OpenRaiser/Intern-Atlas huggingface dataset:
Visual Perception and Modelling in Unstructured Orchard for Apple Harvesting Robots
Venue: arXiv cs.CV 2019
Year: 2019

Vision perception and modelling are the essential tasks of robotic harvesting in the unstructured orchard. This paper develops a framework of visual perception and modelling for robotic harvesting of fruits in the orchard environments. The developed framework includes visual perception, scenarios mapping, and fruit modelling. The Visual perception module utilises a deep-learning model to perform multi-purpose visual perception task within the working scenarios; The scenarios mapping module applies OctoMap to represent the multiple classes of objects or elements within the environment; The fruit modelling module estimates the geometry property of objects and estimates the proper access pose of each fruit. The developed framework is implemented and evaluated in the apple orchards. The experiment results show that visual perception and modelling algorithm can accurately detect and localise the fruits, and modelling working scenarios in real orchard environments. The $F_{1}$ score and mean intersection of union of visual perception module on fruit detection and segmentation are 0.833 and 0.852, respectively. The accuracy of the fruit modelling in terms of centre localisation and pose estimation are 0.955 and 0.923, respectively. Overall, an accurate visual perception and modelling algorithm are presented in this paper.
Adaptive Algorithm for Finding Connected Dominating Sets in Uncertain Graphs
Venue: arXiv cs.DS 2019
Year: 2019

The problem of finding a minimum-weight connected dominating set (CDS) of a given undirected graph has been studied actively, motivated by operations of wireless ad hoc networks. In this paper, we formulate a new stochastic variant of the problem. In this problem, each node in the graph has a hidden random state, which represents whether the node is active or inactive, and we seek a CDS of the graph that consists of the active nodes. We consider an adaptive algorithm for this problem, which repeat choosing nodes and observing the states of the nodes around the chosen nodes until a CDS is found. Our algorithms have a theoretical performance guarantee that the sum of the weights of the nodes chosen by the algorithm is at most $O(α\log (1/δ))$ times that of any adaptive algorithm in expectation, where $α$ is an approximation factor for the node-weighted polymatroid Steiner tree problem and $δ$ is the minimum probability of possible scenarios on the node states.
Individual specialization in multi-task environments with multiagent reinforcement learners
Venue: arXiv cs.AI 2019
Year: 2019

There is a growing interest in Multi-Agent Reinforcement Learning (MARL) as the first steps towards building general intelligent agents that learn to make low and high-level decisions in non-stationary complex environments in the presence of other agents. Previous results point us towards increased conditions for coordination, efficiency/fairness, and common-pool resource sharing. We further study coordination in multi-task environments where several rewarding tasks can be performed and thus agents don't necessarily need to perform well in all tasks, but under certain conditions may specialize. An observation derived from the study is that epsilon greedy exploration of value-based reinforcement learning methods is not adequate for multi-agent independent learners because the epsilon parameter that controls the probability of selecting a random action synchronizes the agents artificially and forces them to have deterministic policies at the same time. By using policy-based methods with independent entropy regularised exploration updates, we achieved a better and smoother convergence. Another result that needs to be further investigated is that with an increased number of agents specialization tends to be more probable.
Predictive Scheduling for Virtual Reality
Venue: arXiv cs.NI 2019
Year: 2019

A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases. In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
Venue: arXiv cs.CV 2019
Year: 2019

One of the most significant challenges facing a few-shot learning task is the generalizability of the (meta-)model from the base to the novel categories. Most of existing few-shot learning models attempt to address this challenge by either learning the meta-knowledge from multiple simulated tasks on the base categories, or resorting to data augmentation by applying various transformations to training examples. However, the supervised nature of model training in these approaches limits their ability of exploring variations across different categories, thus restricting their cross-category generalizability in modeling novel concepts. To this end, we present a novel regularization mechanism by learning the change of feature representations induced by a distribution of transformations without using the labels of data examples. We expect this regularizer could expand the semantic space of base categories to cover that of novel categories through the transformation of feature representations. It could minimize the risk of overfitting into base categories by inspecting the transformation-augmented variations at the encoded feature level. This results in the proposed FLAT (Few-shot Learning via Autoencoding Transformations) approach by autoencoding the applied transformations. The experiment results show the superior performances to the current state-of-the-art methods in literature.
Linnea: Automatic Generation of Efficient Linear Algebra Programs
Venue: arXiv cs.MS 2019
Year: 2019

The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. In order to combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen and Armadillo, with speedups up to and exceeding 10x.
Using ConceptNet to Teach Common Sense to an Automated Theorem Prover
Venue: arXiv cs.AI 2019
Year: 2019

The CoRg system is a system to solve commonsense reasoning problems. The core of the CoRg system is the automated theorem prover Hyper that is fed with large amounts of background knowledge. This background knowledge plays a crucial role in solving commonsense reasoning problems. In this paper we present different ways to use knowledge graphs as background knowledge and discuss challenges that arise.
On Quantified Modal Theorem Proving for Modeling Ethics
Venue: arXiv cs.AI 2019
Year: 2019

In the last decade, formal logics have been used to model a wide range of ethical theories and principles with the goal of using these models within autonomous systems. Logics for modeling ethical theories, and their automated reasoners, have requirements that are different from modal logics used for other purposes, e.g. for temporal reasoning. Meeting these requirements necessitates investigation of new approaches for proof automation. Particularly, a quantified modal logic, the deontic cognitive event calculus (DCEC), has been used to model various versions of the doctrine of double effect, akrasia, and virtue ethics. Using a fragment of DCEC, we outline these distinct characteristics and present a sketches of an algorithm that can help with some aspects proof automation for DCEC.
Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information
Venue: arXiv cs.CV 2019
Year: 2019

With the development of Information technology and communication, a large part of the databases is dedicated to images and videos. Thus retrieving images related to a query image from a large database has become an important area of research in computer vision. Until now, there are various methods of image retrieval that try to define image contents by texture, color or shape properties. In this paper, a method is presented for image retrieval based on a combination of local texture information derived from two different texture descriptors. First, the color channels of the input image are separated. The texture information is extracted using two descriptors such as evaluated local binary patterns and predefined pattern units. After extracting the features, the similarity matching is done based on distance criteria. The performance of the proposed method is evaluated in terms of precision and recall on the Simplicity database. The comparative results showed that the proposed approach offers higher precision rate than many known methods.
An Empirical Study of Factors Affecting Language-Independent Models
Venue: arXiv cs.CL 2019
Year: 2019

Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches. In this work, we empirically investigate the factors affecting language-independent models built with multilingual representations, including task type, language set and data resource. On two most representative NLP tasks -- sentence classification and sequence labeling, we show that language-independent models can be comparable to or even outperforms the models trained using monolingual data, and they are generally more effective on sentence classification. We experiment language-independent models with many different languages and show that they are more suitable for typologically similar languages. We also explore the effects of different data sizes when training and testing language-independent models, and demonstrate that they are not only suitable for high-resource languages, but also very effective in low-resource languages.
"Hinglish" Language -- Modeling a Messy Code-Mixed Language
Venue: arXiv cs.CL 2019
Year: 2019

With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem in categorizing social content written in Hindi-English into Abusive, Hate-Inducing and Not offensive categories. We utilize bi-directional sequence models with easy text augmentation techniques such as synonym replacement, random insertion, random swap, and random deletion to produce a state of the art classifier that outperforms the previous work done on analyzing this dataset.
Minimizing Impurity Partition Under Constraints
Venue: arXiv cs.IT 2019
Year: 2019

Set partitioning is a key component of many algorithms in machine learning, signal processing, and communications. In general, the problem of finding a partition that minimizes a given impurity (loss function) is NP-hard. As such, there exists a wealth of literature on approximate algorithms and theoretical analyses of the partitioning problem under different settings. In this paper, we formulate and solve a variant of the partition problem called the minimum impurity partition under constraint (MIPUC). MIPUC finds an optimal partition that minimizes a given loss function under a given concave constraint. MIPUC generalizes the recently proposed deterministic information bottleneck problem which finds an optimal partition that maximizes the mutual information between the input and partition output while minimizing the partition output entropy. Our proposed algorithm is developed based on a novel optimality condition, which allows us to find a locally optimal solution efficiently. Moreover, we show that the optimal partition produces a hard partition that is equivalent to the cuts by hyperplanes in the probability space of the posterior probability that finally yields a polynomial time complexity algorithm to find the globally optimal partition. Both theoretical and numerical results are provided to validate the proposed algorithm.
Modeling Neural Architecture Search Methods for Deep Networks
Venue: arXiv cs.CV 2019
Year: 2019

There are many research works on the designing of architectures for the deep neural networks (DNN), which are named neural architecture search (NAS) methods. Although there are many automatic and manual techniques for NAS problems, there is no unifying model in which these NAS methods can be explored and compared. In this paper, we propose a general abstraction model for NAS methods. By using the proposed framework, it is possible to compare different design approaches for categorizing and identifying critical areas of interest in designing DNN architectures. Also, under this framework, different methods in the NAS area are summarized; hence a better view of their advantages and disadvantages is possible.
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
Venue: arXiv cs.LG 2019
Year: 2019

Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target domain. Online transfer learning(OTL) is a more challenging problem where the target data arrive in an online manner. Most OTL methods combine source classifier and target classifier directly by assigning a weight to each classifier, and adjust the weights constantly. However, these methods pay little attention to reducing the distribution discrepancy between domains. In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously. We focus on online transfer learning with multiple source domains and use the Hedge strategy to leverage knowledge from source domains. We analyze the theoretical properties of the proposed algorithm and provide an upper mistake bound. Comprehensive experiments on two real-world datasets show that our method outperforms state-of-the-art methods by a large margin.
Fungal architecture
Venue: arXiv cs.ET 2019
Year: 2019

As one of the primary consumers of environmental resource, the building industry faces unprecedented challenges in needing to reduce the environmental impact of current consumption practices. This applies to both the construction of the built environment and resource consumption during its occupation and use. Where incremental improvements to current practices can be realised, the net benefits are often far outstripped by the burgeoning demands of rapidly increasing population growth and urbanisation. Against the backdrop of this grand societal challenge, it is necessary to explore approaches that envision a paradigm shift in how material is sourced, processed and assembled to address the magnitude of these challenges in a truly sustainable way, and which can even provide added value. We propose to develop a structural substrate by using live fungal mycelium, functionalise the substrate with nanoparticles and polymers to make a mycelium-based electronics, implement sensorial fusion and decision making in the fungal electronics and to growing monolithic buildings from the functionalised fungal substrate. Fungal buildings will self-grow, build, and repair themselves subject to substrate supplied, use natural adaptation to the environment, sense all what human can sense.
The MAP/M/s+G Call Center Model with General Patience Times: Stationary Solutions and First Passage Times
Venue: arXiv cs.PF 2019
Year: 2019

We study the MAP/M/s+G queuing model with MAP (Markovian Arrival Process) arrivals, exponentially distributed service times, infinite waiting room, and generally distributed patience times. Using sample-path arguments, we propose to obtain the steady-state distribution of the virtual waiting time and subsequently the other relevant performance metrics of interest for the MAP/M/s+G queue by means of finding the steady-state solution of a properly constructed Continuous Feedback Fluid Queue (CFFQ). The proposed method is exact when the patience time is a discrete random variable and is asymptotically exact when it is continuous/hybrid for which case discretization of the patience time distribution and subsequently the steady-state solution of a Multi-Regime Markov Fluid Queue (MRMFQ) is required. Besides the steady-state distribution, we also propose a new method to approximately obtain the first passage time distribution for the virtual and actual waiting times in the $MAP/M/s+G$ queue. Again, using sample-path arguments, finding the desired distribution is also shown to reduce to obtaining the steady-state solution of a larger dimensionality CFFQ where the deterministic time horizon is to be approximated by Erlang or Concentrated Matrix Exponential (CME) distributions. Numerical results are presented to validate the effectiveness of the proposed method.
Uji Performa dan Website Responsiveness Institusi dan Smart City se-Jawa Barat
Venue: arXiv cs.CY 2019
Year: 2019

The Government through the Ministry of Information and Communication Technology (Kemkominfo) held the 100 Smart Cities Program, an effort to improve the quality of public services. The program produces a Smart City Masterplan to develop regencies and cities. Access to the website of the regencies-cities government that is member of the program is one success indicator of using technology for the community, because the accessing speed (loading time) and responsiveness of the website is a convenience factor in service users. Hence, the importance of viewing and monitoring public service websites related to public service information in loading time and responsiveness of the website. From the data of regencies-cities registered in this program, many of them are from West Java Province, recorded around 11 of the total 26 regencies-cities. This study presents a website assessment using weighting method that focuses on loading time and responsiveness to see the performance of community services provided by the municipal governments that are incorporated in a smart city case study in West Java Province. There are six parameters in weighting performance of website used in this study, namely first-contentful-paint, first-meaningful-paint, speed-index, interactive, first-cpu-idle, and max potential first input delay. The weighting value is compared with Google's dataset, CrUX (Chrome User Experience Report). The test results show that the majority of websites have an average performance of 38.7 (mobile) 63.6 (web) of the 530 websites incorporated in the smart city, with the best results obtained by the City of Bandung.
Text Classification for Azerbaijani Language Using Machine Learning and Embedding
Venue: arXiv cs.CL 2019
Year: 2019

Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For example, the company shares a photo of a new product on Facebook and the company receives a thousand comments for new products. The systems classify the comments into categories like positive or negative. The system can also be applied in recommended systems, spam filtering, etc. Various machine learning techniques such as Naive Bayes, SVM, Decision Trees have been devised to solve the text classification problem in Azerbaijani language.
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifiers (OCCs) perform poorly under certain conditions such as high-dimensionality and sparsity. Also, the size of the training set plays an important role on the performance of one-class classifiers. Autoencoders have been widely used for obtaining useful latent variables from high-dimensional datasets. In the proposed approach, the AE is capable of deriving meaningful features from high-dimensional datasets while doing data augmentation at the same time. The augmented data is used for training the OCC algorithms. The experimental results show that the proposed approach enhance the performance of OCC algorithms and also outperforms other well-known approaches.
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
Venue: arXiv cs.CL 2019
Year: 2019

Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \& IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.