing ICT interventions and for the future potential research and technological development to the containment of the pandemic spread of COVID-19 and future pandemics.
Toward Terabits-per-second Communications: A High-Throughput Hardware Implementation of $G_N$-Coset Codes
Venue: arXiv cs.IT 2020
Year: 2020

Recently, a parallel decoding algorithm of $G_N$-coset codes was proposed.The algorithm exploits two equivalent decoding graphs.For each graph, the inner code part, which consists of independent component codes, is decoded in parallel. The extrinsic information of the code bits is obtained and iteratively exchanged between the graphs until convergence. This algorithm enjoys a higher decoding parallelism than the previous successive cancellation algorithms, due to the avoidance of serial outer code processing. In this work, we present a hardware implementation of the parallel decoding algorithm, it can support maximum $N=16384$. We complete the decoder's physical layout in TSMC $16nm$ process and the size is $999.936μm\times 999.936μm, \,\approx 1.00mm^2$. The decoder's area efficiency and power consumption are evaluated for the cases of $N=16384,K=13225$ and $N=16384, K=14161$. Scaled to $7nm$ process, the decoder's throughput is higher than $477Gbps/mm^2$ and $533Gbps/mm^2$ with five iterations.
NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories
Venue: arXiv cs.AR 2020
Year: 2020

Data copy is a widely-used memory operation in many programs and operating system services. In conventional computers, data copy is often carried out by two separate read and write transactions that pass data back and forth between the DRAM chip and the processor chip. Some prior mechanisms propose to avoid this unnecessary data movement by using the shared internal bus in the DRAM chip to directly copy data within the DRAM chip (e.g., between two DRAM banks). While these methods exhibit superior performance compared to conventional techniques, data copy across different DRAM banks is still greatly slower than data copy within the same DRAM bank. Hence, these techniques have limited benefit for the emerging 3D-stacked memories (e.g., HMC and HBM) that contain hundreds of DRAM banks across multiple memory controllers. In this paper, we present Network-on-Memory (NoM), a lightweight inter-bank data communication scheme that enables direct data copy across both memory banks of a 3D-stacked memory. NoM adopts a TDM-based circuit-switching design, where circuit setup is done by the memory controller. Compared to state-of-the-art approaches, NoM enables both fast data copy between multiple DRAM banks and concurrent data transfer operations. Our evaluation shows that NoM improves the performance of data-intensive workloads by 3.8X and 75%, on average, compared to the baseline conventional 3D-stacked DRAM architecture and state-of-the-art techniques, respectively.
Use of Wikipedia categories on information retrieval research: a brief review
Venue: arXiv cs.DL 2020
Year: 2020

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.
The rise of science in low-carbon energy technologies
Venue: arXiv cs.DL 2020
Year: 2020

Successfully combating climate change will require substantial technological improvements in Low-Carbon Energy Technologies (LCETs), but designing efficient allocation of R\&D budgets requires a better understanding of how LCETs rely on scientific knowledge. Using data covering almost all US patents and scientific articles that are cited by them over the past two centuries, we describe the evolution of knowledge bases of ten key LCETs and show how technological interdependencies have changed over time. The composition of low-carbon energy innovations shifted over time, from Hydro and Wind energy in the 19th and early 20th century, to Nuclear fission after World War II, and more recently to Solar PV and back to Wind. In recent years, Solar PV, Nuclear fusion and Biofuels (including energy from waste) have 35-65\% of their citations directed toward scientific papers, while this ratio is less than 10\% for Wind, Solar thermal, Hydro, Geothermal, and Nuclear fission. Over time, the share of patents citing science and the share of citations that are to scientific papers has been increasing for all technology types. The analysis of the scientific knowledge base of each LCET reveals three fairly separate clusters, with nuclear energy technologies, Biofuels and Waste, and all the other LCETs. Our detailed description of knowledge requirements for each LCET helps to design of targeted innovation policies.
Group Key Agreement in Information Centric Networks with Tree Group Diffie-Hellman
Venue: arXiv cs.CR 2020
Year: 2020

The client-server model is known to scale badly without redundant servers and caches. Information-Centric Networks (ICN) are an alternative designed to lessen that problem by distributing load to intermediary routers, lowering the bar to techniques only accessible to server farms. However, this seamlessness comes with its own set of problems. Communication security, by using a key per connection, prevents intermediaries from reading and thus caching content. We thus need to share a common key for larger groups of correspondents to be able to benefit from the advantages of ICN. This paper presents the algorithmic choices to establish such a group key, the adaptations made for ICN, its implementation, and the statistics backing its performances comparison with our baseline. The first results tend to show a performance improvement over the selected base algorithm.
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generated synthetic data, that come with "free" annotation, to make effective predictions on real data. However, existing DA methods are not designed to cope with the multi-modal nature of RGB-D data, which are widely used in robotic vision. We propose a novel RGB-D DA method that reduces the synthetic-to-real domain shift by exploiting the inter-modal relation between the RGB and depth image. Our method consists of training a convolutional neural network to solve, in addition to the main recognition task, the pretext task of predicting the relative rotation between the RGB and depth image. To evaluate our method and encourage further research in this area, we define two benchmark datasets for object categorization and instance recognition. With extensive experiments, we show the benefits of leveraging the inter-modal relations for RGB-D DA.
Exploring EOSIO via Graph Characterization
Venue: arXiv cs.SI 2020
Year: 2020

Designed for commercial decentralized applications (DApps), EOSIO is a Delegated Proof-of-Stake (DPoS) based blockchain system. It has overcome some shortages of the traditional blockchain systems like Bitcoin and Ethereum with its outstanding features (e.g., free for usage, high throughput and eco-friendly), and thus becomes one of the mainstream blockchain systems. Though there exist billions of transactions in EOSIO, the ecosystem of EOSIO is still relatively unexplored. To fill this gap, we conduct a systematic graph analysis on the early EOSIO by investigating its four major activities, namely account creation, account vote, money transfer and contract authorization. We obtain some novel observations via graph metric analysis, and our results reveal some abnormal phenomenons like voting gangs and sham transactions.
PowerModelsDistribution.jl: An Open-Source Framework for Exploring Distribution Power Flow Formulations
Venue: arXiv cs.CE 2020
Year: 2020

In this work we introduce PowerModelsDistribution, a free, open-source toolkit for distribution power network optimization, whose primary focus is establishing a baseline implementation of steady-state multi-conductor unbalanced distribution network optimization problems, which includes implementations of Power Flow and Optimal Power Flow problem types. Currently implemented power flow formulations for these problem types include AC (polar and rectangular), a second-order conic relaxation of the Branch Flow Model (BFM) and Bus Injection Model (BIM), a semi-definite relaxation of BFM, and several linear approximations, such as the simplified unbalanced BFM. The results of AC power flow have been validated against OpenDSS, an open-source "electric power distribution system simulator", using IEEE distribution test feeders (13, 34, 123 bus and LVTestCase), all parsed using a built-in OpenDSS parser. This includes support for standard distribution system components as well as novel resource models such as generic energy storage (multi-period) and photovoltaic systems, with the intention to add support for additional components in the future.
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Venue: arXiv cs.CL 2020
Year: 2020

In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.
pomdp_py: A Framework to Build and Solve POMDP Problems
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we present pomdp_py, a general purpose Partially Observable Markov Decision Process (POMDP) library written in Python and Cython. Existing POMDP libraries often hinder accessibility and efficient prototyping due to the underlying programming language or interfaces, and require extra complexity in software toolchain to integrate with robotics systems. pomdp_py features simple and comprehensive interfaces capable of describing large discrete or continuous (PO)MDP problems. Here, we summarize the design principles and describe in detail the programming model and interfaces in pomdp_py. We also describe intuitive integration of this library with ROS (Robot Operating System), which enabled our torso-actuated robot to perform object search in 3D. Finally, we note directions to improve and extend this library for POMDP planning and beyond.