Reviewerly/exHarmony huggingface dataset:
Training neural networks using reinforcement learning to reactive path planning

The mobile robots are devices with great boom given the possibilities that their utilities offer, and to a greater extent, those freelancers who do not require an operator to perform their functions. In order to consolidate the autonomy it is necessary to generate a system of planning of ways that allows a viable route and as far as possible optimal. This study develops a reactive two-dimensional path planning method with neural networks trained under the reinforcement learning method. The complexity of the scenario between the initial and final point is due to warning and forbidden obstacle zones, and the experimentation is carried out on different neural network architectures, each one as an agent of the learning-by-reinforcement algorithm, being these DQN and DDQN types. The best results are obtained with the DDQN training, reaching the objective in 89% in the validation episodes, although the DQN method shows to be 15.63% faster in its success cases. This work was carried out within the research group DIGITI of the Universidad Distrital Francisco José de Caldas.
New quantum codes from cyclic codes over finite chain ring of length 3

Let [Formula: see text] be a commutative finite chain ring with [Formula: see text] is its maximal ideal, [Formula: see text] is its residual field and [Formula: see text] the index of nilpotency of [Formula: see text]. In this paper, we compute the dual code of some types of cyclic codes over [Formula: see text] when [Formula: see text]. Afterward, we show how extract some new quantum codes from these cyclic codes.
Rainbow with Episodic Memory in Deep Reinforcement Learning

Recently, episodic memory based deep reinforcement learning using nonparametric value estimation such as EVA has attracted attention because these methods can improve sample efficiency and converge learning faster. In this paper, we propose a method that combines Rainbow with episodic memory by extending EVA in order for an agent to perform better. We show this method improves an agent performance in terms of both sample efficiency and getting high scores through some experiments on Atari environment.
Evaluation of Quality, Productivity, and Defect by applying Test-Driven Development to perform Unit Tests

Unit testing is part of software testing in which an application split up small parts is known as units. The software development technique also contains test-driven development (TDD) that unit tests are applied to support the development process. The aim of this paper is to evaluate quality, productivity and defect rate on tests by applying test-driven development approach. This expected system establishes the defect-based, user story-based and test-based of regression analysis method to examine the performance effect on quality, productivity, and defect - rate. This expected system's outcomes studied the productivity's effect is good, the effect of quality slightly diminished and the defect rate's effect is quite reduced. Test-Driven Development supports issues to be observed at the earliest, which has the impact of reducing the resolution cost but also the no: of bugs. This approach gives the developer confidence to execute the refactoring and on-going improvement tasks essential to the success of the method.
New Hex Patterns for Fill and Prune

For a position in the game of Hex, a fill pattern is a sub-position with one or more empty cells that can be filled without changing the position's minimax value. A cell is prunable if it can be ignored when searching for a winning move. We introduce two new kinds of Hex fill – mutual and near-dead – and some resulting fill patterns; we show four new permanently-inferior fill patterns; and we present three new prune results, based on strong-reversing, reversing, and game-history respectively. Experiments show these results slightly reducing solving time on 8 $$\,\times \,$$ 8 openings.
CPU Utilization Micro-Benchmarking for RealTime Workload Modeling

The major challenge when migrating platform to satisfy the current and future computing demands is to decide which is the most optimal option for migration. Without actually executing the workload in a platform, it is difficult to know the workload performance in the platform. However, comparing the workload performances between different platforms by testing the workload is very tedious and time consuming. This motivates us to design a modeling framework to predict the workload performance of CPU on different platforms without executing. The challenge for the modeling lies within the collection of highly correlated data to train a predictive model. In this paper, we present a novel CPU utilization (%CPU) micro-benchmarking method to collect the data needed as a vital step before proceeding to training phase.
Impact of Text Distance and Size in AR on User Experience : Focusing on the Implement of Experimental Applications

Augmented reality is drawing attention as a next-generation core technology that will lead to information expansion and productivity growth in various fields. With the commercialization of wearable augmented reality devices approaching, it is imperative to study user experience for the production of high-quality augmented reality content. As the purpose of the augmented reality content is to expand and deliver information about the user experience, a study on readability is needed among the user experience factors. Therefore, in this paper, we find variables that affect the text readability of augmented reality contents, and based on this, we produce experimental design and experimental applications to lay the foundation for presenting user experience guidelines that can be referenced in the development of augmented reality contents through future experiments.
Companion AI for Starbound Game Using Utility Theory

Starbound is a popular video game that has player companions as one of its features, but its AI lacks awareness about the player's state, the enemies' state and its own. This study aims to solve it by implementing a new companion AI which is based on utility theory. The AI was developed, and the user study was conducted to evaluate its ability to assist the player. The fighting ability was assessed by measuring AI's ability to stay alive during the battle and dealing damage to enemies without the player's interaction. As a result, the majority of user study participants did not notice the difference between the built-in AI and the one developed in this study. The participants that noticed the difference enjoyed each type of companion in equal measure. However, the fighting ability test showed that the utility-based AI stays alive longer and deals more damage compared to the built-in agent within the game currently.
Kalman Filter-based Adaptive Forecasting of PV Power Output

In building a forecasting model, weather conditions are typical inputs. However, PV faults happen unexpectedly and affect PV power output significantly. Considering that reliable forecasting is still required in such cases, a forecasting model needs to be adaptive to such PV faults until PV faults are fixed. In pursuing that adaptive forecasting, we utilize Kalman filter together with forecast history. Observing recent forecast history relevant to the current forecast, we try to adjust forecast output by reflecting the observations through Kalman filter. Through experiments using the real-world data, we show that our approach quickly realizes adaptive forecasting just when relevant forecast history is available while the effectiveness of the re-training approach (i.e., one representative existing approach) is limited by available data about PV faults. Our approach (the re-training approach) increases MAPE from 15.46% to 20.27% (33.3%) as the amount of drop of PV power output increases from 0% to 30%.
Network Update Compression for Federated Learning

In federated learning setting, models are trained in a variety of edge-devices with locally generated data and each round only updates in the current model rather than the model itself are sent to the server where they are aggregated to compose an improved model. These edge devices, however, reside in highly uneven nature of network with higher latency and lower-throughput connections and are intermittently available for training. In addition, a network connection has an asymmetric nature of downlink and uplink. All these contribute to a major challenge while synchronizing these updates to the server.In this work, we proposed an efficient coding solution to significantly reduce uplink communication cost by reducing the total number of parameters required for updates. This was achieved by applying Gaussian Mixture Model (GMM) to localize Karhunen-Loève Transform (KLT) on inter-model subspace and representing it with two low-rank matrices. Experiments on convolutional neural network (CNN) models showed the proposed model can significantly reduce the uplink communication cost in federated learning while preserving reasonable accuracy.
Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction

This paper presents new methods for entity recognition and relation extraction tasks on partially labeled and unlabeled datasets. The proposed methods are based on techniques of semi-supervised, unsupervised and the transfer learning. We use the few-shot learning technique to construct specific algorithms for the new data sources without manual retraining. To compare the results with other studies, we conducted experiments on two benchmark datasets for the Russian language. The results for named entity recognition demonstrate significant improvement and outperform the state-of-the-art results. Our results for relation extraction are comparable to other research. We assume that a longer BERT fine-tuning will help to improve them, and we also plan to experiment with other few-shot learning methods in the near future.
CalibreNet: Calibration Networks for Multilingual Sequence Labeling

Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC). One major obstacle is the errors on the boundary of predicted answers. To tackle this problem, we propose CalibreNet, which predicts answers in two steps. In the first step, any existing sequence labeling method can be adopted as a base model to generate an initial answer. In the second step, CalibreNet refines the boundary of the initial answer. To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet. Experiments on two cross-lingual benchmark datasets show that the proposed approach achieves SOTA results on zero-shot cross-lingual NER and MRC tasks.
Transparent Provable Data Possession Scheme for Cloud Storage

Provable Data Possession (PDP) is one of the data security techniques to make sure that the data stored in the cloud storage exists. In PDP, the integrity of the data stored in the cloud storage is probabilistically verified by the user or a third-party auditor. In the conventional PDP, the user creates the metadata used for audition. From the viewpoint of user convenience, it is desirable to be able to audit without operations other than uploading. In other words, the challenge is to provide a transparent PDP that verifies the integrity of files according to the general cloud storage system model so as not to add operations to users. We propose a scheme in which the cloud generates the metadata used during verification, and the user only uploads files. It is shown that the proposed scheme is resistant to the forgery of cloud proof and the acquisition of data by a third-party auditor.
Weight-Based Exploration for Unmanned Aerial Teams Searching for Multiple Survivors

During floods, reaching survivors in the shortest possible time is a priority for rescue teams. Given their ability to explore difficult terrain in short spans of time, Unmanned Aerial Vehicles (UAVs) have become an increasingly valuable aid to search and rescue operations. Traditionally, UAVs utilize exhaustive lawnmower exploration patterns to locate stranded survivors, without any information regarding the survivor's whereabouts. In real life disaster scenarios however, on-ground observers provide valuable information to the rescue effort, such as the survivor's last known location and heading. In earlier work, a Weight Based Exploration (WBE) model, which utilizes this information to generate a prioritized list of waypoints to aid the UAV in its search mission, was proposed. This approach was shown to be effective for a single UAV locating a single survivor. In this paper, we extend the WBE model to a team of UAVs locating multiple survivors. The model initially partitions the search environment amongst the UAVs using Voronoi cells. The UAVs then utilize the WBE model to locate survivors in their partitions. We test this model with varying survivor locations and headings. We demonstrate the scalability of the model developed by testing the model with aerial teams comprising several UAVs.
Analyzing Machine‐Learned Representations: A Natural Language Case Study

Abstract As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero‐shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human‐like language understanding.
Sequential Recommendation with a Pre-trained Module Learning Multi-modal Information

Recommendation has already attracted increased attention in various industries, including community of scholars. Especially sequential recommendation considering time series information is improved to satisfy sequential user's needs better. Most efforts have been made in capturing users' dynamic preferences, however, the multi-modal information of items will also contribute to making recommendation. A user may be interested in a pair of shoes because of its color and style, or because of its function that is suitable for dating, so multi-modal(image, text) contains different aspects of information for items. A new three-stream framework is designed to capture user's different aspect preferences using the self-attention mechanism. The image-stream will capture user's interests about what an item looks like, the description-stream will capture user's preference about the function of an item, and the item-stream will capture the sequential preference. The key point in our method lies in that a pre-trained item presentation is utilized which has learned information from multi-modalities. Experiments are conducted by us on public datasets and the results prove that our model obtains good performance improvement over most existing sequential recommendation models.
The American Way — Until Machine Learning Algorithm Beats the Law? Algorithmic Consumer Credit Scoring in the EU and US

Algorithmic consumer credit scoring has caused anxiety among scholars and policy makers. After a significant legislative effort by the European Union, the General Data Protection Regulation (GDPR) that has provisions tailored to automated decision-making (ADM) was implemented. When the EU Commission and the US Department of Commerce negotiated for US organizations to whom data from EU data controller is transferred to comply with the key principles of EU Data Protection Law under the EU-US Privacy Shield (PS) Framework, the Department of Commerce refused to incorporate the GDPR principles governing ADM in the PS Framework. The EU Commission accepted this refusal reasoning that where US companies make automated decisions with respect to EU data subjects, such as in consumer credit risk scoring, there are laws in the US that protect the consumer from adverse decisions. This view contradicts recommendations for implementing GDPR-Inspired law in the US to tackle the challenges of automated consumer credit scoring.This article argues that despite the differences in the approach to the regulation of automated consumer credit scoring in the EU and the US, consumers are similarly protected in both jurisdictions. Furthermore, US consumer credit laws have the necessary flexibility to ensure that adverse automated decisions are tackled effectively. This article, through analyzing statutes, cases, and empirical evidence, demonstrates that the seemingly comprehensive legal rules governing ADM in the GDPR do not make the EU consumers better off. In addition, the challenges presented by the increasing sophistication of Artificial Intelligence (AI), especially machine learning, place both the EU and the US legal regimes in similar position as neither jurisdiction is equipped to respond to autonomous, unpredictable, and unexplainable algorithms making decisions. While the EU’s risk-based approach to AI regulation adopted by the Draft AI Regulation which also contains provisions on regulatory sandboxing is a significant improvement, it does not significantly change the rules regarding algorithmic consumer credit scoring. Nevertheless, this is the approach that regulation should primarily adopt for the future.
Pilaster: A Collection of Citation Metadata Extracted From Publications on Visualization for the Digital Humanities

In this paper, we present Pilaster (https://visusal.github.io/pilaster/), a collection of citation metadata extracted from publications in visualization for the digital humanities. The collection is generated from a seed set of relevant publications from which we extracted cited works, including journal and conference papers, books, theses, or blog posts, among other resources. The main aim of this work revolves around three main points: first, the collection may serve as an entry point to the discipline for digital humanists and visualization scholars without previous experience in the field. Second, Pilaster can be regarded as a meeting point for more established visualization or humanities scholars seeking to collaborate in the development of novel research ideas and related visualization design studies in the context of the humanities. Third, and given the large amount of visualization design spaces that were captured, we believe the dataset has the potential to become the starting point for future studies aimed at understanding the particularities of problem-driven visualization research in this and other contexts.
Detection Algorithm of the Mimicry Attack and Its Implementation in the ZigBee Platform

The broadcast characteristic of wireless network makes the secure access of device face various threat. Among them, the mimicry attack is a kind of fraud attack in which the malicious users imitating the MAC (Media Access Control) address or other identification credentials of the legitimate user. To cope with it, this paper proposes a mimicry attack detection method based on the received signal strengths (RSSs) of multiple receivers. Different from the MAC address and other identification credentials, RSS is difficult to forge as it is related to the physical location of the transmitter. By analyzing the RSS vector of the target device, the system can detect the identity change of the access device without relying on the content of the packet, and thereby detect the mimicry attack. The experiment results on the implemented ZigBee platform show that the farther the distance between malicious devices and legitimate devices, the slower the movement speed transmitter, the higher the recognition rate of the proposed method. When four ZigBee devices are used as receivers, and the distance between the malicious device and the legitimate device is more than 5 meters, the detection rate of the proposed method is more than 92%. This method can be used as a powerful supplement to the traditional authentication system based on MAC address to further enhance the security of wireless communication.
Design and Implementation of Multi-Dimensional Dynamic Precise Recommendation Algorithms for Mobile E-Commerce Platform

The multi-dimensional dynamic accurate recommendation algorithm of mobile e-commerce platform is a recommendation algorithm based on "location + item + time". Through the filtering and filtering of the three most critical modules of location filtering, user filtering and time filtering, the final project with the highest overall score is recommended to users timely, appropriately, dynamically and intelligently. In the experiment, comparing the algorithm with item neighbor model and simple model, it is found that the RMSE value of multidimensional dynamic accurate recommendation algori-thm is 0.7642 minimum, the most accurate and the error is also minimum. Its accuracy, multidimensional, dynamic and noise reduction have been further improved and developed.
Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical stateaction data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.
Low-Memory Implementation of Authenticated Encryption Algorithm SAEAES on ARM Cortex-M0 Microcontroller

SAEAES is a lightweight authenticated encryption algorithm and is a candidate of the NIST's competition for choosing a new standard. Memory footprint is a key performance metric of lightweight cryptography in software implementation on resource-constrained embedded devices. We present the first low-memory implementation of SAEAES on the ARM Cortex-M microcontroller. Our ROM- and RAM-oriented SAES implementations achieved (ROM: 586, RAM: 129 bytes) and (ROM: 608, RAM: 112 bytes). We compare these performances with the conventional implementations including the one optimized for the RL78 microcontroller.