Reviewerly/exHarmony huggingface dataset:
CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language

Existing methods using generative adversarial approaches for Zero-Shot Learning (ZSL) aim to generate realistic visual features from class semantics by a single generative alignment, which is highly under-constrained. As a result, the previous methods cannot guarantee that the generated visual features can truthfully reflect the corresponding semantics. To address this issue, we propose a novel method named Cycle-consistent Adversarial Networks for Zero-Shot Learning (CANZSL). It encourages a visual feature generator to synthesize realistic visual features from semantics, and then inversely translate back the synthesized visual features to the corresponding semantic space by a semantic feature generator. Furthermore, in this paper a more challenging and practical ZSL problem is considered where the original semantics are from natural language with irrelevant words instead of clean semantics, which are widely used in previous work. Specifically, a multi-modal consistent bidirectional generative adversarial model is trained to handle unseen instances by suppressing noise in the natural language. A forward one-to-many mapping from the class level descriptions to the visual features is coupled with an inverse many-to-one mapping from the visual space to the semantic space. Thus, a multi-modal cycle-consistency loss between the synthesized semantic representations and the ground truth can be learned and leveraged to enforce the generated semantic features to approximate to the real distribution in semantic space. Extensive experiments are conducted to demonstrate that our method consistently outperforms state-of-the-art approaches on natural language-based zero-shot learning tasks.
Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed demising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.
JESSIE

JESSIE is a robotic system that enables novice programmers to program social robots by expressing high-level specifications. We employ control synthesis with a tangible front-end to allow users to define complex behavior for which we automatically generate control code. We demonstrate JESSIE in the context of enabling clinicians to create personalized treatments for people with mild cognitive impairment (MCI) on a Kuri robot, in little time and without error. We evaluated JESSIE with neuropsychologists who reported high usability and learnability. They gave suggestions for improvement, including increased support for personalization, multi-party programming, collaborative goal setting, and re-tasking robot role post-deployment, which each raise technical and sociotechnical issues in HRI. We exhibit JESSIE's reproducibility by replicating a clinician-created program on a TurtleBot~2. As an open-source means of accessing control synthesis, JESSIE supports reproducibility, scalability, and accessibility of personalized robots for HRI.
An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization

The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.
Image Encryption Algorithm Based on Tent Delay-Sine Cascade with Logistic Map

We propose a new chaotic map combined with delay and cascade, called tent delay-sine cascade with logistic map (TDSCL). Compared with the original one-dimensional simple map, the proposed map has increased initial value sensitivity and internal randomness and a larger chaotic parameter interval. The chaotic sequence generated by TDSCL has pseudo-randomness and is suitable for image encryption. Based on this chaotic map, we propose an image encryption algorithm with a symmetric structure, which can achieve confusion and diffusion at the same time. Simulation results show that after encryption using the proposed algorithm, the entropy of the cipher is extremely close to the ideal value of eight, and the correlation coefficients between the pixels are lower than 0.01, thus the algorithm can resist statistical attacks. Moreover, the number of pixel change rate (NPCR) and the unified average changing intensity (UACI) of the proposed algorithm are very close to the ideal value, which indicates that it can efficiently resist chosen-plain text attack.
Regulating Fintech in the EU: the Case for a Guided Sandbox

New financial technology holds the promise of innovation and competition, challenging established products and services and frequently improving market processes. However, regulation of these new services faces a double challenge: to keep pace with innovation and facilitate new market entries while at the same time understanding and managing the regulatory risks that are involved. At this stage, the existing EU regulatory framework is of little help: the bulk of the present body of financial regulation stems from a different time, with different regulatory problems in mind. EU regulation is also very slow to change and to adapt. Therefore, this paper proposes a regulatory “sandbox” – an experimentation space – as a step towards a regulatory environment where such new business models can thrive. A sandbox would allow market participants to test fintech services in the real market, with real consumers, but under the close scrutiny of the supervisor. The benefit of such an approach is that it fuels the development of new business practices and reduces the “time to market” cycle of financial innovation, while simultaneously safeguarding consumer protection. At the same time, a sandbox allows for mutual learning in a technical field which is sometimes poorly understood, both for firms and for the regulator. This would help to reduce the prevalent regulatory uncertainty for all market participants. In the particular EU legal framework with various layers of legal instruments, the implementation of such a sandbox is not straightforward. In this paper, we propose a “guided sandbox”, operated by the EU Member States, but with endorsement, support, and monitoring by EU institutions. This innovative approach would be somewhat uncharted territory for the EU, and thereby also contribute to the future development of EU financial market governance as a whole.
Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No

Previous studies generally agree that face recognition accuracy is higher for older persons than for younger persons. But most previous studies were before the wave of deep learning matchers, and most considered accuracy only in terms of the verification rate for genuine pairs. This paper investigates accuracy for age groups 16-29, 30-49 and 50-70, using three modern deep CNN matchers, and considers differences in the impostor and genuine distributions as well as verification rates and ROC curves. We find that accuracy is lower for older persons and higher for younger persons. In contrast, a pre deep learning matcher on the same dataset shows the traditional result ofhigher accuracy for older persons, although its overall accuracy is much lower than that of the deep learning matchers. Comparing the impostor and genuine distributions, we conclude that impostor scores have a larger effect than genuine scores in causing lower accuracy for the older age group. We also investigate the effects of training data across the age groups. Our results show that fine-tuning the deep CNN models on additional images ofolder persons actually lowers accuracy for the older age group. Also, we fine-tune and train from scratch two models using age-balanced training datasets, and these results also show lower accuracy for older age group. These results argue that the lower accuracy for the older age group is not due to imbalance in the original training data.
Encrypted Cooperative Control Revisited

Distributed systems are ubiquitous in present-day technologies like smart cities. Such applications require decentralized control, which reduces the load on a single central party, but requires communication and data sharing between the participating agents. However, agents might not trust their peers with their private data. We propose secure multi-party computation schemes that ensure the private computation of the control updates of each agent, without leaking any other information about the states and controls of their neighbors. To this end, we make use of homomorphic encryption and private sum aggregation schemes. We analyze the conditions such that a dishonest agent cannot observe the rest of the network. Finally, we present implementations of the proposed schemes and showcase their efficiency.
Unsupervised Machine Learning in Pathology

Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised machine learning workflows can be deployed in existing pathology workflows to begin learning autonomously through exploration and without the need for extensive direction. Although still in its infancy, this type of machine learning, which more closely mirrors human intelligence, stands to add another exciting layer of innovation to computational pathology and accelerate the transition to autonomous pathologic tissue analysis.
An Explainable Artificial Intelligence Model for Clustering Numerical Databases

Nowadays, the international scientific community of machine learning has an enormous campaign in favor of creating understandable models instead of black-box models. The main reason is that experts in the application area are showing reluctance due to black-box models cannot be understood by them, and consequently, their results are difficult to be explained. In unsupervised problems, where experts have not labeled objects, obtaining an explanation of the results is necessary because specialists in the application area need to understand both the applied model as well as the obtained results for finding the rationale behind each obtained clustering from a practical point of view. Hence, in this paper, we introduce a clustering based on decision trees (eUD3.5), which builds several decision trees from numerical databases. Unlike previous solutions, our proposal takes into account both separation and compactness for evaluating a feature split without decreasing time efficiency and with no empirical parameter to control the depth of the trees. We tested eUD3.5 on 40 numerical databases of UCI Machine Learning Repository, showing that our proposal builds a set of high-quality unsupervised decision trees for clustering, allowing us to obtain the best average ranking compared with other popular state-of-the-art clustering solutions. Also, from the collection of unsupervised decision trees induced by our proposal, a set of high-quality patterns are extracted for showing the main feature-value pairs describing each cluster.
High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning

Path planning for sailboat robots is a challenging task particularly due to the kinematics and dynamics modelling of such kinds of wind propelled boats. The problem is divided into two layers. The first one is global were a general trajectory composed of waypoints is planned, which can be done automatically based on some variables such as weather conditions or defined by hand using some human-robot interface (a ground-station). In the second local layer, at execution time, the global route should be followed by making the sailboat proceed between each pair of consecutive waypoints. Our proposal in this paper is an algorithm for the global, path generation layer, which has been developed for the N-Boat (The Sailboat Robot project), in order to compute feasible sailing routes between a start and a target point while avoiding dangerous situations such as obstacles and borders. A reinforcement learning approach (Q-Learning) is used based on a reward matrix and a set of actions that changes according to wind directions to account for the dead zone, which is the region against the wind where the sailboat can not gain velocity. Our algorithm generates straight and zigzag paths accounting for wind direction. The path generated also guarantees the sailboat safety and robustness, enabling it to sail for long periods of time, depending only on the start and target points defined for this global planning. The result is the development of a complete path planner algorithm that, together with the local planner solved in previous work, can be used to allow the final developments of an N-Boat making it a fully autonomous sailboat.
Modeling and optimization of back temperature models of mono-crystalline silicon modules with special focus on the effect of meteorological and geographical parameters on PV performance

In this study, experimental technique was used to obtain datasets applied for developing 80 models, optimizing 7 models and computing back temperature at various tilt angles and orientations in Lagos, Nigeria and Africa. Statistical evaluation and ranking tools such as MBE, MPE, RMSE, RRMSE, R2 and GPI results revealed that model 3 outperformed excellently for module 3, model 31 for module 4, model 47 for module 5, model 57 for module 6 and model 70 for the generalised module category and equally the best for the 80 established models. Similar statistical analysis was performed on the selected seven models obtained from literature so as to test their performances with best established model in each category of development. The result revealed that the established models in this study outperformed the seven unoptimised models from literature. The authors thereafter established a single hybrid-parameter based optimization based model so as to enhance the performance capacity of the seven models. The result equally revealed that the established best performing models in each category significantly outperformed these seven models. The newly established monthly and yearly module temperature coefficient models performed excellently in the 36 towns in Nigeria and 54 cities in Africa when compared ambient temperature.
DeepProfile: Finding fake profile in online social network using dynamic CNN

Online Social Networks (OSN) are popular applications for sharing various data, including text, photos, and videos. However, fake account problems are one of the obstacles in the current OSN systems. Attacker exploits fake accounts to distribute misleading information such as malware, virus, or malicious URLs. Inspired by the big successes of deep learning in computer vision, mainly in automatic feature extraction and representation, we propose DeepProfile, a deep neural network (DNN) algorithm to deal with fake account issues. Instead of using standard machine learning, we construct a dynamic CNN to train a learning model in fake profile classification. Notably, we propose a novel pooling layer to optimize the neural network performance in the training process. Demonstrated by the experiments, we harvest a promising result with better accuracy and small loss than common learning algorithms in a malicious account classification task.
An improved visually meaningful encrypted image scheme

An encrypted image generally presents an appearance of texture-like or noise-like image, which exhibits an indication of encrypted data that may motivate a significantly large number of attacks. To address this problem several approaches have been carried out, where an encrypted image is transformed into a visually meaningful encrypted image (VMEI). We present an improved approach to encrypt a plain image into a visually meaningful cover image, where the preprocessing and embedding phases are modified to achieve a quite comparable performance. To make the assessment of this proposal, some standard metrics such as PSNR and SSIM are performed, and the obtained numerical results show that this proposal is comparable or superior to some VMEI schemes used as benchmark in this work. In most of the reference images considered in the dataset the values of PSNR are greater than 40 dB, and a value close to 1 for the SSIM metric, which means a great similarity and high quality, respectively, between the reference images and their obtained VMEI images. Also, the two-dimensional detrended fluctuation analysis is carried out to observe the scaling behavior of the images, which allows us to have a perceptual security parameter of encrypted images and visually meaningful cover images. For this metric, the scaling exponent values of the VMEI images are close to 2.1, which are very similar to the values obtained for the reference images, a situation that confirms that the hidden information may not be detected by the human visual system. Furthermore, for two reference images, this metric was able to detect how the pixel values are distributed, and how the pixel values are highly correlated, suggesting that this metric can be an objective measure of encrypted images.
Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming

Quantifying the robustness of neural networks or verifying their safety properties against input uncertainties or adversarial attacks have become an important research area in learning-enabled systems. Most results concentrate around the worst-case scenario where the input of the neural network is perturbed within a norm-bounded uncertainty set. In this paper, we consider a probabilistic setting in which the uncertainty is random with known first two moments. In this context, we discuss two relevant problems: (i) probabilistic safety verification, in which the goal is to find an upper bound on the probability of violating a safety specification; and (ii) confidence ellipsoid estimation, in which given a confidence ellipsoid for the input of the neural network, our goal is to compute a confidence ellipsoid for the output. Due to the presence of nonlinear activation functions, these two problems are very difficult to solve exactly. To simplify the analysis, our main idea is to abstract the nonlinear activation functions by a combination of affine and quadratic constraints they impose on their input-output pairs. We then show that the safety of the abstracted network, which is sufficient for the safety of the original network, can be analyzed using semidefinite programming. We illustrate the performance of our approach with numerical experiments.
MetaNER: Named Entity Recognition with Meta-Learning

Recent neural architectures in named entity recognition (NER) have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for NER under homogeneous and heterogeneous settings. We propose MetaNER, a novel meta-learning approach for domain adaptation in NER. Specifically, MetaNER incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaNER is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaNER on multiple datasets under homogeneous and heterogeneous settings. The experimental results show that MetaNER achieves state-of-the-art performance against eight baselines. Impressively, MetaNER surpasses the in-domain performance using only 16.17% and 34.76% of target domain data on average for homogeneous and heterogeneous settings, respectively.
OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning

Recommending fashion outfits to users presents several challenges. First of all, an outfit consists of multiple fashion items, and each user emphasizes different parts of an outfit when considering whether they like it or not. Secondly, a user's liking for a fashion outfit considers not only the aesthetics of each item but also the compatibility among them. Lastly, fashion outfit data is often sparse in terms of the relationship between users and fashion outfits. Not to mention, we can only obtain what the users like, but not what they dislike.