Reviewerly/exHarmony huggingface dataset:
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression.

In experimental design, we are given a large collection of vectors, each with a hidden response value that we assume derives from an underlying linear model, and we wish to pick a small subset of the vectors such that querying the corresponding responses will lead to a good estimator of the model. A classical approach in statistics is to assume the responses are linear, plus zero-mean i.i.d. Gaussian noise, in which case the goal is to provide an unbiased estimator with smallest mean squared error (A-optimal design). A related approach, more common in computer science, is to assume the responses are arbitrary but fixed, in which case the goal is to estimate the least squares solution using few responses, as quickly as possible, for worst-case inputs. Despite many attempts, characterizing the relationship between these two approaches has proven elusive. We address this by proposing a framework for experimental design where the responses are produced by an arbitrary unknown distribution. We show that there is an efficient randomized experimental design procedure that achieves strong variance bounds for an unbiased estimator using few responses in this general model. Nearly tight bounds for the classical A-optimality criterion, as well as improved bounds for worst-case responses, emerge as special cases of this result. In the process, we develop a new algorithm for a joint sampling distribution called volume sampling, and we propose a new i.i.d. importance sampling method: inverse score sampling. A key novelty of our analysis is in developing new expected error bounds for worst-case regression by controlling the tail behavior of i.i.d. sampling via the jointness of volume sampling. Our result motivates a new minimax-optimality criterion for experimental design which can be viewed as an extension of both A-optimal design and sampling for worst-case regression.
Active Generative Adversarial Network for Image Classification

Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance performance. In recent emerging studies, a generative adversarial network (GAN) has been integrated with active learning to generate good candidates to be presented to the oracle. In this paper, we propose a novel model that is able to obtain labels for data in a cheaper manner without the need to query an oracle. In the model, a novel reward for each sample is devised to measure the degree of uncertainty, which is obtained from a classifier trained with existing labeled data. This reward is used to guide a conditional GAN to generate informative samples with a higher probability for a certain label. With extensive evaluations, we have confirmed the effectiveness of the model, showing that the generated samples are capable of improving the classification performance in popular image classification tasks.
A Self-organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems

In this work, we present the development of a neuro-inspired approach for characterizing sensorimotor relations in robotic systems. The proposed method has self-organizing and associative properties that enable it to autonomously obtain these relations without any prior knowledge of either the motor (e.g. mechanical structure) or perceptual (e.g. sensor calibration) models. Self-organizing topographic properties are used to build both sensory and motor maps, then the associative properties rule the stability and accuracy of the emerging connections between these maps. Compared to previous works, our method introduces a new varying density self-organizing map (VDSOM) that controls the concentration of nodes in regions with large transformation errors without affecting much the computational time. A distortion metric is measured to achieve a self-tuning sensorimotor model that adapts to changes in either motor or sensory models. The obtained sensorimotor maps prove to have less error than conventional self-organizing methods and potential for further development.
Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning

Online planning methods for partially observable Markov decision processes (POMDPs) have recently gained much interest. In this paper, we propose the introduction of prior knowledge in the form of (probabilistic) relationships among discrete state-variables, for online planning based on the well-known POMCP algorithm. In particular, we propose the use of hard constraint networks and probabilistic Markov random fields to formalize state-variable constraints and we extend the POMCP algorithm to take advantage of these constraints. Results on a case study based on Rocksample show that the usage of this knowledge provides significant improvements to the performance of the algorithm. The extent of this improvement depends on the amount of knowledge encoded in the constraints and reaches the 50% of the average discounted return in the most favorable cases that we analyzed.
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter

In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019).Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULM-FiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets.We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets.We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.
Probability Density Rank-Based Quantization for Convex Universal Learning Machines

The distributions of input data are very important for learning machines, such as the convex universal learning machines (CULMs). The CULMs are a family of universal learning machines with convex optimization. However, the computational complexity is a crucial problem in CULMs, because the dimension of the nonlinear mapping layer (the hidden layer) of the CULMs is usually rather large in complex system modeling. In this article, we propose an efficient quantization method called Probability density Rank-based Quantization (PRQ) to decrease the computational complexity of CULMs. The PRQ ranks the data according to the estimated probability densities and then selects a subset whose elements are equally spaced in the ranked data sequence. We apply the PRQ to kernel ridge regression (KRR) and random Fourier feature recursive least squares (RFF-RLS), which are two typical algorithms of CULMs. The proposed method not only keeps the similarity of data distribution between the code book and data set but also reduces the computational cost by using the kd-tree. Meanwhile, for a given data set, the method yields deterministic quantization results, and it can also exclude the outliers and avoid too many borders in the code book. This brings great convenience to practical applications of the CULMs. The proposed PRQ is evaluated on several real-world benchmark data sets. Experimental results show satisfactory performance of PRQ compared with some state-of-the-art methods.
BDA: practical dependence analysis for binary executables by unbiased whole-program path sampling and per-path abstract interpretation

Binary program dependence analysis determines dependence between instructions and hence is important for many applications that have to deal with executables without any symbol information. A key challenge is to identify if multiple memory read/write instructions access the same memory location. The state-of-the-art solution is the value set analysis (VSA) that uses abstract interpretation to determine the set of addresses that are possibly accessed by memory instructions. However, VSA is conservative and hence leads to a large number of bogus dependences and then substantial false positives in downstream analyses such as malware behavior analysis. Furthermore, existing public VSA implementations have difficulty scaling to complex binaries. In this paper, we propose a new binary dependence analysis called BDA enabled by a randomized abstract interpretation technique. It features a novel whole program path sampling algorithm that is not biased by path length, and a per-path abstract interpretation avoiding precision loss caused by merging paths in traditional analyses. It also provides probabilistic guarantees. Our evaluation on SPECINT2000 programs shows that it can handle complex binaries such as gcc whereas VSA implementations from the-state-of-art platforms have difficulty producing results for many SPEC binaries. In addition, the dependences reported by BDA are 75 and 6 times smaller than Alto, a scalable binary dependence analysis tool, and VSA, respectively, with only 0.19% of true dependences observed during dynamic execution missed (by BDA). Applying BDA to call graph generation and malware analysis shows that BDA substantially supersedes the commercial tool IDA in recovering indirect call targets and outperforms a state-of-the-art malware analysis tool Cuckoo by disclosing 3 times more hidden payloads.
Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

Advances in deep learning techniques have led to compelling achievements in medical image analysis. However, performance of neural network models degrades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift between MR images. We evaluate our method on two publicly available MRI dataset to address two different types of domain shifts: On the BraTS dataset [11] to mitigate domain shift between high grade and low grade gliomas and on the SCGM dataset [13] to tackle cross institutional domain shift. Through extensive evaluation, we show that our method achieves favorable results on both datasets.
Anytime Large-Scale Analytics of Linked Open Data

Analytical queries are queries with numerical aggregators: computing the average number of objects per property, identifying the most frequent subjects, etc. Such queries are essential to monitor the quality and the content of the Linked Open Data (LOD) cloud. Many analytical queries cannot be executed directly on the SPARQL endpoints, because the fair use policy cuts off expensive queries. In this paper, we show how to rewrite such queries into a set of queries that each satisfy the fair use policy. We then show how to execute these queries in such a way that the result provably converges to the exact query answer. Our algorithm is an anytime algorithm, meaning that it can give intermediate approximate results at any time point. Our experiments show that the approach converges rapidly towards the exact solution, and that it can compute even complex indicators at the scale of the LOD cloud.
Classification of Twitter Contents using Chi-Square and K-Nearest Neighbour Algorithm

A survey conducted by Hootsuite in 2019, that there are 3,484 billion active social media users. The percentage of active social media users has increased by 15% from 2018. One way or action is to anticipate negative things that quickly circulate among the public such as fake news (hoax), reduced ethics and courtesy or referred to as negative content. From various types of social media such as; Facebook, Twitter, Instagram, and others, one of which Twitter has a percentage of 52% is a social media network service. In this study we used a dataset obtained from UCI in 2014 entitled “Twitter dataset for Arabic Sentiment Analysis”[7]. Category of twitter content using the Chi-Square algorithm and KNN as the method. The chi-square method is an algorithm for selecting features, and for the classification process the author uses the KNN algorithm. This study concluded that, the KNN algorithm with feature selection (chi-square) obtained the results of accuracy seen from the value of the nearest neighbor distance K = 3 obtained the same results with an accuracy value of 65.00%, which states that both methods with feature selection and without selection the feature in the dataset turns out that the method with feature selection has the advantage of being more efficient in the calculation process.
A New Path Evaluation Method for Path Planning With Localizability

As a fundamental capability of mobile robots, path planning highly relies on the accurate localization of the robot. However, limited consideration for the localizability (which describes the capability of acquiring accurate localization) has been made in path planning. This brings a high risk of choosing a path that is optimal but results in the robot easily getting lost. There exist two key challenges to address this problem: 1) How to evaluate the localizability of a path and its impact on path planning. 2) How to balance the localizability of a path and the standard path planning criteria (e.g., shortest travel distance, obstacle-free path, etc.. To overcome the two challenges a new path evaluation method is required. So we first analyzed the uncertainty that comes from dead-reckoning and map matching. Then the localizability was estimated by the fusion of the uncertainty coming from both of them. Based on that, the impact of the localizability on the path planning task has been evaluated by an evaluation function. By combining the localizability evaluation function with traditional criteria (e.g., shortest length, obstacle-free path, etc.), a new path evaluation function for path planning is established. Both simulation and experimental studies show that the new path evaluation function can offer a balance between the localizability and the traditional criteria for path planning.
A New Measure of Polarization in the Annotation of Hate Speech

The number of social media users is ever-increasing. Unfortunately, this has also resulted in the massive rise of uncensored online hate against vulnerable communities such as immigrants, LGBT and women. Current work on the automatic detection of various forms of hate speech (HS) typically employs supervised learning, requiring manually annotated data. The highly polarizing nature of the topics involved raises concerns about the quality of annotations these systems rely on, because not all the annotators are equally sensitive to different kinds of hate speech. We propose an approach to leverage the fine-grained knowledge expressed by individual annotators, before their subjectivity is averaged out by the gold standard creation process. This helps us to refine the quality of training sets for hate speech detection. We introduce a measure of polarization at the level of single instances in the data to manipulate the training set and reduce the impact of most polarizing text on the learning process. We test our approach on three datasets, in English and Italian, annotated by experts and workers hired on a crowdsourcing platform. We classify instances of sexist, racist, and homophobic hate speech in tweets and show how our approach improves the prediction performance of a supervised classifier. Moreover, the proposed polarization measure helps towards the manual exploration of the individual instances of tweets in our datasets.
Embedding Symbolic Knowledge into Deep Networks

In this work, we aim to leverage prior symbolic knowledge to improve the performance of deep models. We propose a graph embedding network that projects propositional formulae (and assignments) onto a manifold via an augmented Graph Convolutional Network (GCN). To generate semantically-faithful embeddings, we develop techniques to recognize node heterogeneity, and semantic regularization that incorporate structural constraints into the embedding. Experiments show that our approach improves the performance of models trained to perform entailment checking and visual relation prediction. Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding. Future exploration of this connection may elucidate the relationship between knowledge compilation and vector representation learning.
Dissecting the Performance of VR Video Streaming through the VR-EXP Experimentation Platform

To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques.
Variational Recurrent Models for Solving Partially Observable Control Tasks

In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorithm for solving PO tasks. Our method comprises two parts: a variational recurrent model (VRM) for modeling the environment, and an RL controller that has access to both the environment and the VRM. The proposed algorithm was tested in two types of PO robotic control tasks, those in which either coordinates or velocities were not observable and those that require long-term memorization. Our experiments show that the proposed algorithm achieved better data efficiency and/or learned more optimal policy than other alternative approaches in tasks in which unobserved states cannot be inferred from raw observations in a simple manner.
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit-assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.
Scrum Without a Scrum Master

The fact that since March 2018 Agile is included in the Project Management Body of Knowledge (PMBOK), indicates the importance of this methodology in project management. Scrum as the one of the most accepted Agile forms emphasizes the importance of Scrum Master who has many responsibilities: teaching, coaching, mentoring the team, removing impediments, acting as a protector of the team, etc. However, many companies assign this role as additional work to a team member, a project manager or even to an executive. Guided by the impression that the role of Scrum Master is not fully recognized and appreciated, this paper will attempt to determine the potential risk of not having a dedicated Scrum Master within an agile team. As a research tool, we used interviews and online surveys with employees of companies that use Scrum.
Augmented Reality to support education in Industry 4.0

Game-based learning is one of the most widespread approaches in educational and training processes. The success of the integration of the entertainment dimension in learning has been largely documented. It is important to make the learning process attractive not only for kids but also for adults who need to acquire new knowledge and skills to be in line with the new competences required by the job market. Thus, in line with the requirements of Industry 4.0, all companies, especially the smallest ones, should upgrade their work processes to permit to keep up with larger and technologically advanced companies. In this context, the Competences Lab for Industry 4.0 (C-LAB 4.0) project aims to define a participatory model for the promotion of educational and training processes supported by a game simulation solution. The article presents an application for geometric solids developed as proof-of-concept of the use of Augmented Reality (AR) in educational contexts. A comparative study proves the learning effectiveness of the defined AR application used as support in a traditional learning context.
The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. We summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.