Reviewerly/exHarmony huggingface dataset:
PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS

Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the concept of “starting small”, we present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction. We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark datasets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations. By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.
Bayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage

The CACOC (Chaotic Ant Colony Optimisation for Coverage) algorithm has been developed to manage the mobility of a swarm of Unmanned Aerial Vehicles (UAVs). Using a specific chaotic dynamic obtained from the Rössler system, CACOC provides waypoints for UAVs that aim to optimise the coverage of an unknown area while having unpredictable trajectories. Since the chaotic dynamics are obtained from a three differential equations system with parameters, it is possible to tune one parameter to obtain another chaotic dynamic, which will result in different UAV mobility behaviours. This work aims at optimising this parameter of the Rössler chaotic system to improve the coverage performance of CACOC. Since each evaluation of a solution requires a full simulation, global optimisation techniques (e.g., population-based heuristics) would be very time-consuming. We therefore considered a surrogate-based method to efficiently explore the parameter space of the Rössler system for CACOC, i.e., Bayesian optimisation. Experimental results demonstrate that this approach permits to improve the speed of coverage of the UAV swarm. In addition an analysis of the dynamical properties of the obtained chaotic system is provided.
Improving Statistical Machine Translation Quality Using Differential Evolution

Machine Translation has become an important tool in overcoming the language barrier.The quality of translations depends on the languages and used methods.The research presented in this paper is based on well-known standard methods for Statistical Machine Translation that are advanced by a newly proposed approach for optimizing the weights of translation system components.Better weights of system components improve the translation quality.In most cases, machine translation systems translate to/from English and, in our research, English is paired with a Slavic language, Slovenian.In our experiment, we built two Statistical Machine Translation systems for the Slovenian-English language pair of the Acquis Communautaire corpus.Both systems were optimized using self-adaptive Differential Evolution and compared to the other related optimization methods.The results show improvement in the translation quality, and are comparable to the other related methods.
Improving the Annotations in the Turkish Universal Dependency Treebank

This study focuses on a comprehensive analysis and manual re-annotation of the Turkish IMST-UD Treebank, which was automatically converted from the IMST Treebank (Sulubacak et al., 2016b).In accordance with the Universal Dependencies' guidelines and the necessities of Turkish grammar, the existing treebank was revised.The current study presents the revisions that were made alongside the motivations behind the major changes.Moreover, it reports the parsing results of a transition-based dependency parser and a graph-based dependency parser obtained over the previous and updated versions of the treebank.In light of these results, we have observed that the re-annotation of the Turkish IMST-UD treebank improves performance with regards to dependency parsing.
Bucket Oblivious Sort: An Extremely Simple Oblivious Sort

We propose a conceptually simple oblivious sort and oblivious random permutation algorithms called bucket oblivious sort and bucket oblivious random permutation. Bucket oblivious sort uses 6n log n time (measured by the number of memory accesses) and 2Z client storage with an error probability exponentially small in Z. The above runtime is only 3× slower than a non-oblivious merge sort baseline; for 230 elements, it is 5× faster than bitonic sort, the de facto oblivious sorting algorithm in practical implementations.
Reference Extraction from Vietnamese Legal Documents

Legal and regulatory texts are ubiquitous and important in our life. Automated processing of such documents using natural language processing and information retrieval techniques is desired. Many legal text processing problems require information extraction as a base component. In this paper, we address the task of extracting references from law and regulatory documents, which are necessary for recognition of the relations between documents and document parts, and other problems. We formulate the task as a sequence labeling problem and introduce several extraction models, consisting of both traditional (conditional random fields) and more advanced (deep neural networks) methods. In addition to features learned by deep networks, we investigate various types of manually engineered features that reflect the characteristics of legal documents. Our best model that combines bidirectional long short-term memory networks and conditional random fields achieves 95.35% in the F1 score on a corpus consisting of more than 11 thousand sentences from Vietnamese law and regulatory documents.
Undecidability of <i> d <sub>&lt;:</sub> </i> and its decidable fragments

Dependent Object Types (DOT) is a calculus with path dependent types, intersection types, and object self-references, which serves as the core calculus of Scala 3. Although the calculus has been proven sound, it remains open whether type checking in DOT is decidable. In this paper, we establish undecidability proofs of type checking and subtyping of $D_{<:}$, a syntactic subset of DOT. It turns out that even for $D_{<:}$, undecidability is surprisingly difficult to show, as evidenced by counterexamples for past attempts. To prove undecidability, we discover an equivalent definition of the $D_{<:}$ subtyping rules in normal form. Besides being easier to reason about, this definition makes the phenomenon of bad bounds explicit as a single inference rule. After removing this rule, we discover two decidable fragments of $D_{<:}$ subtyping and identify algorithms to decide them. We prove soundness and completeness of the algorithms with respect to the fragments, and we prove that the algorithms terminate. Our proofs are mechanized in a combination of Coq and Agda.
Blockchain: Embedding Trust in Organic Products’ Supply Chain

Blockchain is set to disrupt supply chain activities. Research has barely begun to assess its effects on various inter-organizational interactions and on the end-consumer. This paper explores the lack of trust that consumers of organic products have on its supply chain, why it is important to reduce that trust gap and how blockchain is likely to help in embedding trust in it. Special emphasis has been placed on roles of smart-contracts and internet-of-things in blockchain based solutions.
Blockchain Technology in Finance Industry

Blockchain is a peer-to-peer public ledger which guarantees the reliability of all the transactional record without a trusted third party. Basically, all the participants in the network create transactional record, examine the validity of the transaction, and keep the valid transactional record. The whole finance industry including banks and fintechs recognizes the value of the blockchain because of its efficiency and secure mechanism. In this paper, we explained the blockchain mechanism by providing its basic principles, strengths and weaknesses. Then, we provided what comes with it in the finance industry. Though there are still a few disadvantages such as persistence, and lack of the standard improvements and modifications in blockchain technology could overcome them and lead to the broader acceptance. Blockchain is definitely a disruptive technology in the financial system.
Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).
Masking Orchestration: Multi-Task Pretraining for Multi-Role Dialogue Representation Learning

Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.
Formalized Computer-Aided Handwriting Psychology: Validation and Integration into Psychological Assessment

In contrast to traditional researches that involve a manual, non-quantitative, and subjective way of performing handwriting analysis, in the current research, a special computer-aided method of revised handwriting analysis is used. It includes the detection of personality traits via manual quantitative registration of handwriting signs and their automated quantitative evaluation. This method is based on a mathematical–statistical model that integrates multiple international publications on the evaluation of handwriting signs. The first aim is the validation of the revised method against the 16 Personality Factor Questionnaire Revised (16PF-R), which is performed as a self-report personality test by test persons and was developed and researched empirically by Raymond B. Cattell et al. A second aim is the development of an integrated model for assessment including handwriting analysis: when both methods come to the same result on a certain scale, then the construct can be accepted with higher reliability; in contrast, when results are contradictory, they should be regarded as a limitation of each method and raise awareness in the researchers, as these contradictions are a precious source of additional information regarding the complexity, ambiguity, and context specificity of personality traits.
Citizens Vote to Act: smart contracts for the management of water resources in smart cities

Smart cities leverage Information and Communication Technologies (ICTs) to enhance the quality of urban services. However, it is nowadays clear that the success of a smart city largely depends on the level of engagement of its citizens. In this paper we explore to what extent disruptive blockchain technologies can be used to incentivise the democratic participation of citizen. The investigated approach extends the standard IoT cycle 1) sense data, 2) cloudify and elaborate them, and 3) push information to the users. Here, the user takes an active role by means of data-informed votes on policies, therefore influencing behaviours. We illustrate such an approach by means of a proof-of-concept decentralised application (dApp) supporting the negotiation of polices for the management of urban water resources. The dApp consists of a smart contract that manages the execution of other smart contracts (the policies) according to the data-driven choices of the community. This use case demonstrates how suitably blockchain technologies can support fair and safe access to data and user engagement in smart cities.
Gradient-Based Optimization for Bayesian Preference Elicitation

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel Monte Carlo method for EVOI optimization, which is much more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or “partial items,” which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.
Simulation research on classification and identification of typical active jamming against LFM radar

In this paper, models of jamming signals are established based on the mechanism of active jamming signals against LFM radar. Five time-domain characteristics and frequency-domain characteristics of jamming signals are extracted. The decision tree method, BP neural network method and decision tree support vector machine (DTSVM) method are used to establish the classification models, and the simulation is performed for identifying and classifying the jamming signals at different jamming-to-noise ratio (JNR). The result shows that the model based on DTSVM method has better adaptability, smaller calculation and higher recognition success rate at low JNR.
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding

Common grounding is the process of creating, repairing and updating mutual understandings, which is a fundamental aspect of natural language conversation. However, interpreting the process of common grounding is a challenging task, especially under continuous and partially-observable context where complex ambiguity, uncertainty, partial understandings and misunderstandings are introduced. Interpretation becomes even more challenging when we deal with dialogue systems which still have limited capability of natural language understanding and generation. To address this problem, we consider reference resolution as the central subtask of common grounding and propose a new resource to study its intermediate process. Based on a simple and general annotation schema, we collected a total of 40,172 referring expressions in 5,191 dialogues curated from an existing corpus, along with multiple judgements of referent interpretations. We show that our annotation is highly reliable, captures the complexity of common grounding through a natural degree of reasonable disagreements, and allows for more detailed and quantitative analyses of common grounding strategies. Finally, we demonstrate the advantages of our annotation for interpreting, analyzing and improving common grounding in baseline dialogue systems.
Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an Inter-Domain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on COCO).
Text Quality Analysis of Emergency Response Plans

Emergency response plans are regarded as effective guidance for natural disasters and these plans describe emergency response processes in natural language. More specifically, they are textual process descriptions and describe not only how all departments perform their own response tasks, but also how different departments interact with each other. Analyzing text quality of emergency response plans as a typical evaluation approach is an important concern in emergency responses. Because of the flexibility of natural language, emergency response plans normally contain unwanted ambiguities, and it is difficult to check consistency and completeness. Automatic text quality analysis of emergency response plans written in Chinese from the perspective of process descriptions is proposed in this paper. Firstly, three types of response tasks including message sending tasks, message receiving tasks and regular tasks are extracted through Bi-LSTM-CRF networks (a Conditional Random Fields network is combined with a Bidirectional Long Short-Term Memory network). Then, a series of text quality analysis rules associated with extracted response tasks are created. These rules focus on the progressive relationship of four levels of emergency responses, completeness of response tasks, ambiguity and redundancy of emergency response plans. Finally, real-world data is collected to validate the proposed approach, which consists of four types of emergency response plans of natural disasters including district, municipal, provincial and national emergency response plans. It is demonstrated that the proposed approach can be used to facilitate revisions and improvements of emergency response plans.
Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

In robot-assisted endoscopic nasal surgery, due to the slender and complicated nasal cavity and sinus anatomical structure, the nasal endoscope of robot end-effector is prone to injure surrounding tissues during surgical approaches. In order to improve the movement safety of the endoscope in robot-assisted endoscopic nasal surgery, a path planning method for endoscopic surgical approaches based on medical image pixel map searching is proposed in this paper, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Firstly, through the trajectory analysis of nasal endoscope tip during surgery, the path planning requirements of endoscope tip are analyzed and obtained. Then, considering the slender and complicated anatomy of the nasal cavity, a binarized three-dimensional grid map containing the spatial anatomy of the nasal cavity is constructed based on the patient's CT medical image sequence. The endoscopic surgical approaches are searched and optimized by introducing the A-star algorithm, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Finally, a virtual nasal endoscopy system is developed and tested on a head model containing nasal tissue, and the effectiveness of the planned surgical paths is verified by automatic virtual nasal endoscopy browsing experiment.