Reviewerly/exHarmony huggingface dataset:
Development of Python Application for Automated Geomatics Data Processing, Analysis, and Visualisation

Abstract Emerging technologies have led to the rapid generation of vast quantities of field data by geomatics professionals. However, these raw field data cannot be directly utilised unless they undergo rigorous processing (using complex mathematical models e.g. - reductions, adjustments, and computations) to convert them to meaningful information. These operations are often repetitive. Despite global advancement in automated data processing, most surveyors in developing countries such as Nigeria still deploy manual surveying computation techniques for their day-to-day data processing and analysis needs. Hence, the development of cost effective and easy-to-use automated applications based on the existing models is highly relevant, to ease operations requiring repeated processes. This study developed a Python-based geomatics application “ Survey Companion ” that can streamline the processing of survey field data into meaningful spatial information. To achieve these, python codes were written for the processing of data from key geomatics operations such as traverse and levelling networks, resection, intersection, and bathymetric operations as well as area computation. It is expected that this application will greatly improve survey operations in developing countries as well as increase the speed of data processing, thereby reducing overall project costs.
30 visualisation of historical site “Sumberawan temple” preservation utilizing virtual reality technology

Virtual Reality (VR) has been extensively developed in numerous studies to visualize historical sites. VR is advantageous to bring users realistic-feeling environment without any location and time boundaries. One of the historical sites serving as the object of VR research is a temple. In Indonesia, numerous temples are typically visited and implemented as a means of studying history. Most prior studies however focus on big and famous temples in Indonesia and particularly in Malang. Previous research thus inspires the development of a 30 model of Sumberawan temple in a 30 virtualization as an effort to preserve historical sites prone to damages over time to facilitate virtual documentation. In addition, more attractive 30 visualization of the existing historical information has been to provide information as a media to the public through an image depicting the temple conditions. This study thus aims to develop a prototype by creating a 30 visualization of Sumberawan temple and some artifacts surrounding the temple site employing Kinect v2 and software Blender. As a result, the 30 respondents provided positive responses and interests regarding the information of Sumberawan temple, strongly agreed that the prototype was necessary as a documentation medium.
Usability evaluation utilizing SUS (system usability scale) method and correlation determination with student graduation rate

: The level of usability of a software plays an important role due to its advantages on users where the success of a software, one of which lies in the quality of the software itself. Universitas Muhammadiyah Malang (UMM) is one of the universities in Malang City that has developed an e-learning (LMS) based learning model. In the UMM LMS website, there are various features which are accessible by lecturers and students enabling the student and lecturer discussion concerning the provided material at LMS. Upon considering the level of Usability in the LMS system, the researcher evaluates the LMS system, where the Usability evaluation aims to determine and ensure that the developed system is in accordance with the user criteria and whether the Usability factor of the LMS system affects the final grades of students. Therefore, this study aims to determine whether there is a correlation between the level of usability and the graduation rate of students from a Learning Management System (LMS) website at the Universitas Muhammadiyah Malang. The utilized method is the SUS (Syatem Usability Scale) method. The final result of the usability level is 57.5 classified as weak, and upon the completion of the correlation test with the student graduation rate, it is navigated that there is no correlation between the two and the correlation value is worth 0.
Low Bandwidth Video-Chat Compression using Deep Generative Models

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.
Security Analysis of Ripple Consensus

The Ripple network is one of the most prominent blockchain platforms and its native XRP token currently has one of the highest cryptocurrency market capitalizations. The Ripple consensus protocol powers this network and is generally considered to a Byzantine fault-tolerant agreement protocol, which can reach consensus in the presence of faulty or malicious nodes. In contrast to traditional Byzantine agreement protocols, there is no global knowledge of all participating nodes in Ripple consensus; instead, each node declares a list of other nodes that it trusts and from which it considers votes. Previous work has brought up concerns about the liveness and safety of the consensus protocol under the general assumptions stated initially by Ripple, and there is currently no appropriate understanding of its workings and its properties in the literature. This paper closes this gap and makes two contributions. It first provides a detailed, abstract description of the protocol, which has been derived from the source code. Second, the paper points out that the abstract protocol may violate safety and liveness in several simple executions under relatively benign network assumptions.
A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces

Authorial clustering involves the grouping of documents written by the same author or team of authors without any prior positive examples of an author's writing style or thematic preferences. For authorial clustering on shorter texts (paragraph-length texts that are typically shorter than conventional documents), the document representation is particularly important: very high-dimensional feature spaces lead to data sparsity and suffer from serious consequences like the curse of dimensionality, while feature selection may lead to information loss. We propose a high-level framework which utilizes a compact data representation in a latent feature space derived with non-parametric topic modeling. Authorial clusters are identified thereafter in two scenarios: (a) fully unsupervised and (b) semi-supervised where a small number of shorter texts are known to belong to the same author (must-link constraints) or not (cannot-link constraints). We report on experiments with 120 collections in three languages and two genres and show that the topic-based latent feature space provides a promising level of performance while reducing the dimensionality by a factor of 1500 compared to state-of-the-arts. We also demonstrate that, while prior knowledge on the precise number of authors (i.e. authorial clusters) does not contribute much to additional quality, little knowledge on constraints in authorial clusters memberships leads to clear performance improvements in front of this difficult task. Thorough experimentation with standard metrics indicates that there still remains an ample room for improvement for authorial clustering, especially with shorter texts
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates

Model-free reinforcement learning (RL) is a powerful tool to learn a broad range of robot skills and policies. However, a lack of policy interpretability can inhibit their successful deployment in downstream applications, particularly when differences in environmental conditions may result in unpredictable behaviour or generalisation failures. As a result, there has been a growing emphasis in machine learning around the inclusion of stronger inductive biases in models to improve generalisation. This paper proposes an alternative strategy, inverse value estimation for interpretable policy certificates (IV-Posterior), which seeks to identify the inductive biases or idealised conditions of operation already held by pre-trained policies, and then use this information to guide their deployment. IV-Posterior uses MaskedAutoregressive Flows to fit distributions over the set of conditions or environmental parameters in which a policy is likely to be effective. This distribution can then be used as a policy certificate in downstream applications. We illustrate the use of IV-Posterior across a two environments, and show that substantial performance gains can be obtained when policy selection incorporates knowledge of the inductive biases that these policies hold.
The FEDHC Bayesian network learning algorithm

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, that can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show it is computationally efficient, and produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software \textit{R}.
Simulated Quantum Annealing is Efficient on the Spike Hamiltonian

In this work we study the convergence of a classical algorithm called Simulated Quantum Annealing (SQA) on the Spike Hamiltonian, a specific toy model Hamiltonian for quantum-mechanical tunneling introduced by [FGG02]. This toy model Hamiltonian encodes a simple bit-symmetric cost function f in the computational basis, and is used to emulate local minima in more complex optimization problems. In previous work [CH16] showed that SQA runs in polynomial time in much of the regime of spikes that QA does, pointing to evidence against an exponential speedup through tunneling. In this paper we extend their analysis to the remaining polynomial regime of energy gaps of the spike Hamiltonian, to show that indeed QA presents no exponential speedup with respect to SQA on this family of toy models.
Language-Driven Region Pointer Advancement for Controllable Image Captioning

Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token, motivated by a strong correlation to the sentence structure in the training data. We find that our timing agrees with the ground-truth timing in the Flickr30k Entities test data with a precision of 86.55% and a recall of 97.92%. Our model implementing this technique improves the state-of-the-art on standard captioning metrics while additionally demonstrating a considerably larger effective vocabulary size.
Sim2Real for Self-Supervised Monocular Depth and Segmentation

Image-based learning methods for autonomous vehicle perception tasks require large quantities of labelled, real data in order to properly train without overfitting, which can often be incredibly costly. While leveraging the power of simulated data can potentially aid in mitigating these costs, networks trained in the simulation domain usually fail to perform adequately when applied to images in the real domain. Recent advances in domain adaptation have indicated that a shared latent space assumption can help to bridge the gap between the simulation and real domains, allowing the transference of the predictive capabilities of a network from the simulation domain to the real domain. We demonstrate that a twin VAE-based architecture with a shared latent space and auxiliary decoders is able to bridge the sim2real gap without requiring any paired, ground-truth data in the real domain. Using only paired, ground-truth data in the simulation domain, this architecture has the potential to generate perception tasks such as depth and segmentation maps. We compare this method to networks trained in a supervised manner to indicate the merit of these results.
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification

We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-fine memory matching search procedure for learning and inference. Evaluated on the standard blind test set of the FEVER fact verification dataset, classification accuracy is significantly higher than approaches that only rely on the language model parameters as a knowledge base, and approaches some recent multi-model pipeline systems, using only a single BERT base model augmented with memory layers. We further demonstrate how coupled retrieval and classification can be leveraged to identify low confidence instances, and we extend exemplar auditing to this setting for analyzing and constraining the model. As a result, our approach yields a means of updating language model behavior through two distinct mechanisms: The retrieved information can be updated explicitly, and the model behavior can be modified via the exemplar database.
Resolving code smells in software product line using refactoring and reverse engineering

Software Product Lines SPL are recognized as a successful approach to reuse in software development.Its purpose is to reduce production costs. This approach allows products to be different with respect of particular characteristics and constraints in order to cover different markets. Software Product Line engineering is the production process in product lines.It exploits the commonalities between software products, but also to preserve the ability to vary the functionality between these products.Sometimes, an inappropriate implementation of SPL during this process can conduct to code smells or code anomalies. Code smells are considered as problems in source code which can have an impact on the quality of the derived products of an SPL. The same problem can be present in many derived products from an SPL due to reuse. A possible solution to this problem can be the refactoring which can improve the internal structure of source code without altering external behavior.This paper proposes an approach for building SPL from source code.Its purpose is to reduce code smells in the obtained SPL using refactoring source code.Another part of the approach consists on obtained SPL's design based on reverse engineering.
Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations

Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The Duelling Bandit based exploration provides robust exploration as compared to the state-of-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as com-pared to existing techniques.
Continuous Transition: Improving Sample Efficiency for Continuous  Control Problems via MixUp

Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the poor sample efficiency. Attempting to overcome this shortcoming, several works focus on reusing the collected trajectory data during the training by decomposing them into a set of policy-irrelevant discrete transitions. However, their improvements are somewhat marginal since i) the amount of the transitions is usually small, and ii) the value assignment only happens in the joint states. To address these issues, this paper introduces a concise yet powerful method to construct Continuous Transition, which exploits the trajectory information by exploiting the potential transitions along the trajectory. Specifically, we propose to synthesize new transitions for training by linearly interpolating the consecutive transitions. To keep the constructed transitions authentic, we also develop a discriminator to guide the construction process automatically. Extensive experiments demonstrate that our proposed method achieves a significant improvement in sample efficiency on various complex continuous robotic control problems in MuJoCo and outperforms the advanced model-based / model-free RL methods. The source code is available.
Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality

Building Machine Translation (MT) systems for low-resource languages remains challenging. For many language pairs, parallel data are not widely available, and in such cases MT models do not achieve results comparable to those seen with high-resource languages. When data are scarce, it is of paramount importance to make optimal use of the limited material available. To that end, in this paper we propose employing the same parallel sentences multiple times, only changing the way the words are split each time. For this purpose we use several Byte Pair Encoding models, with various merge operations used in their configuration. In our experiments, we use this technique to expand the available data and improve an MT system involving a low-resource language pair, namely English-Esperanto. As an additional contribution, we made available a set of English-Esperanto parallel data in the literary domain.
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age

The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease diagnosis, clinical trial recruitment, and continuing medical education. This manual search-and-extract process, however, has been hampered by the growth of large-scale clinical databases and the increased prevalence of unlabelled instances. To address this challenge, we propose a supervised contrastive learning framework, CROCS, where representations of cardiac signals associated with a set of patient-specific attributes (e.g., disease class, sex, age) are attracted to learnable embeddings entitled clinical prototypes. We exploit such prototypes for both the clustering and retrieval of unlabelled cardiac signals based on multiple patient attributes. We show that CROCS outperforms the state-of-the-art method, DTC, when clustering and also retrieves relevant cardiac signals from a large database. We also show that clinical prototypes adopt a semantically meaningful arrangement based on patient attributes and thus confer a high degree of interpretability.
Characteristic Polynomials of Simple Ordinary Abelian Varieties over Finite Fields

We provide an easy method for the construction of characteristic polynomials of simple ordinary abelian varieties ${\mathcal A}$ of dimension $g$ over a finite field ${\mathbb F}_q$, when $q\ge 4$ and $2g=\rho^{b-1}(\rho-1)$ for some prime $\rho\ge 5$, with $b\ge 1$. Moreover, we show that ${\mathcal A}$ is absolutely simple if $b=1$ and $\rho=5$, but ${\mathcal A}$ is not absolutely simple for arbitrary $\rho$ with $b>1$.
Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs. Unlike most pose-and-scale methods, gP4Pc is based on constraints arising from the congruence of shapes defined by two sets of four points related by an unknown similarity transformation. By choosing a novel parametrization for the problem, we derive a system of four quadratic equations in four scalar variables. The variables represent the distances of 3D points along the rays from the camera centers. After solving this system via Groebner basis-based automatic polynomial solvers, we compute the similarity transformation using an efficient 3D point-point alignment method. We also propose a specialized variant of our solver for the case of coplanar points, which is computationally very efficient and about 3x faster than the fastest existing solver. Our experiments on real and synthetic datasets, demonstrate that gP4Pc is among the fastest methods in terms of total running time when used within a RANSAC framework, while achieving competitive numerical stability, accuracy, and robustness to noise.
SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking

We propose an object tracking method, SFTrack++, that smoothly learns to preserve the tracked object consistency over space and time dimensions by taking a spectral clustering approach over the graph of pixels from the video, using a fast 3D filtering formulation for finding the principal eigenvector of this graph's adjacency matrix. To better capture complex aspects of the tracked object, we enrich our formulation to multi-channel inputs, which permit different points of view for the same input. The channel inputs are in our experiments, the output of multiple tracking methods. After combining them, instead of relying only on hidden layers representations to predict a good tracking bounding box, we explicitly learn an intermediate, more refined one, namely the segmentation map of the tracked object. This prevents the rough common bounding box approach to introduce noise and distractors in the learning process. We test our method, SFTrack++, on five tracking benchmarks: OTB, UAV, NFS, GOT-10k, and TrackingNet, using five top trackers as input. Our experimental results validate the pre-registered hypothesis. We obtain consistent and robust results, competitive on the three traditional benchmarks (OTB, UAV, NFS) and significantly on top of others (by over $1.1\%$ on accuracy) on GOT-10k and TrackingNet, which are newer, larger, and more varied datasets.