OpenRaiser/Intern-Atlas huggingface dataset:
Baba is Y'all: Collaborative Mixed-Initiative Level Design
Venue: arXiv cs.HC 2020
Year: 2020

We present a collaborative mixed-initiative system for building levels for the puzzle game "Baba is You". Unlike previous mixed-initiative systems, Baba is Y'all is designed for collaborative asynchronous creation by multiple users over the internet. The system includes several AI-assisted features to help designers, including a level evolver and an automated player for playtesting. The level archives catalogues levels according to which mechanics are implemented and not implemented, allowing the system to ask users to design levels with specific combinations of mechanics. We describe the operation of the system and the results of small-scale informal user test, and discuss future development paths for this system as well as for collaborative mixed-initiative systems in general.
Generative Latent Implicit Conditional Optimization when Learning from Small Sample
Venue: arXiv cs.LG 2020
Year: 2020

We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space. Unlike most recent works, which rely on access to large amounts of unlabeled data, GLICO does not require access to any additional data other than the small set of labeled points. In fact, GLICO learns to synthesize completely new samples for every class using as little as 5 or 10 examples per class, with as few as 10 such classes without imposing any prior. GLICO is then used to augment the small training set while training a classifier on the small sample. To this end, our proposed method samples the learned latent space using spherical interpolation, and generates new examples using the trained generator. Empirical results show that the new sampled set is diverse enough, leading to improvement in image classification in comparison with the state of the art, when trained on small samples obtained from CIFAR-10, CIFAR-100, and CUB-200.
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Venue: arXiv cs.CV 2020
Year: 2020

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
Venue: arXiv cs.LG 2020
Year: 2020

This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.
UniformAugment: A Search-free Probabilistic Data Augmentation Approach
Venue: arXiv cs.CV 2020
Year: 2020

Augmenting training datasets has been shown to improve the learning effectiveness for several computer vision tasks. A good augmentation produces an augmented dataset that adds variability while retaining the statistical properties of the original dataset. Some techniques, such as AutoAugment and Fast AutoAugment, have introduced a search phase to find a set of suitable augmentation policies for a given model and dataset. This comes at the cost of great computational overhead, adding up to several thousand GPU hours. More recently RandAugment was proposed to substantially speedup the search phase by approximating the search space by a couple of hyperparameters, but still incurring non-negligible cost for tuning those. In this paper we show that, under the assumption that the augmentation space is approximately distribution invariant, a uniform sampling over the continuous space of augmentation transformations is sufficient to train highly effective models. Based on that result we propose UniformAugment, an automated data augmentation approach that completely avoids a search phase. In addition to discussing the theoretical underpinning supporting our approach, we also use the standard datasets, as well as established models for image classification, to show that UniformAugment's effectiveness is comparable to the aforementioned methods, while still being highly efficient by virtue of not requiring any search.
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
Venue: arXiv cs.CL 2020
Year: 2020

We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university students translated French texts, producing either written or oral translations to Bambara. Our results suggest that similar quality can be obtained from either written or spoken translations for certain kinds of texts. They also suggest specific instructions that human translators should be given in order to improve the quality of their work.
Modeling the Realization and Execution of Functions and Functional Requirements
Venue: arXiv cs.SE 2020
Year: 2020

Requirements engineering plays a critical role in developing software systems. One of the most difficult tasks in this process is identifying functional requirements. A critical problem in many projects is missing requirements until late in the development cycle. In this paper, our core interest is function modeling, which refers to building models of systems based on their functionalities and on the functionalities of their subcomponents. We present a framework as the basis for specifying functional requirements via a modeling language that produces a high-level diagrammatic representation. The aim is to deliver an overall system description, facilitate communication and understanding, construct a holistic view of the system above the domains of different expertise, and lay the foundation for the design phase. We analyze the notion of function and its elementary types and apply examples of natural language description and scenarios. The results reveal a new method that lays a foundation for works on functionality and viable methodology for capturing its requirements.
"Trust me, I have a Ph.D.": A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities
Venue: arXiv cs.SI 2020
Year: 2020

Online communities adopt various reputation schemes to measure content quality. This study analyzes the effect of a new reputation scheme that exposes one's offline social status, such as an education degree, within an online community. We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the "transferred" social status affects the interactions among the users. We computed propensity scores to test whether flairs give ad-hoc authority to the adopters while minimizing the effects of confounding variables such as topics of content. The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles. In another community with a focus on casual science topics, exposing mere academic degrees did not obtain such benefits. Still, the users with the highest degree (e.g., Ph.D. or M.D.) were likely to receive more feedback from the audience. These findings suggest that reputation schemes that link the offline and online worlds could induce halo effects on feedback behaviors differently depending upon the community culture. We discuss the implications of this research for the design of future reputation mechanisms.
Design and Simulation of Memristor-Based Artificial Neural Network for Bidirectional Adaptive Neural Interface
Venue: arXiv cs.ET 2020
Year: 2020

This article proposes a general approach to the simulation and design of a multilayer perceptron (MLP) network on the basis of cross-bar arrays of metal-oxide memristive devices. The proposed approach uses the ANNM theory, tolerance theory, simulation methodology and experiment design. The tolerances analysis and synthesis process is performed for the ANNM hardware implementation on the basis of two arrays of memristive microdevices in the original 16x16 cross-bar topology being a component of bidirectional adaptive neural interface for automatic registration and stimulation of bioelectrical activity of a living neuronal culture used in robotics control system. The ANNM is trained for solving a nonlinear classification problem of stable information characteristics registered in the culture grown on a multi-electrode array. Memristive devices are fabricated on the basis of a newly engineered Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti multilayer structure, which contains self-organized interface oxide layers, nanocrystals and is specially developed to obtain robust resistive switching with low variation of parameters. An array of memristive devices is mounted into a standard metal-ceramic package and can be easily integrated into the neurointerface circuit. Memristive devices demonstrate bipolar switching of anionic type between the high-resistance state and low-resistance state and can be programmed to set the intermediate resistive states with a desired accuracy. The ANNM tuning, testing and control are implemented by the FPGA-based control subsystem. All developed models and algorithms are implemented as Python-based software.
Technical Report: Developing a Working Data Hub
Venue: arXiv cs.DB 2020
Year: 2020

Data forms a key component of any enterprise. The need for high quality and easy access to data is further amplified by organizations wishing to leverage machine learning or artificial intelligence for their operations. To this end, many organizations are building resources for managing heterogenous data, providing end-users with an organization wide view of available data, and acting as a centralized repository for data owned/collected by an organization. Very broadly, we refer to these class of techniques as a "data hub." While there is no clear definition of what constitutes a data hub, some of the key characteristics include: data catalog; links to data sets or owners of data sets or centralized data repository; basic ability to serve / visualize data sets; access control policies that ensure secure data access and respects policies of data owners; and computing capabilities tied with data hub infrastructure. Of course, developing such a data hub entails numerous challenges. This document provides background in databases, data management and outlines best practices and recommendations for developing and deploying a working data hub.
Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound
Venue: arXiv cs.CV 2020
Year: 2020

3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the evaluated organs accurately. Typical deep learning methods usually regress the coordinates directly or involve heatmap-matching. However, these methods struggle to deal with volumes with large sizes and the highly-varying positions and orientations of fetuses. In this work, we exploit an object detection framework to detect landmarks in 3D fetal facial US volumes. By regressing multiple parameters of the landmark-centered bounding box (B-box) with a strict criteria, the proposed model is able to pinpoint the exact location of the targeted landmarks. Specifically, the model uses a 3D region proposal network (RPN) to generate 3D candidate regions, followed by several 3D classification branches to select the best candidate. It also adopts an IoU-balance loss to improve communications between branches that benefits the learning process. Furthermore, it leverages a distance-based graph prior to regularize the training and helps to reduce false positive predictions. The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks. Results showed the proposed method outperforms some of the state-of-the-art methods in efficacy and efficiency.
Exponentially Stable First Order Control on Matrix Lie Groups
Venue: arXiv cs.RO 2020
Year: 2020

We present a novel first order controller for systems evolving on matrix Lie groups, a major use case of which is Cartesian velocity control on robot manipulators. This controller achieves global exponential trajectory tracking on a number of commonly used Lie groups including the Special Orthogonal Group SO(n), the Special Euclidean Group SE(n), and the General Linear Group over complex numbers GL(n, C). Additionally, this controller achieves local exponential trajectory tracking on all matrix Lie groups. We demonstrate the effectiveness of this controller in simulation on a number of different Lie groups as well as on hardware with a 7-DOF Sawyer robot arm.
Adversarial Transfer Learning for Punctuation Restoration
Venue: arXiv cs.CL 2020
Year: 2020

Previous studies demonstrate that word embeddings and part-of-speech (POS) tags are helpful for punctuation restoration tasks. However, two drawbacks still exist. One is that word embeddings are pre-trained by unidirectional language modeling objectives. Thus the word embeddings only contain left-to-right context information. The other is that POS tags are provided by an external POS tagger. So computation cost will be increased and incorrect predicted tags may affect the performance of restoring punctuation marks during decoding. This paper proposes adversarial transfer learning to address these problems. A pre-trained bidirectional encoder representations from transformers (BERT) model is used to initialize a punctuation model. Thus the transferred model parameters carry both left-to-right and right-to-left representations. Furthermore, adversarial multi-task learning is introduced to learn task invariant knowledge for punctuation prediction. We use an extra POS tagging task to help the training of the punctuation predicting task. Adversarial training is utilized to prevent the shared parameters from containing task specific information. We only use the punctuation predicting task to restore marks during decoding stage. Therefore, it will not need extra computation and not introduce incorrect tags from the POS tagger. Experiments are conducted on IWSLT2011 datasets. The results demonstrate that the punctuation predicting models obtain further performance improvement with task invariant knowledge from the POS tagging task. Our best model outperforms the previous state-of-the-art model trained only with lexical features by up to 9.2% absolute overall F_1-score on test set.
Leveraging Data Preparation, HBase NoSQL Storage, and HiveQL Querying for COVID-19 Big Data Analytics Projects
Venue: arXiv cs.DB 2020
Year: 2020

Epidemiologist, Scientists, Statisticians, Historians, Data engineers and Data scientists are working on finding descriptive models and theories to explain COVID-19 expansion phenomena or on building analytics predictive models for learning the apex of COVID-19 confimed cases, recovered cases, and deaths evolution curves. In CRISP-DM life cycle, 75% of time is consumed only by data preparation phase causing lot of pressions and stress on scientists and data scientists building machine learning models. This paper aims to help reducing data preparation efforts by presenting detailed schemas design and data preparation technical scripts for formatting and storing Johns Hopkins University COVID-19 daily data in HBase NoSQL data store, and enabling HiveQL COVID-19 data querying in a relational Hive SQL-like style.
A Case Study on Tool Support for Collaboration in Agile Development
Venue: arXiv cs.SE 2020
Year: 2020

We report on a longitudinal case study conducted at the Italian site of a large software company to further our understanding of how development and communication tools can be improved to better support agile practices and collaboration. After observing inconsistencies in the way communication tools (i.e., email, Skype, and Slack) were used, we first reinforced the use of Slack as the central hub for internal communication, while setting clear rules regarding tools usage. As a second main change, we refactored the Jira Scrum board into two separate boards, a detailed one for developers and a high-level one for managers, while also introducing automation rules and the integration with Slack. The first change revealed that the teams of developers used and appreciated Slack differently with the QA team being the most favorable and that the use of channels is hindered by automatic notifications from development tools (e.g., Jenkins). The findings from the second change show that 85\% of the interviewees reported perceived improvements in their workflow. Despite the limitations due to the single nature of the reported case, we highlight the importance for companies to reflect on how to properly set up their agile work environment to improve communication and facilitate collaboration.
Research of Caller ID Spoofing Launch, Detection, and Defense
Venue: arXiv cs.CR 2020
Year: 2020

Caller ID parodying produces the valid Caller character, in this manner deciding seem to start from another client. This apparently basic assault strategy has been utilized in the developing communication fake and trick calls, bringing about significant financial trouble. Unfortunately, callerID spoofing is easy to implement but yet it is difficult to have protection against it. In addition, there are not effective and defense solutions available right now. In this research it is suggested the CIVE (Callee Inference & VErification), a compelling and viable guard against Caller ID spoofing. This way it is described how it's possible to lunch call spoofing and between line describe how CIVE approach method can help to prevent somehow this kind of attacks. Caller ID Spoofing could cause huge financial and political issues special nowadays, when many things even sometimes authentication and verification are available by phone call, like banks approving transactions or two factor authentications and many other things. We believe critical industries specially banks and payment service providers should be protected against such vulnerabilities with their system and make an approach to prevent it, also it is very important to learn people specially who has special social place like politicians or celebrities to know such kind of attack are already exist. For this paper we implemented a call from white house to show there is no limitation and no matter whom you try to spoof, but destination which is the victim receive the call and that make this attack vector dangerous. And even modern communication and even devices like 4G and smart phones are not able to prevent or even detect this kind of attack. This study is a demonstration of the vulnerabilities available. All experiments were conducted on isolated mock-ups.
Editable Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

These days deep neural networks are ubiquitously used in a wide range of tasks, from image classification and machine translation to face identification and self-driving cars. In many applications, a single model error can lead to devastating financial, reputational and even life-threatening consequences. Therefore, it is crucially important to correct model mistakes quickly as they appear. In this work, we investigate the problem of neural network editing $-$ how one can efficiently patch a mistake of the model on a particular sample, without influencing the model behavior on other samples. Namely, we propose Editable Training, a model-agnostic training technique that encourages fast editing of the trained model. We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
DNN-based Localization from Channel Estimates: Feature Design and Experimental Results
Venue: arXiv cs.NI 2020
Year: 2020

We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems. We discuss the practical impairments that are likely to be present in practical CSI estimates, and introduce a principled approach to feature design for CSI-based DNN applications based on the objective of making the features invariant to the considered impairments. We demonstrate the efficiency of this approach by applying it to a dataset constituted of geo-tagged CSI measured in an outdoors campus environment, and training a DNN to estimate the position of the UE on the basis of the CSI. We provide an experimental evaluation of several aspects of that learning approach, including localization accuracy, generalization capability, and data aging.