OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Limitation, Security and Privacy Issues on Additive Manufacturing
Venue: arXiv cs.CR 2021
Year: 2021

Additive manufacturing (AM) is growing as fast as anyone can imagine, and it is now a multi-billion-dollar industry. AM becomes popular in a variety of sectors, such as automotive, aerospace, biomedical, and pharmaceutical, for producing parts/ components/ subsystems. However, current AM technologies can face vast risks of security issues and privacy loss. For the security of AM process, many researchers are working on the defense mechanism to countermeasure such security concerns and finding efficient ways to eliminate those risks. Researchers have also been conducting experiments to establish a secure framework for the user's privacy and security components. This survey consists of four sections. In the first section, we will explore the relevant limitations of additive manufacturing in terms of printing capability, security, and possible solutions. The second section will present different kinds of attacks on AM and their effects. The next part will analyze and discuss the mechanisms and frameworks for access control and authentication for AM devices. The final section examines the security issues in various industrial sectors and provides the observations on the security of the additive manufacturing process.
Self-supervised Text-to-SQL Learning with Header Alignment Training
Venue: arXiv cs.CL 2021
Year: 2021

Since we can leverage a large amount of unlabeled data without any human supervision to train a model and transfer the knowledge to target tasks, self-supervised learning is a de-facto component for the recent success of deep learning in various fields. However, in many cases, there is a discrepancy between a self-supervised learning objective and a task-specific objective. In order to tackle such discrepancy in Text-to-SQL task, we propose a novel self-supervised learning framework. We utilize the task-specific properties of Text-to-SQL task and the underlying structures of table contents to train the models to learn useful knowledge of the \textit{header-column} alignment task from unlabeled table data. We are able to transfer the knowledge to the supervised Text-to-SQL training with annotated samples, so that the model can leverage the knowledge to better perform the \textit{header-span} alignment task to predict SQL statements. Experimental results show that our self-supervised learning framework significantly improves the performance of the existing strong BERT based models without using large external corpora. In particular, our method is effective for training the model with scarce labeled data. The source code of this work is available in GitHub.
A Survey of Video Game Testing
Venue: arXiv cs.SE 2021
Year: 2021

Video-game projects are notorious for having day-one bugs, no matter how big their budget or team size. The quality of a game is essential for its success. This quality could be assessed and ensured through testing. However, to the best of our knowledge, little is known about video-game testing. In this paper, we want to understand how game developers perform game testing. We investigate, through a survey, the academic and gray literature to identify and report on existing testing processes and how they could automate them. We found that game developers rely, almost exclusively, upon manual play-testing and the testers' intrinsic knowledge. We conclude that current testing processes fall short because of their lack of automation, which seems to be the natural next step to improve the quality of games while maintaining costs. However, the current game-testing techniques may not generalize to different types of games.
The Curse of Correlations for Robust Fingerprinting of Relational Databases
Venue: arXiv cs.CR 2021
Year: 2021

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8\% fingerprint bits by just modifying 14.2\% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort $3\%$ fingerprint bits).
Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information
Venue: arXiv cs.RO 2021
Year: 2021

This paper proposes a hierarchical Bayesian model based on spatial concepts that enables a robot to transfer the knowledge of places from experienced environments to a new environment. The transfer of knowledge based on spatial concepts is modeled as the calculation process of the posterior distribution based on the observations obtained in each environment with the parameters of spatial concepts generalized to environments as prior knowledge. We conducted experiments to evaluate the generalization performance of spatial knowledge for general places such as kitchens and the adaptive performance of spatial knowledge for unique places such as `Emma's room' in a new environment. In the experiments, the accuracies of the proposed method and conventional methods were compared in the prediction task of location names from an image and a position, and the prediction task of positions from a location name. The experimental results demonstrated that the proposed method has a higher prediction accuracy of location names and positions than the conventional method owing to the transfer of knowledge.
Preventing Extreme Polarization of Political Attitudes
Venue: arXiv cs.MA 2021
Year: 2021

Extreme polarization can undermine democracy by making compromise impossible and transforming politics into a zero-sum game. Ideological polarization - the extent to which political views are widely dispersed - is already strong among elites, but less so among the general public (McCarty, 2019, p. 50-68). Strong mutual distrust and hostility between Democrats and Republicans in the U.S., combined with the elites' already strong ideological polarization, could lead to increasing ideological polarization among the public. The paper addresses two questions: (1) Is there a level of ideological polarization above which polarization feeds upon itself to become a runaway process? (2) If so, what policy interventions could prevent such dangerous positive feedback loops? To explore these questions, we present an agent-based model of ideological polarization that differentiates between the tendency for two actors to interact (exposure) and how they respond when interactions occur, positing that interaction between similar actors reduces their difference while interaction between dissimilar actors increases their difference. Our analysis explores the effects on polarization of different levels of tolerance to other views, responsiveness to other views, exposure to dissimilar actors, multiple ideological dimensions, economic self-interest, and external shocks. The results suggest strategies for preventing, or at least slowing, the development of extreme polarization.
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering
Venue: arXiv cs.CL 2021
Year: 2021

Question answering (QA) is an important use case on voice assistants. A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage. However, extractive answers are often unnatural in a conversational context which results in suboptimal user experience. In this work, we investigate conversational answer generation for QA. We propose AnswerBART, an end-to-end generative RC model which combines answer generation from multiple passages with passage ranking and answerability. Moreover, a hurdle in applying generative RC are hallucinations where the answer is factually inconsistent with the passage text. We leverage recent work from summarization to evaluate factuality. Experiments show that AnswerBART significantly improves over previous best published results on MS MARCO 2.1 NLGEN by 2.5 ROUGE-L and NarrativeQA by 9.4 ROUGE-L.
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Venue: arXiv cs.ET 2021
Year: 2021

Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital logic. In contrast to conventional representation schemes used within the binary domain, the sequence of bit streams in the stochastic domain is inconsequential, and computation is usually non-deterministic. In this brief, we exploit the stochasticity during switching of probabilistic Conductive Bridging RAM (CBRAM) devices to efficiently generate stochastic bit streams in order to perform Deep Learning (DL) parameter optimization, reducing the size of Multiply and Accumulate (MAC) units by 5 orders of magnitude. We demonstrate that in using a 40-nm Complementary Metal Oxide Semiconductor (CMOS) process our scalable architecture occupies 1.55mm$^2$ and consumes approximately 167$μ$W when optimizing parameters of a Convolutional Neural Network (CNN) while it is being trained for a character recognition task, observing no notable reduction in accuracy post-training.
Drone-as-a-Service Composition Under Uncertainty
Venue: arXiv cs.DC 2021
Year: 2021

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose an uncertainty-aware DaaS route-planning algorithm that selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. A spatiotemporal DaaS composer first selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. A predictive DaaS composer then utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction
Venue: arXiv cs.CL 2021
Year: 2021

Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities. %Considering the sentiment of the aspects(ACSA) and the overall review rating(RP) simultaneously has the potential to improve the overall performance. ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios. While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitation of both tasks. To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset \textbf{ASAP} including $46,730$ genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. Besides a $5$-star scale rating, each review is manually annotated according to its sentiment polarities towards $18$ pre-defined aspect categories. We hope the release of the dataset could shed some light on the fields of sentiment analysis. Moreover, we propose an intuitive yet effective joint model for ACSA and RP. Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.
A Generalization of the Concavity of Rényi Entropy Powe
Venue: arXiv cs.IT 2021
Year: 2021

Recently, Savaré-Toscani proved that the Rényi entropy power of general probability densities solving the $p$-nonlinear heat equation in $\mathbb{R}^n$ is always a concave function of time, which extends Costa's concavity inequality for Shannon's entropy power to Rényi entropies. In this paper, we give a generalization of Savaré-Toscani's result by giving a class of sufficient conditions of the parameters under which the concavity of the Rényi entropy power is still valid. These conditions are quite general and include the parameter range given by Savaré-Toscani as special cases. Also, the conditions are obtained with a systematical approach.
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings
Venue: arXiv cs.CL 2021
Year: 2021

In this work we look into adding a new language to a multilingual NMT system in an unsupervised fashion. Under the utilization of pre-trained cross-lingual word embeddings we seek to exploit a language independent multilingual sentence representation to easily generalize to a new language. While using cross-lingual embeddings for word lookup we decode from a yet entirely unseen source language in a process we call blind decoding. Blindly decoding from Portuguese using a basesystem containing several Romance languages we achieve scores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English. In an attempt to train the mapping from the encoder sentence representation to a new target language we use our model as an autoencoder. Merely training to translate from Portuguese to Portuguese while freezing the encoder we achieve 26 BLEU on English-Portuguese, and up to 28 BLEU when adding artificial noise to the input. Lastly we explore a more practical adaptation approach through non-iterative backtranslation, exploiting our model's ability to produce high quality translations through blind decoding. This yields us up to 34.6 BLEU on English-Portuguese, attaining near parity with a model adapted on real bilingual data.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
Venue: arXiv cs.LG 2021
Year: 2021

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at "what if" scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.
Deep Transfer Learning for Infectious Disease Case Detection Using Electronic Medical Records
Venue: arXiv cs.LG 2021
Year: 2021

During an infectious disease pandemic, it is critical to share electronic medical records or models (learned from these records) across regions. Applying one region's data/model to another region often have distribution shift issues that violate the assumptions of traditional machine learning techniques. Transfer learning can be a solution. To explore the potential of deep transfer learning algorithms, we applied two data-based algorithms (domain adversarial neural networks and maximum classifier discrepancy) and model-based transfer learning algorithms to infectious disease detection tasks. We further studied well-defined synthetic scenarios where the data distribution differences between two regions are known. Our experiments show that, in the context of infectious disease classification, transfer learning may be useful when (1) the source and target are similar and the target training data is insufficient and (2) the target training data does not have labels. Model-based transfer learning works well in the first situation, in which case the performance closely matched that of the data-based transfer learning models. Still, further investigation of the domain shift in real world research data to account for the drop in performance is needed.
Swarm Robots in Agriculture
Venue: arXiv cs.RO 2021
Year: 2021

Agricultural mechanization is an area of knowledge that has evolved a lot over the past century, its main actors being agricultural tractors that, in 100 years, have increased their powers by 3,300%. This evolution has resulted in an exponential increase in the field capacity of such machines. However, it has also generated negative results such as excessive consumption of fossil fuel, excessive weight on the soil, very high operating costs, and millionaire acquisition value. This paper aims to present an antiparadigmatic alternative in this area. It is proposing a swarm of small electric robotic tractors that together have the same field capacity as a large tractor with an internal combustion engine. A comparison of costs and field capacity between a 270 kW tractor and a swarm of ten swarm tractors of 24 kW each was carried out. The result demonstrated a wide advantage for the small robot team. It was also proposed the preliminary design of an electric swarm robot tractor. Finally, research challenges were suggested to operationalize such a proposal, calling on the Brazilian Robotics Research Community to elaborate a roadmap for research in the area of swarm robot for mechanized agricultural operations.
Residuation for Soft Constraints: Lexicographic Orders and Approximation Techniques
Venue: arXiv cs.LO 2021
Year: 2021

Residuation theory concerns the study of partially ordered algebraic structures, most often monoids, equipped with a weak inverse for the monoidal operator. One of its area of application has been constraint programming, whose key requirement is the presence of an aggregator operator for combining preferences. Given a residuated monoid of preferences, the paper first shows how to build a new residuated monoid of (possibly infinite) tuples, which is based on the lexicographic order. Second, it introduces a variant of an approximation technique (known as Mini-bucket) that exploits the presence of the weak inverse.
CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts
Venue: arXiv cs.SE 2021
Year: 2021

Requirements often specify the expected system behavior by using causal relations (e.g., If A, then B). Automatically extracting these relations supports, among others, two prominent RE use cases: automatic test case derivation and dependency detection between requirements. However, existing tools fail to extract causality from natural language with reasonable performance. In this paper, we present our tool CiRA (Causality detection in Requirements Artifacts), which represents a first step towards automatic causality extraction from requirements. We evaluate CiRA on a publicly available data set of 61 acceptance criteria (causal: 32; non-causal: 29) describing the functionality of the German Corona-Warn-App. We achieve a macro F_1 score of 83%, which corroborates the feasibility of our approach.
Bluejay: A Cross-Tooling Audit Framework For Agile Software Teams
Venue: arXiv cs.SE 2021
Year: 2021

Agile software teams are expected to follow a number of specific Team Practices (TPs) during each iteration, such as estimating the effort ("points") required to complete user stories and coordinating the management of the codebase with the delivery of features. For software engineering instructors trying to teach such TPs to student teams, manually auditing teams if teams are following the TPs and improving over time is tedious, time-consuming and error-prone. It is even more difficult when those TPs involve two or more tools. For example, starting work on a feature in a project-management tool such as Pivotal Tracker should usually be followed relatively quickly by the creation of a feature branch on GitHub. Merging a feature branch on GitHub should usually be followed relatively quickly by deploying the new feature to a staging server for customer feedback. Few systems are designed specifically to audit such TPs, and existing ones, as far as we know, are limited to a single specific tool. We present Bluejay, an open-source extensible platform that uses the APIs of multiple tools to collect raw data, synthesize it into TP measurements, and present dashboards to audit the TPs. A key insight in Bluejay's design is that TPs can be expressed in terminology similar to that used for modeling and auditing Service Level Agreement (SLA) compliance. Bluejay therefore builds on mature tools used in that ecosystem and adapts them for describing, auditing, and reporting on TPs. Bluejay currently consumes data from five different widely-used development tools, and can be customized by connecting it to any service with a REST API. Video showcase available at governify.io/showcase/bluejay