Reviewerly/exHarmony huggingface dataset:
Smooth Curve Fitting of Mobile Robot Trajectories Using Differential Evolution

Mobile robots have recently attracted the attention and applicability in field areas ubiquitously. Within the context of autonomous navigation, path planning is relevant for comfortability, safety, execution time and energy savings. In this paper, we propose an approach to suggest smooth paths from observed robot trajectories by optimizing fitting and smoothness criteria using Differential Evolution with distinct modes of initialization, selection pressure, exploration and exploitation. Our rigorous computational experiments using a relevant set of real-world robot trajectories from the Boe-Bot mobile robot architecture show the feasibility and efficiency of our approach in computing smooth curves, suggesting the superior performance of the greedy initialization scheme based on the triangular convex hull of the robot trajectory, and Differential Evolution with exploitative and parameter adaptation schemes such as Rank-Based Differential Evolution (RBDE), Adaptive Differential Evolution with External Archive (JADE) and Strategy Adaptation Differential Evolution (SADE). Our obtained results offer the building blocks to further advance towards developing data-driven curve fitting and path planning algorithms, which may find use in several real-world applications in Robotics and Operations Research.
FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.
An Improved Tobit Kalman Filter with Adaptive Censoring Limits

This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman filter residual. The designed algorithm is tested using both synthetic and real data sets. The real data set includes human skeleton joints’ coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain real-life situations that cause problems in human skeleton tracking, such as (self)-occlusions, closely interacting persons, etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall root-mean-square error for synthetic and real data sets.
SACCOS: A Semi-Supervised Framework for Emerging Class Detection and Concept Drift Adaption Over Data Streams

In this paper, we address challenges of detecting instances from emerging classes over a non-stationary data stream during data classification. In particular, data instances from an entirely unknown class may appear in a data stream over time. Existing classification techniques utilize unsupervised clustering to identify emergence of such data instances. Unfortunately, they make strong assumptions which are typically invalid in practice; (i) Most instances associated with a class are closer to each other in feature space than instances associated with different classes, (ii) Covariates of data are normalized through an oracle to overcome the effect of a few data instances having large feature values, and (iii) Labels of instances from emerging classes are readily available soon after detection. To address the challenges that occur in practice when the above assumptions are weak, i.e., instances of each class are scattered and the true labels of novel class instances are sparsely available, we propose a practical semi-supervised emerging class detection framework. Particularly, we aim to identify similar data instances within local regions in feature space by incorporating a mutual graph clustering mechanism. We also perform online normalization along the data stream instead of assuming an oracle, and propose a classification technique that uses only a small amount of true labels for training and emerging class detection. Our empirical evaluation of this framework on real-world datasets demonstrates its superiority of classification performance compared to existing methods, while using significantly fewer labeled instances.
Public-Key Puncturable Encryption: Modular and Compact Constructions

We revisit the method of designing public-key puncturable encryption schemes and present a generic conversion by leveraging the techniques of distributed key-distribution and revocable encryption. In particular, we first introduce a refined version of identity-based revocable encryption, named key-homomorphic identity-based revocable key encapsulation mechanism with extended correctness. Then, we propose a generic construction of puncturable key encapsulation mechanism from the former by merging the idea of distributed key-distribution. Compared to the state-of-the-art, our generic construction supports unbounded number of punctures and multiple tags per message, thus achieving more fine-grained revocation of decryption capability. Further, it does not rely on random oracles, not suffer from non-negligible correctness error, and results in a variety of efficient schemes with distinct features. More precisely, we obtain the first scheme with very compact ciphertexts in the standard model, and the first scheme with support for both unbounded size of tags per ciphertext and unbounded punctures as well as constant-time puncture operation. Moreover, we get a comparable scheme proven secure under the standard DBDH assumption, which enjoys both faster encryption and decryption than previous works based on the same assumption, especially when the number of tags associated with the ciphertext is large.
Data Augmentation in Training Deep Learning Models for Medical Image Analysis

Data augmentation is widely utilized to achieve more generalizable and accurate deep learning models based on relatively small labeled datasets. Data augmentation techniques are particularly critical in medical applications, where access to labeled data samples is commonly limited. Although data augmentation methods generally have a positive impact on the performance of deep learning models, not all data augmentation techniques are applicable and suitable for analyzing medical images. In this chapter, we review common image augmentation techniques and their properties. Furthermore, we present and evaluate application-specific data augmentation methods that are beneficial for medical image analysis. The material presented in this chapter aims to guide the use of data augmentation techniques in training deep learning models for various medical image analysis applications, in which annotated data are not abundant or are difficult to acquire.
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features

Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. The code of this model will be publicly available at <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey

The accurate modelling and prediction of air temperature values is an exceptionally important meteorological variable that affects in many areas. The present study is aimed at developing models for the prediction of monthly mean air temperature values in Turkey using ANN, ANFIS and SVMr methods. In developing the models, the monthly data derived from eight stations of the TSMS for the 1963–2015 period were used, including latitude, longitude, elevation, month, and minimum, maximum and mean air temperatures. The performances of the ANN, ANFIS and SVMr models were compared using R2, MSE, MAPE and RRMSE. In order to verify the differences between the predicted temperature values provided by the ANN, ANFIS and SVMr models and the observed temperature values derived from the stations, a t-test analysis was conducted, and the best ANN, ANFIS and SVMr models were determined according to the statistical performance values. These models were then used to make air temperature predictions for the cities. Manova was carried out to determine the effects of the differences temperature predictions and RRMSE values of the models. Generally, the statistical performance values of the ANFIS models were found to be slightly better than those of the ANN and SVMr models.
Software maintainability prediction using an enhanced random forest algorithm

Abstract Abstract Now-a-days, software maintainability has become one of the significant quality-assessing attributes for any software system. Current study presents an enhanced-RFA (Random Forest Algorithm) approach for Software Maintainability Prediction. The proposed approach combines Random Forest (RF) algorithm with three prevalent feature selection techniques namely Chi-Squared, RF and Linear Correlation Filter along with a re-sampling technique intended to improve the prediction accuracy of basic RF algorithm. Enhanced- RFA is applied on two commercially available datasets, QUES and UIMS and performance is evaluated on the basis of R2. Results indicate that the proposed approach performs significantly better than RFA for both the datasets with an improvement in R2 values equal to 69.50%, 65.57% & 69.40% for QUES and 31.90%, 44.94% & 51.81% for UIMS using chisquared, RF and linear correlation filter techniques respectively. Subject Classification: Primary 68N30Secondary 68T01Keywords: Maintainability PredictionMachine LearningEnhanced-RFAFeature Selection
Disentangled Inference for GANs With Latently Invertible Autoencoder

Generative Adversarial Networks (GANs) can synthesize more and more realistic images. However, one fundamental issue hinders their practical applications: the incapability of encoding real samples in the latent space. Many semantic image editing applications rely on inverting the given image into the latent space and then manipulating inverted code. One possible solution is to learn an encoder for GAN via Variational Auto-Encoder. However, the entanglement of the latent space poses a major challenge for learning the encoder. To tackle the challenge and enable inference in GANs, we propose a novel method named Latently Invertible Autoencoder (LIA). In LIA, an invertible network and its inverse mapping are symmetrically embedded in the latent space of an autoencoder. The decoder of LIA is first trained as a standard GAN with the invertible network, and then the encoder is learned from a disentangled autoencoder by detaching the invertible network from LIA. It thus avoids the entanglement problem caused by the latent space. Extensive experiments on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for the image inversion and its applications. Code and models are available at https://github.com/genforce/lia .
Structure-augmented knowledge graph embedding for sparse data with rule learning

In recent years, knowledge graphs have received considerable attention because of their ability to express rich information and their potential for use in knowledge-based reasoning. For example, they can assist in-depth knowledge discovery related to user associations, switching policies, and traffic content in mobile services. Knowledge graph embeddings project the entities and relations of knowledge graphs into vectors that are dense and low dimensional, thus allowing the complex semantic information and relations between these entities to be measured efficiently. However, traditional knowledge graph embedding methods consider only direct facts, making it difficult to achieve reasonable embedding learning of entities and relations when faced with sparse data. To settle this issue, this paper proposes a novel knowledge graph embedding method based on tensor decomposition combined with rule learning. First, rules are inferred and scored based on the initial embeddings of entities and relations. Then, new triples for sparse entities are inferred from rules with high scores. Finally, these new triples are iteratively embedded into the model. Experimental results obtained on the WN18 and FB15k datasets indicate that the proposed model achieves significantly better performance than other state-of-the-art knowledge graph embedding models when faced with sparse data.
QuantQueryEXP : A novel strategic approach for query expansion based on quantum computing principles

In our current world, numerous innovative approaches aimed at expansion and recommendation of queries are developing to satisfy the mounting tendencies of social networking and the internet. Many effectual procedures have been developed to achieve query expansion. Additionally, the creation of quantum-based procedures is now becoming an important task. Quantum based algorithms are the way to secure the future of computation by preparing for the advent of quantum computers. QuantQueryEXP implements an eminent technique, the Deutsch-Jozsa [1] algorithm, in an altered manner, delivering a substitute for expansion and recommendation of queries implementing quantum techniques which offers much higher accuracy with optimization in complexity over any conventional algorithm to achieve supremacy in quantum computing. QuantQueryEXP attains an accuracy of 90.43% along with great optimization of resource and temporal complexity.
Deep Adaptive Wavelet Network

Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks. The Code implemented for this research is available at https://github.com/mxbastidasr/DAWN_WACV2020.
Chaos-based image hybrid encryption algorithm using key stretching and hash feedback

The paper proposes a chaos-based image hybrid stream and block encryption algorithm, a key stretching method is implemented to further enhance its security. The random time span sequence and salt sequences are generated by the Logistic-Sine map. The key stretching method repeatedly feeds salt and current intermediate hash value of state variables back into the next round message for hash function simultaneously, and the state variables are extracted from Sprott C system through time span sequence, the aim of redundant iterations of Sprott C system is to resist side channel attack. After multiple iteration loops, the key stream is generated by Sprott C system to encrypt pixel blocks of the pre-encoded image. Experimental results and security analysis demonstrated the scheme’s effectiveness and resistance to all kinds of known attacks.
METS VR: Mining Evacuation Training Simulator in Virtual Reality for Underground Mines

The mining industry is one of America’s most important suppliers of raw materials such as metals for the manufacturing, construction and high-tech industry. It is very profitable, but requires the mine to be fully active. Mining is also a comparably dangerous line of work and lives can be lost on the work site due to an emergency. As of right now, mines will generally conduct quarterly training to go over evacuation drills and how to handle emergency situations. However, the mine needs to halt production during drills, which is a major disadvantage of this training method. This in turn becomes a loss of profit for the mine. With the Mining Evacuation Training Simulator (METS), the mining industry will have a virtual training scenario for their workers. A more immersive experience is critical to the quality of training as the teaching content actualizes the real world experience. METS is a cost efficient training and safe alternative to the current model of evacuation training. With METS the mine will still be able to remain in production while a few employees at a time perform evacuation and emergency training.
Academic Dishonesty Prevention in E-learning University System

This article shows the ways of prevention academic dishonesty in e-learning. We reveal this phenomenon as a complex social notion in the context of the sociology of communication and the Internet and educational data mining. The main categories of academic dishonesty are considered in the paper: cheating, plagiarism and collusion. Cheating is shown in details because it is associated with the tests during the e-learning course. The authors carried out a substantial analysis of academic dishonesty in e-learning and it’s prevention using more than 50 examples covering the experience of various countries and higher education systems. The most interesting case shows measures preventing extension for Google Chrome PolyTestHelper. The main conclusion of the study proves the priority of preventive measures. It is obvious that the problem of cheating cannot be solved purely technically, since a technical war can last forever. At the present stage of information and communication technologies widespread usage a situation of prohibition or significant restrictions in e-learning environment may seem contrived to students. False results of anti-cheat programs can seriously undermine the trust between students and teachers. The ideal way to prevent academic dishonesty is to perform tasks including creative academic elements. Nevertheless, this way is not universal because these are mass courses with a regular enrollment of several thousand students, with a fully automatic verification of academic results.
Prediction of Likes and Retweets Using Text Information Retrieval

Twitter is one of the major social media platforms today to study human behaviours by analysing their interactions. To ensure popularity of the tweet, the focus should be on the content of the tweet that results in numerous followings of that message with sufficient number of likes and retweets. The high quality of tweets, increases the online reputation of the users who post it. If a user can get the prediction of likes and retweets on his text before posting it on the internet, it would improve the popularity of the tweet from information sharing perspective. In this paper we employed different machine learning classifiers like SVM, Naïve Bayes, Logistic Regression, Random Forest, and Neural Network, on top of two different text processing approaches used in NLP (natural language processing), namely bag-of-words (TFIDF) and word embeddings (Doc2Vec), to check how many likes and retweets can a tweet generate. The results obtained indicate that all the models performed 10-15% better with the bag-of-word technique.
Precise Landing of Autonomous Aerial Vehicles Using Vector Fields

This letter proposes a strategy for autonomous precision landing of aerial vehicles in fixed pads or runways. Our approach is based on a velocity vector field, which is a closed-loop strategy that guides the vehicle towards the landing point and simultaneously enforces the shape of its trajectory. Relative position between the vehicle and the landing zone, which can be easily determined using visual sensors installed on-board, is the only information necessary to perform the landing. We present vector fields that can be used to land both fixed- and rotary-wing autonomous aircrafts. We also show experiments and simulations with a quadrotor equipped with a monocular camera to illustrate the characteristics of the proposed approach.