Reviewerly/exHarmony huggingface dataset:
Symbiotic organisms search algorithm using random walk and adaptive Cauchy mutation on the feature selection of sleep staging

Sleep staging can objectively evaluate sleep quality to effectively assist in preventing and diagnosing sleep disorder. Because of the multi-channel and multi-model characteristics of physiological signals, high-dimensional features cannot be avoided when studying sleep staging. High-dimensional features are often mixed with redundant and irrelevant features, which may decrease the accuracy of classifiers and increase the computational cost. Feature selection can remove redundant and irrelevant features but is considered a challenging task in machine learning. Therefore, feature selection can be regarded as a multi-objective optimization problem. In this paper, the proposed symbiotic search algorithm (RCSOS), which is based on random walk and adaptive Cauchy mutation, can improve the optimization performance of the original algorithm. A binary version of RCSOS is proposed according to the twenty transformation functions. Then, the proposed algorithm is applied to feature selection in sleep staging. To validate the performance and generalization of the algorithm, seven groups of data from two different datasets were tested. Compared with the state-of-art algorithms, the proposed binary version of the RCSOS algorithm performs best on feature selection of sleep staging.
Robust Jointly Sparse Fuzzy Clustering With Neighborhood Structure Preservation

Fuzzy clustering techniques, especially fuzzy C-means (FCM) and its weighted variants, are typical partitive clustering models that are widely used for revealing possible hidden structures in data. Although they can quantitatively depict the overlapping areas with a partition matrix, their performances deteriorate when dealing with high-dimensional data because the distance computations may be negatively impacted by the irrelevant features, and then the concentration effect may arise. Moreover, they are sensitive to noisy environments. To tackle these obstacles, a robust jointly sparse fuzzy clustering method (RJSFC) is proposed in this study. The representative prototypes, sparse membership grades, and an orthogonal projection matrix are simultaneously learnt when optimizing RJSFC. The obtained low-dimensional embeddings can preserve the local neighborhood structure, and the clustering is conducted in the transformed lower dimensional space rather than the original space, which improves the capability of fuzzy clustering for dealing with high-dimensional scenarios. Furthermore, <inline-formula><tex-math notation="LaTeX">${L_{2,1}}$</tex-math></inline-formula>-norm is exploited as the basic metric for both loss and regularization parts in RJSFC, the robustness of the model and the interpretability of the extracted features are enhanced. The notions of fuzzy clustering, neighborhood structure preservation, and feature extraction are seamlessly integrated into a unified model. The limitation of the previous two-stage clustering framework when dealing with high-dimensional data entailing dimensionality reduction and clustering procedures separately can be effectively addressed. Extensive experimental results on various well-known datasets demonstrate the usefulness of RJSFC when comparing with some state-of-the-art methods.
Towards graph-based class-imbalance learning for hospital readmission

Predicting hospital readmission with effective machine learning techniques has attracted a great attention in recent years. The fundamental challenge of this task stems from characteristics of the data extracted from electronic health records (EHR), which are imbalanced class distributions. This challenge further leads to the failure of most existing models that only provide a partial understanding for the learning problem and result in a biased and inaccurate prediction. To address this challenge, we propose a new graph-based class-imbalance learning method by fully making use of the data from different classes. First, we conduct graph construction for learning the pattern discrimination from between-class and within-class data samples. Then we design an optimization framework to incorporate the constructed graphs to obtain a class-imbalance aware graph embedding and further alleviate performance degeneration. Finally, we design a neural network model as the classifier to conduct imbalanced classification, i.e., hospital readmission prediction. Comprehensive experiments on six real-world readmission datasets show that the proposed method outperforms state-of-the-art approaches in readmission prediction task.
Risk Assessment for Personalized Health Insurance Based on Real-World Data

The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.
Differential Tweetment

Automated systems for detecting harmful social media content are afflicted by a variety of biases, some of which originate in their training datasets. In particular, some systems have been shown to propagate racial dialect bias: they systematically classify content aligned with the African American English (AAE) dialect as harmful at a higher rate than content aligned with White English (WE). This perpetuates prejudice by silencing the Black community. Towards this problem we adapt and apply two existing bias mitigation approaches: preferential sampling pre-processing and adversarial debiasing in-processing. We analyse the impact of our interventions on model performance and propagated bias. We find that when bias mitigation is employed, a high degree of predictive accuracy is maintained relative to baseline, and in many cases bias against AAE in harmful tweet predictions is reduced. However, the specific effects of these interventions on bias and performance vary widely between dataset contexts. This variation suggests the unpredictability of autonomous harmful content detection outside of its development context. We argue that this, and the low performance of these systems at baseline, raise questions about the reliability and role of such systems in high-impact, real-world settings.
AI for humanitarian action: Human rights and ethics

Abstract Artificial intelligence (AI)-supported systems have transformative applications in the humanitarian sector but they also pose unique risks for human rights, even when used with the best intentions. Drawing from research and expert consultations conducted across the globe in recent years, this paper identifies key points of consensus on how humanitarian practitioners can ensure that AI augments – rather than undermines – human interests while being rights-respecting. Specifically, these consultations emphasized the necessity of an anchoring framework based on international human rights law as an essential baseline for ensuring that human interests are embedded in AI systems. Ethics, in addition, can play a complementary role in filling gaps and elevating standards above the minimum requirements of international human rights law. This paper summarizes the advantages of this framework, while also identifying specific tools and best practices that either already exist and can be adapted to the AI context, or that need to be created, in order to operationalize this human rights framework. As the COVID crisis has laid bare, AI will increasingly shape the global response to the world's toughest problems, especially in the development and humanitarian sector. To ensure that AI tools enable human progress and contribute to achieving the Sustainable Development Goals, humanitarian actors need to be proactive and inclusive in developing tools, policies and accountability mechanisms that protect human rights.
Deep Learning Methods for Indian Sign Language Recognition

Indian Sign Language is the language used by specially abled people in India. Unfortunately the general population has no understanding of the sign language which hampers the communication between the specially abled and the general population. We are proposing a methodology to bridge this gap. We have used two approaches to solve this problem. First using the depth+RGB data captured using a Microsoft Kinect and predicting the gestures in real time. For segmenting the hand region from the data obtained by the RGB-D camera we used 3D reconstruction and affine transformation to map the depth and RGB information. Convolutional neural networks were used and segmented hand images/videos were used as an input to them. 36 static hand gestures from Indian Sign Language were trained and a classification accuracy of 98.81% was achieved on the test data. This model also showed a good performance when we transfer learned the American Sign Language giving a classification accuracy of 97.71%. LSTM with a convolutional kernel was used for training 10 dynamic gestures. This model achieved a classification accuracy of 99.08%. But as soon as we implemented this system, we figured out there is an inherent problem with this methodology. It is practically unreasonable to carry the bulky Microsoft Kinect around along with a system capable of performing the computation to communicate with people. We attempted to solve this problem using semantic segmentation of the hands. We used U-Net with ResNet 101 as the backbone for the same. Semantic segmentation utilises the input from a normal RGB camera which completely removes the necessity of using a RGB-D Kinect camera. We performed multi-class semantic segmentation which gave an IOU score of 0.9920 and an F1 score of 0.9957 on the training data. The above models performed extremely well in real time.
Enhanced Twitter bot detection using ensemble machine learning

Social media has been an unavoidable part of our life over the years. As it is getting popular, the number of social media bots are also increasing. Social media bots are the artificial agents who imitate as a human on social media. They are intended to like, retweet the posts which eventually can tamper with the genuineness of the trend. They can also be a menace to democracy as they can falsely influence people. Social media bots can be used for cyberbullying, terrorist activities, gaining fame, spreading wrong information, restricting freedom of speech, spamming. To detect social media bots on Twitter, we utilized metadata of Twitter profiles and applied a unique feature selection method, and also explored the power of ensemble learning to make a robust classifier.
The Place of Photovoltaics in Poland’s Energy Mix

The energy strategy and environmental policy in the European Union are climate neutrality, low-carbon gas emissions, and an environmentally friendly economy by fighting global warming and increasing energy production from renewable sources (RES). These sources, which are characterized by high investment costs, require the use of appropriate support mechanisms introduced with suitable regulations. The article presents the current state and perspectives of using renewable energy sources in Poland, especially photovoltaic systems (PV). The specific features of Polish photovoltaics and the economic analysis of investment in a photovoltaic farm with a capacity of 1 MW are presented according to a new act on renewable energy sources. This publication shows the importance of government support that is adequate for the green energy producers.
Feature Selection Boosted by Unselected Features

Feature selection aims to select strongly relevant features and discard the rest. Recently, embedded feature selection methods, which incorporate feature weights learning into the training process of a classifier, have attracted much attention. However, traditional embedded methods merely focus on the combinatorial optimality of all selected features. They sometimes select the weakly relevant features with satisfactory combination abilities and leave out some strongly relevant features, thereby degrading the generalization performance. To address this issue, we propose a novel embedded framework for feature selection, termed feature selection boosted by unselected features (FSBUF). Specifically, we introduce an extra classifier for unselected features into the traditional embedded model and jointly learn the feature weights to maximize the classification loss of unselected features. As a result, the extra classifier recycles the unselected strongly relevant features to replace the weakly relevant features in the selected feature subset. Our final objective can be formulated as a minimax optimization problem, and we design an effective gradient-based algorithm to solve it. Furthermore, we theoretically prove that the proposed FSBUF is able to improve the generalization ability of traditional embedded feature selection methods. Extensive experiments on synthetic and real-world data sets exhibit the comprehensibility and superior performance of FSBUF.
Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight

Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.
Towards Robust Robot Control in Cartesian Space Using an Infrastructureless Head- and Eye-Gaze Interface

This paper presents a lightweight, infrastructureless head-worn interface for robust and real-time robot control in Cartesian space using head- and eye-gaze. The interface comes at a total weight of just 162 g. It combines a state-of-the-art visual simultaneous localization and mapping algorithm (ORB-SLAM 2) for RGB-D cameras with a Magnetic Angular rate Gravity (MARG)-sensor filter. The data fusion process is designed to dynamically switch between magnetic, inertial and visual heading sources to enable robust orientation estimation under various disturbances, e.g., magnetic disturbances or degraded visual sensor data. The interface furthermore delivers accurate eye- and head-gaze vectors to enable precise robot end effector (EFF) positioning and employs a head motion mapping technique to effectively control the robots end effector orientation. An experimental proof of concept demonstrates that the proposed interface and its data fusion process generate reliable and robust pose estimation. The three-dimensional head- and eye-gaze position estimation pipeline delivers a mean Euclidean error of 19.0±15.7 mm for head-gaze and 27.4±21.8 mm for eye-gaze at a distance of 0.3–1.1 m to the user. This indicates that the proposed interface offers a precise control mechanism for hands-free and full six degree of freedom (DoF) robot teleoperation in Cartesian space by head- or eye-gaze and head motion.
Uncertainty and dispute resolution for blockchain and smart contract institutions

Abstract Smart contracts have been proposed as a means of revolutionizing transacting between human actors and contributing to blockchain platforms substituting for many current institutions. However, the technical nature of blockchain platforms and smart contracts requires levels of certainty and foresight sufficient for contracts to be complete. We examine the technical and economic characteristics of blockchains and smart contracts to identify sources of uncertainty that may pose challenges to the ability of these technologies to displace existing institutional arrangements, in particular, the courts and other arbitration arrangements. Despite the development of alternative automated blockchain institutions such as the Kleros dispute resolution system, the case for smart contracts and blockchain applications to supplant real-world institutions remains weak. Inherent incompleteness due to limits to information availability, human cognition, and communication means that traditional contract governance institutions will continue to complement blockchain smart contract governance arrangements. The more complex and unique the transaction, the higher the value at risk, the harder to anticipate and precisely specify contingencies and measure and observe outcomes. Furthermore, the longer the time frame between agreement and execution, the less likely it is that smart contracting will be more efficient than traditional contracting.
Kinematic Identification of Industrial Robot Using End-Effector Mounted Monocular Camera Bypassing Measurement of 3-D Pose

This article proposes a new method for kinematic identification of an industrial robot using a monocular camera mounted on its end-effector. Measurement of end-effector 3-D poses required as input for this process is bypassed by measurements solely in 2-D image space, thus permitting estimation of corrections in parameters in a single stage. This is in contrast with multiple stages used earlier. The use of camera enabled the combination of geometric and parametric techniques of identification. The advantages are well-defined experimental strategy and identification of externally defined coordinate frames. Kinematic identification using measurements in 2-D image space was not affected by issues associated with observability which is commonly encountered when measurements of 3-D pose are used. This is because of the fact that the algorithm used for this purpose utilized identification Jacobian which relates measurements in image space to corrections in parameters. It was computed using backward recursion. Hand-eye transformation was also obtained during the identification process, thus minimizing the number of steps or hardware that is involved. The results were verified through simulation and validated through multiple experiments including those based on ISO standards, on KUKA KR 5 arc industrial robot. Some of the results were comparable to that obtained using a laser tracker which is a popular but expensive and offline measurement system which is not commonly available. Robustness of the proposed method to measurement noise has also been demonstrated through simulation. The experiments were conducted and the estimated parameters could be used in an already existing system used for bin-picking.
Self-attentive Adversarial Stain Normalization

Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. Traditionally proposed stain normalization and color augmentation strategies can handle the human level bias. But deep learning models can easily disentangle the linear transformation used in these approaches, resulting in undesirable bias and lack of generalization. To handle these limitations, we propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.
Objects Detection of UAV for Anti-UAV Based on YOLOv4

Currently, drone detection is a research hotspot in the security field due to its popularity. This paper proposed a recognition method of the low-altitude drone detection based on the YOLOv4 (You Only Look Once version 4) model and also introduced YOLOv4 algorithm in detection low-altitude UAV object for the first time. Sample set of drone flight attitude images constructed by shooting, downloading from the Internet and expanding the existing data is used to solve the lack of standard data set. The experimental results show that although YOLOv4, YOLOv3 and SSD algorithm all belong to one-stage algorithm, the average accuracy and real-time detection speed of YOLOv4 are better than that of the YOLOv3 and SSD.
Comments on biometric-based non-transferable credentials and their application in blockchain-based identity management

In IT-ecosystems, access to unauthorized parties is prevented with credential-based access control techniques (locks, RFID cards, biometrics, etc.). Some of these methods are ineffective against malicious users who lend their credentials to other users. To obtain non-transferability, Adams proposed a combination of biometrics encapsulated in Pedersen commitment with Brands digital credential. However, Adams’ work does not consider the Zero Knowledge Proof-of Knowledge (ZKPoK) system for Double Discrete Logarithm Representation of the credential. Besides, biometrics is used directly, without employing any biometric cryptosystem to guarantee biometric privacy, thus Adams’ work cannot be GDPR-compliant. In this paper, we construct the missing ZKPoK protocol for Adam’s work and show its inefficiency. To overcome this limitation, we present a new biometric-based non-transferable credential scheme that maintains the efficiency of the underlying Brands credential. Secondly, we show the insecurity of the first biometric-based anonymous credential scheme designed by Blanton et al.. In this context, we present a brute-force attack against Blanton’s biometric key generation algorithm implemented for fuzzy vault. Next, we integrate an Oblivious PRF (OPRF) protocol to solve the open problem in Blanton’s work and improve its efficiency by replacing the underlying signature scheme with PS-signatures. Finally, we evaluate application scenarios for non-transferable digital/anonymous credentials in the context of Blockchain-based Identity Management (BBIM). We show that our modified constructions preserve biometric privacy and efficiency, and can easily be integrated into current BBIM systems built upon efficient Brands and PS-credentials.