Reviewerly/exHarmony huggingface dataset:
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions

Genetic programming (GP) is a general purpose bio-inspired meta-heuristic for the evolution of computer programs. In contrast to the several successful applications, there is little understanding of the working principles behind GP. In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (ANDn). The analysis of a random local search GP system with minimal terminal and function sets reveals the relationship between the number of iterations and the progress the GP makes toward finding the target function. Afterwards we consider a more realistic GP system equipped with a global mutation operator and prove that it can efficiently solve ANDn by producing programs of linear size that fit a training set to optimality and with high probability generalise well. Additionally, we consider more general problems which extend the terminal set with undesired variables or negated variables. In the presence of undesired variables, we prove that, if non-strict selection is used, then the algorithm fits the complete training set efficiently while the strict selection algorithm may fail with high probability unless the substitution operator is switched off. If negations are allowed, we show that while the algorithms fail to fit the complete training set, the constructed solutions generalise well. Finally, from a problem hardness perspective, we reveal the existence of small training sets that allow the evolution of the exact conjunctions even with access to negations or undesired variables.
Homomorphic Encryption and Data Security in the Cloud

In the recent times, the use of cloud computing has gained popularity all over the world. There are lots of benefits associated with the use of this modern technology, however, there is a concern about the security of information during computation. A homomorphic encryption scheme provides a mechanism whereby arithmetic operation on the ciphertexts produces the same result as the arithmetic operation on plaintexts. Concept of homomorphic encryption (HME) is discussed with reviews, applications and future challenges to this promising field of research
Reminder Care System: An Activity-Aware Cross-Device Recommendation System

Alzheimer’s disease (AD) affects large numbers of elderly people worldwide and represents a significant social and economic burden on society, particularly in relation to the need for long term care facilities. These costs can be reduced by enabling people with AD to live independently at home for a longer time. The use of recommendation systems for the Internet of Things (IoT) in the context of smart homes can contribute to this goal. In this paper, we present the Reminder Care System (RCS), a research prototype of a recommendation system for the IoT for elderly people with cognitive disabilities. RCS exploits daily activities that are captured and learned from IoT devices to provide personalised recommendations. The experimental results indicate that RCS can inform the development of real-world IoT applications.
Comprehensive Multi-Dataset Evaluation of Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering research in natural language understanding.A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.Given the availability of many such datasets, comprehensive and reliable evaluation is tedious and time-consuming for researchers working on this problem.We present an evaluation server, ORB, that reports performance on seven diverse reading comprehension datasets, encouraging and facilitating testing a single model's capability in understanding a wide variety of reading phenomena.The evaluation server places no restrictions on how models are trained, so it is a suitable test bed for exploring training paradigms and representation learning for general reading facility.As more suitable datasets are released, they will be added to the evaluation server.We also collect and include synthetic augmentations for these datasets, testing how well models can handle out-of-domain questions.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus.Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve answer passages, i.e., the passages containing the groundtruth answers.However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question.To resolve this issue, we introduce a new subproblem of open-domain multi-hop QA, which aims to recognize the bridge (i.e., the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.This model, the bridge reasoner, is trained with a weakly supervised signal and produces the candidate answer passages for the passage reader to extract the answer.On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1.Without using any memoryinefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules.
Fusion Despeckling Based on Surface Variation Anisotropic Diffusion Filter and Ratio Image Filter

This article proposes a novel fusing filter algorithm based on a surface variation anisotropic diffusion (SVAD) filter and a ratio image filter to achieve good speckle reduction and edge preservation. The proposed algorithm can be divided into three steps. First, the proposed SVAD filter effectively calculates the diffusion coefficient of each pixel to obtain filtering results on different scales. Second, the proposed ratio image filter obtains a new denoising result that can effectively recover some details lost with the SVAD filter. Then, the two filtering results are fused to obtain the final despeckling result. Furthermore, the effects of the weighting coefficients of the fusion processing and the number of iterations of the ratio image filter on the final filtering results are analyzed. The proposed algorithm is effectively evaluated by conducting some experiments on the added noise image and real synthetic aperture radar (SAR) images. The experimental results confirm that the proposed method can not only significantly reduce speckle but also effectively preserve the edge information of images.
Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report

This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019. The tasks consisted of two machine comprehension evaluations, each of which tested a system’s ability to answer questions/queries about a text. Both evaluations were designed such that systems need to exploit commonsense knowledge, for example, in the form of inferences over information that is available in the common ground but not necessarily mentioned in the text. A total of five participating teams submitted systems for the shared tasks, with the best submitted system achieving 90.6% accuracy and 83.7% F1-score on task 1 and task 2, respectively.
Development of an Image Grating Sensor for Position Measurement

In this research paper, a precision position-measurement system based on the image grating technique is presented. The system offers a better robustness and flexibility for 1D position measurement compared to a conventional optical encoder. It is equipped with an image grating attached to a linear stage as the target feature and a line scan camera as the stationary displacement reader. By measuring the position of the specific feature in the image and applying a subpixel image registration method, the position of the linear stage can be obtained. In order to improve the computational efficiency, the calculations for pattern correlation and subpixel registration are performed in the frequency domain. An error compensation method based on a lens distortion model is investigated and implemented to improve the measurement accuracy of the proposed system. Experimental data confirms the capability of the developed image grating system as ±0.3&nbsp;µm measurement accuracy within a 50 mm range and ±0.2 µm measurement accuracy within a 25 mm range. By applying different optics, the standoff distance, measurement range, and resolution can be customized to conform to different precision measurement applications.
Question Answering for Fact-Checking

Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural languages tasks such as question-answering, natural language inference (NLI), and textual entailment.These tasks not only require the contextual knowledge but also the reasoning abilities to be solved efficiently.In this paper, we propose an unsupervised question-answering based approach for a similar task, fact-checking.We transform the FEVER dataset into a Clozetask by masking named entities provided in the claims.To predict the answer token, we utilize pre-trained Bidirectional Encoder Representations from Transformers (BERT).The classifier computes label based on the correctly answered questions and a threshold.Currently, the classifier is able to classify the claims as "SUPPORTS" and "MANUAL REVIEW".This approach achieves a label accuracy of 80.2% on the development set and 80.25% on the test set of the transformed dataset.
A Cyclic Attribution Technique Feature Selection Method for Human Activity Recognition

Feature selection is a technique of selecting the most important features for predictive model construction.It is a key component in machine learning for many pattern recognition applications.The primary objective of this paper is to create a more precise system for Human Activity Recognition (HAR) by identifying the most appropriate features.We propose a Cyclic Attribution Technique (CAT) feature selection technique for recognition of human activity based on group theory and the fundamental properties of the cyclic group.We tested our model on UCI-HAR dataset focusing on six activities.With the proposed method, 561 features could be reduced to 63.Using an Artificial Neural Network (ANN), we compared performances of our new dataset with selected features and the original dataset classifier.Results showed that the model could provide an excellent overall accuracy of 96.7%.The proposed CAT technique can specify high-quality features to the success of HAR with ANN classifier.Two benefits support this technique by reducing classification overfitting and training time.
Synchronization in Interpersonal Speech

During both positive and negative dyadic exchanges, individuals will often unconsciously imitate their partner. A substantial amount of research has been made on this phenomenon and such studies have shown that synchronisation between communication partners can improve interpersonal relationships. Automatic computational approaches for recognising synchrony are still in their infancy. In this study, we extend on previous work in which we applied a novel method utilising hand-crafted low-level acoustic descriptors and autoencoders (AEs) to analyse synchrony in the speech domain. For this purpose, a database consisting of 394 in-the-wild speakers from 6 different cultures, is used. For each speaker in the dyadic exchange, two AEs are implemented. Post the training phase, the acoustic features for one of the speakers is tested using the AE trained on their dyadic partner. In this same way, we also explore the benefits that deep representations from audio may have, implementing the state-of-the-art Deep Spectrum toolkit. For all speakers at varied time-points during their interaction, the calculation of reconstruction error from the AE trained on their respective dyadic partner is made. The results obtained from this acoustic analysis are then compared with the linguistic experiments based on word counts and word embeddings generated by our word2vec approach. The results demonstrate that there is a degree of synchrony during all interactions. We also find that, this degree varies across the 6 cultures found in the investigated database. These findings are further substantiated through the use of 4096 dimensional Deep Spectrum features.
A Performance Comparison of Deep Learning Methods for Real-time Localisation of Vehicle Lights in Video Frames

A vehicle's braking lights can help to infer its future trajectory. Visible light communication using vehicle lights can also transmit other safety information to assist drivers with collision avoidance (whether the drivers be human or autonomous). Both these use cases require accurate localisation of vehicle lights by computer vision. Due to the large variation in lighting conditions (day, night, fog, snow, etc), the shape and brightness of the light itself, as well as difficulties with occlusions and perspectives, conventional methods are challenging and deep learning is a promising strategy. This paper presents a comparison of deep learning methods which are selected based on their potential to evaluate real-time video. The detection accuracy is shown to have a strong dependence on the size of the vehicle light within the image. A cascading approach is taken, where a downsampled image is used to detect vehicles, and then a second routine searches for vehicle lights at higher resolution within these Regions of Interest. This approach is demonstrated to improve detection, especially for small objects. Using YOLOv3 for the first stage and Tiny_YOLO for the second stage achieves satisfactory results across a wide range of conditions, and can execute at 37 frames per second. The ground truth for training and evaluating the methods is available for other researchers to use and compare their results.
Learning Structured Sparse Representations for Voice Conversion

Sparse-coding techniques for voice conversion assume that an utterance can be decomposed into a sparse code that only carries linguistic contents, and a dictionary of atoms that captures the speakers' characteristics. However, conventional dictionary-construction and sparse-coding algorithms rarely meet this assumption. The result is that the sparse code is no longer speaker-independent, which leads to lower voice-conversion performance. In this paper, we propose a Cluster-Structured Sparse Representation (CSSR) that improves the speaker independence of the representations. CSSR consists of two complementary components: a Cluster-Structured Dictionary Learning module that groups atoms in the dictionary into clusters, and a Cluster-Selective Objective Function that encourages each speech frame to be represented by atoms from a small number of clusters. We conducted four experiments on the CMU ARCTIC corpus to evaluate the proposed method. In a first ablation study, results show that each of the two CSSR components enhances speaker independence, and that combining both components leads to further improvements. In a second experiment, we find that CSSR uses increasingly larger dictionaries more efficiently than phoneme-based representations by allowing finer-grained decompositions of speech sounds. In a third experiment, results from objective and subjective measurements show that CSSR outperforms prior voice-conversion methods, improving the acoustic quality of the synthesized speech while retaining the target speaker's voice identity. Finally, we show that the CSSR captures latent (i.e., phonetic) information in the speech signal.
Deep Hashing Based on VAE‐GAN for Efficient Similarity Retrieval

Inspired by the recent advances in generative networks, we propose a VAE-GAN based hashing framework for fast image retrieval. The method combines a Variational autoencoder (VAE) with a Generative adversarial network (GAN) to generate content preserving images for pairwise hashing learning. By accepting real image and systhesized image in a pairwise form, a semantic perserving feature mapping model is learned under a adversarial generative process. Each image feature vector in the pairwise is converted to a hash codes, which are used in a pairwise ranking loss that aims to preserve relative similarities on images. Extensive experiments on several benchmark datasets demonstrate that the proposed method shows substantial improvement over the state-of-the-art hashing methods.
An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.
FSNet: A Target Detection Algorithm Based on a Fusion Shared Network

Currently, in a target detection framework, a feature pyramid is widely used to capture differences resulting from the change in size of a detected object. Examples include the one-stage detector, the Deconvolutional Single-Shot Detector (DSSD), the RefineDet and the two-stage detector DetNet. Although these target detection frameworks with characteristic pyramid structure have achieved good results, they are limited by the facts that 1) they only use one feature layer of multi-scale information for prediction, and 2) they do not fully incorporate multi-scale inclusion of feature information. Therefore, based on Single-shot detector (SSD) architecture, we propose a novel one-stage target detector framework-fusion shared network (FSNet) which makes full use of feature information between multiple scale feature maps to more effectively detect objects of different scales. First, we introduce a fusion attention mechanism to fuse features between feature layers of different scales. Secondly, we combine this with multi-head structure fusion multi-layer features extracted as new features by a backbone network. Finally, we feed multi-layer fusion features into shared prediction module (shared PM). In the module, a new class feature pyramid for target detection is formed wherein the feature map used for prediction is composed of multiple levels of feature layers. In order to verify the effectiveness of this new FSNet target detection algorithm, we used the 320X320 input for FSNet, obtaining 80.7% mean accuracy (mAP) and 15 frames per second (Frame Per Second, FPS) in the PASCAL VOC 2007 test.
A Template-Based Method for the Generation of Attack Trees

Attack trees are used in cybersecurity analysis to give an analyst a view of all the ways in which an attack can be carried out. Attack trees can become large, and developing them by hand can be tedious and error-prone. In this paper the automated generation of attack trees is considered. The method proposed is based on a library of attack templates – parameterisable patterns of attacks such as denial of service or eavesdropping – and that also uses an abstract model of the network architecture under attack. A pseudocode implementation of the method is also presented. The example application given is from the automotive domain and using an architecture consisting of linked CAN networks – a network configuration found in virtually every current vehicle.
Iterative energy-based projection on a normal data manifold for anomaly localization

Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its autoencoder reconstruction. In practice however, local defects added to a normal image can deteriorate the whole reconstruction, making this segmentation challenging. To tackle the issue, we propose in this paper a new approach for projecting anomalous data on a autoencoder-learned normal data manifold, by using gradient descent on an energy derived from the autoencoder's loss function. This energy can be augmented with regularization terms that model priors on what constitutes the user-defined optimal projection. By iteratively updating the input of the autoencoder, we bypass the loss of high-frequency information caused by the autoencoder bottleneck. This allows to produce images of higher quality than classic reconstructions. Our method achieves state-of-the-art results on various anomaly localization datasets. It also shows promising results at an inpainting task on the CelebA dataset.
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large. In this paper, we introduce Evolutionary Population Curriculum (EPC), a curriculum learning paradigm that scales up Multi-Agent Reinforcement Learning (MARL) by progressively increasing the population of training agents in a stage-wise manner. Furthermore, EPC uses an evolutionary approach to fix an objective misalignment issue throughout the curriculum: agents successfully trained in an early stage with a small population are not necessarily the best candidates for adapting to later stages with scaled populations. Concretely, EPC maintains multiple sets of agents in each stage, performs mix-and-match and fine-tuning over these sets and promotes the sets of agents with the best adaptability to the next stage. We implement EPC on a popular MARL algorithm, MADDPG, and empirically show that our approach consistently outperforms baselines by a large margin as the number of agents grows exponentially.
Image Denoising Algorithm Based on Nonlocal Regularization Sparse Representation

Image denoising is essential for subsequent image processing and application, and it ensures that people can obtain valid information of images more accurately. The key to image denoising is how to preserve the structure and detail information in the original image while removing noise. By the further analysis of image features, based on K-SVD image denoising algorithm, an image sparse regularization denoising algorithm based on structural similarity is proposed. In this paper, the structural similarity is used instead of the mean square error as the fidelity term in the sparse regularization denoising model. By using it as an optimization criterion, the geometric features of the image can be restored to the utmost. The experimental results show that compared with the K-SVD image denoising algorithm, the algorithm can better preserve the structural information of the image and obtain better image visual effects while effectively denoising.