OpenRaiser/Intern-Atlas huggingface dataset:
Responsible AI and Its Stakeholders
Venue: arXiv cs.CY 2020
Year: 2020

Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems. It, however, fails to accommodate the possibility of holding AI responsible per se, which could close some legal and moral gaps concerning the deployment of autonomous and self-learning systems. We discuss three notions of responsibility (i.e., blameworthiness, accountability, and liability) for all stakeholders, including AI, and suggest the roles of jurisdiction and the general public in this matter.
On Skolem-hardness and saturation points in Markov decision processes
Venue: arXiv cs.LO 2020
Year: 2020

The Skolem problem and the related Positivity problem for linear recurrence sequences are outstanding number-theoretic problems whose decidability has been open for many decades. In this paper, the inherent mathematical difficulty of a series of optimization problems on Markov decision processes (MDPs) is shown by a reduction from the Positivity problem to the associated decision problems which establishes that the problems are also at least as hard as the Skolem problem as an immediate consequence. The optimization problems under consideration are two non-classical variants of the stochastic shortest path problem (SSPP) in terms of expected partial or conditional accumulated weights, the optimization of the conditional value-at-risk for accumulated weights, and two problems addressing the long-run satisfaction of path properties, namely the optimization of long-run probabilities of regular co-safety properties and the model-checking problem of the logic frequency-LTL. To prove the Positivity- and hence Skolem-hardness for the latter two problems, a new auxiliary path measure, called weighted long-run frequency, is introduced and the Positivity-hardness of the corresponding decision problem is shown as an intermediate step. For the partial and conditional SSPP on MDPs with non-negative weights and for the optimization of long-run probabilities of constrained reachability properties (a U b), solutions are known that rely on the identification of a bound on the accumulated weight or the number of consecutive visits to certain sates, called a saturation point, from which on optimal schedulers behave memorylessly. In this paper, it is shown that also the optimization of the conditional value-at-risk for the classical SSPP and of weighted long-run frequencies on MDPs with non-negative weights can be solved in pseudo-polynomial time exploiting the existence of a saturation point.
Upgrading the Newsroom: An Automated Image Selection System for News Articles
Venue: arXiv cs.MM 2020
Year: 2020

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level word embeddings to help both modeling morphologically rich languages, e.g. German, and transferring knowledge across nearby languages. The text encoder adopts a hierarchical self-attention mechanism to attend more to both keywords within a piece of text and informative components of a news article. We extensively experiment with our system on a large-scale text-image database containing multimodal multilingual news articles collected from Swiss local news media websites. The system is compared with multiple baselines with ablation studies and is shown to beat existing text-image retrieval methods in a weakly-supervised learning setting. Besides, we also offer insights on the advantage of using multiple textual sources and multilingual data.
War of the Hashtags: Trending New Hashtags to Override Critical Topics in Social Media
Venue: arXiv cs.SI 2020
Year: 2020

Hashtags play a cardinal role in the classification of topics over social media. A sudden burst on the usage of certain hashtags, representing specific topics, give rise to trending topics. Trending topics can be immensely useful as it can spark a discussion on a particular subject. However, it can also be used to suppress an ongoing pivotal matter. This paper discusses how a significant economic crisis was covered by triggering a current trending topic. A case study on politics in India has been studied over the past two months. The analysis shows how the issue on inflation was attacked by the exercise of a new constitutional law over media. Hashtags used to discuss the topics were scrutinized, and we notice a steep ascend of the more recent topic and an eventual drop in discussions over the previous issue on inflation. Balancing the influence of hashtags on social media can be employed. Still, it can be equally challenging since some hashtags that represent the need of the hour topics should be given more importance, and evaluating such issues can be hard.
Debiasing Skin Lesion Datasets and Models? Not So Fast
Venue: arXiv cs.CV 2020
Year: 2020

Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data. When models learn spurious correlations not found in real-world situations, their deployment for critical tasks, such as medical decisions, can be catastrophic. In this work we address this issue for skin-lesion classification models, with two objectives: finding out what are the spurious correlations exploited by biased networks, and debiasing the models by removing such spurious correlations from them. We perform a systematic integrated analysis of 7 visual artifacts (which are possible sources of biases exploitable by networks), employ a state-of-the-art technique to prevent the models from learning spurious correlations, and propose datasets to test models for the presence of bias. We find out that, despite interesting results that point to promising future research, current debiasing methods are not ready to solve the bias issue for skin-lesion models.
Multiple Segmentations of Thai Sentences for Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English--Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool.
Deep Global Registration
Venue: arXiv cs.CV 2020
Year: 2020

We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensional convolutional network for correspondence confidence prediction, a differentiable Weighted Procrustes algorithm for closed-form pose estimation, and a robust gradient-based SE(3) optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art methods, both learning-based and classical, on real-world data.
Customization and modifications of SignWriting by LIS users
Venue: arXiv cs.CL 2020
Year: 2020

Historically, the various sign languages (SL) have not developed an own writing system; nevertheless, some systems exist, among which the SignWriting (SW) is a powerful and flexible one. In this paper, we present the mechanisms adopted by signers of the Italian Sign Language (LIS), expert users of SW, to modify the standard SW glyphs and increase their writing skills and/or represent peculiar linguistic phenomena. We identify these glyphs and show which characteristics make them "acceptable" by the expert community. Eventually, we analyze the potentialities of these glyphs in hand writing and in computer-assisted writing, focusing on SWift, a software designed to allow the electronic writing-down of user-modified glyphs.
Concept Drift Detection via Equal Intensity k-means Space Partitioning
Venue: arXiv cs.LG 2020
Year: 2020

Data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind-spots. There is a need to improve the drift detection accuracy for histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence
Venue: arXiv cs.AI 2020
Year: 2020

We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the Dempster-Shafer theory of belief functions and possibility theory. In this perspective, Dempster-Shafer theory deals with belief functions generated by random sets, while possibility theory deals with belief functions induced by fuzzy sets. The more general theory allows us to represent and combine evidence that is both uncertain and fuzzy. We demonstrate the application of this formalism to statistical inference, and show that it makes it possible to reconcile the possibilistic interpretation of likelihood with Bayesian inference.
Survey on Visual Sentiment Analysis
Venue: arXiv cs.CV 2020
Year: 2020

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the related applications, the subject is tackled under different main headings. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view: emotional models, dataset definition, feature design. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways. To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis. The paper also includes a description of new challenges, the evaluation from the viewpoint of progress toward more sophisticated systems and related practical applications, as well as a summary of the insights resulting from this study.
Retrofitting Parallelism onto OCaml
Venue: arXiv cs.PL 2020
Year: 2020

OCaml is an industrial-strength, multi-paradigm programming language, widely used in industry and academia. OCaml is also one of the few modern managed system programming languages to lack support for shared memory parallel programming. This paper describes the design, a full-fledged implementation and evaluation of a mostly-concurrent garbage collector (GC) for the multicore extension of the OCaml programming language. Given that we propose to add parallelism to a widely used programming language with millions of lines of existing code, we face the challenge of maintaining backwards compatibility--not just in terms of the language features but also the performance of single-threaded code running with the new GC. To this end, the paper presents a series of novel techniques and demonstrates that the new GC strikes a balance between performance and feature backwards compatibility for sequential programs and scales admirably on modern multicore processors.
6G White paper: Research challenges for Trust, Security and Privacy
Venue: arXiv cs.CR 2020
Year: 2020

The roles of trust, security and privacy are somewhat interconnected, but different facets of next generation networks. The challenges in creating a trustworthy 6G are multidisciplinary spanning technology, regulation, techno-economics, politics and ethics. This white paper addresses their fundamental research challenges in three key areas. Trust: Under the current "open internet" regulation, the telco cloud can be used for trust services only equally for all users. 6G network must support embedded trust for increased level of information security in 6G. Trust modeling, trust policies and trust mechanisms need to be defined. 6G interlinks physical and digital worlds making safety dependent on information security. Therefore, we need trustworthy 6G. Security: In 6G era, the dependence of the economy and societies on IT and the networks will deepen. The role of IT and the networks in national security keeps rising - a continuation of what we see in 5G. The development towards cloud and edge native infrastructures is expected to continue in 6G networks, and we need holistic 6G network security architecture planning. Security automation opens new questions: machine learning can be used to make safer systems, but also more dangerous attacks. Physical layer security techniques can also represent efficient solutions for securing less investigated network segments as first line of defense. Privacy: There is currently no way to unambiguously determine when linked, deidentified datasets cross the threshold to become personally identifiable. Courts in different parts of the world are making decisions about whether privacy is being infringed, while companies are seeking new ways to exploit private data to create new business revenues. As solution alternatives, we may consider blockchain, distributed ledger technologies and differential privacy approaches.
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning
Venue: arXiv cs.RO 2020
Year: 2020

The exploration-exploitation trade-off is at the heart of reinforcement learning (RL). However, most continuous control benchmarks used in recent RL research only require local exploration. This led to the development of algorithms that have basic exploration capabilities, and behave poorly in benchmarks that require more versatile exploration. For instance, as demonstrated in our empirical study, state-of-the-art RL algorithms such as DDPG and TD3 are unable to steer a point mass in even small 2D mazes. In this paper, we propose a new algorithm called "Plan, Backplay, Chain Skills" (PBCS) that combines motion planning and reinforcement learning to solve hard exploration environments. In a first phase, a motion planning algorithm is used to find a single good trajectory, then an RL algorithm is trained using a curriculum derived from the trajectory, by combining a variant of the Backplay algorithm and skill chaining. We show that this method outperforms state-of-the-art RL algorithms in 2D maze environments of various sizes, and is able to improve on the trajectory obtained by the motion planning phase.
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study
Venue: arXiv cs.IR 2020
Year: 2020

Identifying semantically identical questions on, Question and Answering social media platforms like Quora is exceptionally significant to ensure that the quality and the quantity of content are presented to users, based on the intent of the question and thus enriching overall user experience. Detecting duplicate questions is a challenging problem because natural language is very expressive, and a unique intent can be conveyed using different words, phrases, and sentence structuring. Machine learning and deep learning methods are known to have accomplished superior results over traditional natural language processing techniques in identifying similar texts. In this paper, taking Quora for our case study, we explored and applied different machine learning and deep learning techniques on the task of identifying duplicate questions on Quora's dataset. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed previous studies on this task. Xgboost model with character level term frequency and inverse term frequency is our best machine learning model that has also outperformed a few of the Deep learning baseline models. We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Our deep learning models achieved better accuracy than machine learning models. Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora's question pair dataset, and our best model achieved accuracy of 85.82% which is close to Quora state of the art accuracy.
Revenue Maximization Approaches in IaaS Clouds: Research Challenges and Opportunities
Venue: arXiv cs.DC 2020
Year: 2020

Revenue generation is the main concern of any business, particularly in the cloud, where there is no direct interaction between the provider and the consumer. Cloud computing is an emerging core for today's businesses, however, Its complications (e.g, installation, and migration) with traditional markets are the main challenges. It earns more but needs exemplary performance and marketing skills. In recent years, cloud computing has become a successful paradigm for providing desktop services. It is expected that more than \$ 331 billion will be invested by 2023, likewise, 51 billion devices are expected to be connected to the cloud. Infrastructure as a Service (IaaS) provides physical resources (e.g, computing, memory, storage, and network) as VM instances. In this article, the main revenue factors are categorized as SLA and penalty management, resource scalability, customer satisfaction and management, resource utilization and provision, cost and price management, and advertising and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered.
Hardware Impairments Aware Full-Duplex NOMA Networks Over Rician Fading Channels
Venue: arXiv cs.IT 2020
Year: 2020

A cooperative full duplex (FD) non-orthogonal multiple access (NOMA) scheme over Rician fading channels is considered. To be practical, imperfect successive interference cancellation (ipSIC) and residual hardware impairments (RHIs) at transceivers are taken into account. To evaluate the performance of the considered system, the analytical approximate expressions for the outage probability (OP) and the ergodic rate (ER) of the considered system are derived, and the asymptotic performance is also explored. Simulation results manifest that the FD cooperative NOMA can improve the ergodic sum rate (ESR) of the system compared to the half duplex (HD) mode.
Evolution of Q Values for Deep Q Learning in Stable Baselines
Venue: arXiv cs.LG 2020
Year: 2020

We investigate the evolution of the Q values for the implementation of Deep Q Learning (DQL) in the Stable Baselines library. Stable Baselines incorporates the latest Reinforcement Learning techniques and achieves superhuman performance in many game environments. However, for some simple non-game environments, the DQL in Stable Baselines can struggle to find the correct actions. In this paper we aim to understand the types of environment where this suboptimal behavior can happen, and also investigate the corresponding evolution of the Q values for individual states. We compare a smart TrafficLight environment (where performance is poor) with the AI Gym FrozenLake environment (where performance is perfect). We observe that DQL struggles with TrafficLight because actions are reversible and hence the Q values in a given state are closer than in FrozenLake. We then investigate the evolution of the Q values using a recent decomposition technique of Achiam et al.. We observe that for TrafficLight, the function approximation error and the complex relationships between the states lead to a situation where some Q values meander far from optimal.
Exploring Explainable Selection to Control Abstractive Summarization
Venue: arXiv cs.CL 2020
Year: 2020

Like humans, document summarization models can interpret a document's contents in a number of ways. Unfortunately, the neural models of today are largely black boxes that provide little explanation of how or why they generated a summary in the way they did. Therefore, to begin prying open the black box and to inject a level of control into the substance of the final summary, we developed a novel select-and-generate framework that focuses on explainability. By revealing the latent centrality and interactions between sentences, along with scores for sentence novelty and relevance, users are given a window into the choices a model is making and an opportunity to guide those choices in a more desirable direction. A novel pair-wise matrix captures the sentence interactions, centrality, and attribute scores, and a mask with tunable attribute thresholds allows the user to control which sentences are likely to be included in the extraction. A sentence-deployed attention mechanism in the abstractor ensures the final summary emphasizes the desired content. Additionally, the encoder is adaptable, supporting both Transformer- and BERT-based configurations. In a series of experiments assessed with ROUGE metrics and two human evaluations, ESCA outperformed eight state-of-the-art models on the CNN/DailyMail and NYT50 benchmark datasets.
CQE in Description Logics Through Instance Indistinguishability (extended version)
Venue: arXiv cs.AI 2020
Year: 2020

We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-Lite$_{\mathcal{R}}$ ontologies, through a comparison with an alternative, existing confidentiality-preserving approach to CQE. Finally, we identify a semantically well-founded notion of approximated query answering for CQE, and prove that, for DL-Lite$_{\mathcal{R}}$ ontologies, this form of CQE is tractable with respect to data complexity and is first-order rewritable, i.e., it is always reducible to the evaluation of a first-order query over the data instance.