Reviewerly/exHarmony huggingface dataset:
Adaptive, Hybrid Feature Selection (AHFS)

This paper deals with the problem of integrating the most suitable feature selection methods for a given problem in order to achieve the best feature order. A new, adaptive and hybrid feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution. Various state-of-the-art feature selection methods are presented in detail with examples of their applications and an exhaustive evaluation is conducted to measure and compare the their performance with the proposed approach. Results prove that while the individual feature selection methods may perform with high variety on the test cases, the combined algorithm steadily provides noticeably better solution.
Federated Learning with Differential Privacy for Resilient Vehicular Cyber Physical Systems

Vehicular cyber physical systems (VCPS) will play a vital role in the quest to develop intelligent transportation systems (ITS) and smart cities around the world. Consequently, researchers in academia, industry and government continue to leverage on emerging technologies like software defined networking (SDN), blockchain, cloud computing and machine learning (ML) to improve the overall efficiency of these intelligent systems. Recently, mobile edge computing (MEC) has been used to enhance content caching and efficient resource allocation and therefore advance the development of data-intensive and delay-constrained applications that improve the driving experience in VCPS. Security and privacy concerns that endanger the safety of lives and infrastructure necessitate the need to use federated learning (FL), a distributed ML algorithm that employs learning at the edge to ensure that data remains at the different vehicles and thus enhance greater efficiency. In this paper therefore, we propose the use of FL, together with differential privacy to improve the resiliency of VCPS to adversarial attacks in connected vehicles.
Generating Stable Spin Squeezing by Squeezed-Reservoir Engineering

As a genuine many-body entanglement, spin squeezing (SS) can be used to realize the highly precise measurement beyond the limit constrained by classical physics. Its generation has attracted much attention recently. It was reported that $N$ two-level systems (TLSs) located near a one-dimensional waveguide can generate a SS by using the mediation effect of the waveguide. However, a coherent driving on each TLS is used to stabilize the SS, which raises a high requirement for experiments. We here propose a scheme to generate stable SS resorting to neither the spin-spin coupling nor the coherent driving on the TLSs. Incorporating the mediation role of the common waveguide and the technique of squeezed-reservoir engineering, our scheme exhibits the advantages over previous ones in the scaling relation of the SS parameter with the number of the TLSs. The long-range correlation feature of the generated SS along the waveguide in our scheme may endow it with certain superiority in quantum sensing, e.g., improving the sensing efficiency of spatially unidentified weak magnetic fields.
Dense Haze Removal by Nonlinear Transformation

Images captured in hazy or foggy weather conditions, suffer from various problems, such as limited visibility, low contrast, color distortions. These images are used in many computer vision applications, such as video surveillance, transportation, remote sensing. The elimination of the haze effect from these images is essential to ensure the perfect working of these applications. The degradation of a captured image is expressed by the physical model of hazy image formation. The physical model requires the estimation of transmission to restore a haze-free image, which is one of the most important parameters of single image dehazing (SID). Due to the ill-posed nature of SID, lots of priors/assumptions have been used. However, traditional methods fail when these priors do not hold, especially for varying haze concentrations, which lead to many issues such as incomplete haze removal or over enhancement in long-range regions. In this paper, a single image dehazing method based on a superpixel and nonlinear transformation is proposed. The proposed method transforms the minimum filtering on superpixels of a hazy image into the minimum filtering on superpixels of a haze-free image using nonlinear transformation. The nonlinear transformation prevents over enhancement in the long-range regions, while the superpixels reduce the halo artifacts in the dehazed image. The experimental results on challenging real hazy images, dense-hazy images, and synthetic images have proved that a combination of nonlinear transformation and superpixels provide the strength to the proposed method. The obtained dehazed results are evaluated qualitatively and quantitatively and it is found that the proposed method has tremendous performance as compared to state-of-the-art dehazing approaches.
Dense Interaction Learning for Video-based Person Re-identification

Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the structural interaction among them is pivotal for its success. In this paper, we propose a hybrid framework, Dense Interaction Learning (DenseIL), that takes the principal advantages of both CNN-based and Attention-based architectures to tackle video-based person re-ID difficulties. DenseIL contains a CNN encoder and a Dense Interaction (DI) decoder. The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames. Different from previous works, we additionally let the DI decoder densely attends to intermediate fine-grained CNN features and that naturally yields multi-grained spatial-temporal representation for each video clip. Moreover, we introduce Spatio-TEmporal Positional Embedding (STEP-Emb) into the DI decoder to investigate the positional relation among the spatial-temporal inputs. Our experiments consistently and significantly outperform all the state-of-the-art methods on multiple standard video-based person re-ID datasets.
Real-life Human Activity Recognition with Tri-axial Accelerometer Data from Smartphone using Hybrid Long Short-Term Memory Networks

Human activity recognition (HAR) has an enthusiastic research field in time-series classification due to its variation of successful applications in various domains. The availability of affordable wearable devices have provided many challenging and interesting research HAR problems. Current researches suggest that deep learning approaches are suited to automated feature extraction from raw sensor data, instead of conventional machine learning approaches that reply on handcrafted features. Based on the recent success of Long Short-Term Memory (LSTM) networks for HAR domains, this work proposes a generic framework for accelerometer data based on LSTM networks for real-life HAR. Four hybrid LSTM networks have been comparatively studied on a public available real-life HAR dataset. Moreover, we take advantage of Bayesian optimization techniques for tuning hyperparameter of each LSTM networks. The experimental results indicate that the CNN-LSTM network surpasses other hybrid LSTM networks.
IoT Security in Healthcare using AI: A Survey

Internet of Things (IoT) and Artificial Intelligence (AI) has led the digital transformation in modern healthcare. With any kind of digital transformation, security challenges must be considered in the early stages of the design. Healthcare data is sensitive and any breach compromises the privacy of patients. More so in IoT networks where the connected devices are vulnerable to attacks. Cyberattacks can result in life-threatening consequences. In this paper, a survey and analysis of research on the use of AI as a tool for cybersecurity to protect IoT networks used for healthcare are presented. A thorough analysis of literature between 2014 and 2019 is provided. A niche opportunity for researchers to focus on in this space is identified following a thorough analysis of related papers.
Offline and Online Learning of Signal Temporal Logic Formulae Using Decision Trees

In this article, we focus on inferring high-level descriptions of a system from its execution traces. Specifically, we consider a classification problem where system behaviors are described using formulae of Signal Temporal Logic (STL). Given a finite set of pairs of system traces and labels, where each label indicates whether the corresponding trace exhibits some system property, we devised a decision-tree-based framework that outputs an STL formula that can distinguish the traces. We also extend this approach to the online learning scenario. In this setting, it is assumed that new signals may arrive over time and the previously inferred formula should be updated to accommodate the new data. The proposed approach presents some advantages over traditional machine learning classifiers. In particular, the produced formulae are interpretable and can be used in other phases of the system’s operation, such as monitoring and control. We present two case studies to illustrate the effectiveness of the proposed algorithms: (1) a fault detection problem in an automotive system and (2) an anomaly detection problem in a maritime environment.
Dynamic obstacle detection based on panoramic vision in the moving state of agricultural machineries

he manner in which the obstacles around the automatic navigation agricultural machinery can be detected and identified is of great importance in improving the safety and operation efficiency of agricultural machineries. This study was based on a panoramic camera to rapidly detect the dynamic obstacles around the moving agricultural machinery. It used the Lucas–Kanade optical flow algorithm to detect the moving obstacles in a panoramic image. On the basis of the actual farmland operation, background optical flow dynamic model was established to filter the background optical flow. The noise optical flow was filtered by combining K-means clustering segmentation algorithm and the variances of optical flow direction and of optical flow length within clusters. On the basis of the segmentation clusters, we used an external rectangular box to select foreground moving object. The principal optical flow direction in the segmentation clusters, and the distance between clusters determined whether the same foreground moving object was selected. Subsequently, we placed the corresponding combination processing into use, which can make the box select the complete foreground motion target. By processing 100 frames of the images, result showed that the average time consumption of the proposed method was 0.801 s. The accuracy rate of detecting dynamic agricultural machinery was 88.06%, the accuracy rate of detecting pedestrians was 81.61%, and the overall accuracy rate was 82.93%. Thus, the proposed method could meet the requirements of actual farmland operations and have a good instantaneity and detection results.
Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Coherent gate errors are a concern in many proposed quantum-computing architectures. Here, we show that certain coherent errors can be reduced by a local optimization that chooses between two forms of the same Hermitian and unitary quantum gate. We refer to this method as hidden inverses, and it relies on constructing the same gate from either one sequence of physical operations or the inverted sequence of inverted operations. We use parity-controlled $Z$ rotations as our model circuit and numerically show the utility of hidden inverses as a function of circuit width $n$. We experimentally demonstrate the effectiveness for $n=2$ and $n=4$ qubits in a trapped-ion quantum computer. We numerically compare the method to other gate-level compilations for reducing coherent errors.
Exploring Image Enhancement for Salient Object Detection in Low Light Images

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.
Online discriminative graph learning from multi-class smooth signals

• Introducing discriminative graph learning for graph signal classification. • Tackling dynamic environments and online topology inference. • Online framework is guaranteed to track the optimal time-varying batch solution. • Validated by investigating real-world EEG and financial market data. • State-of-the-art performance in EEG emotion recognition (DEAP dataset). Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and inference tasks such as supervised classification is heavily dependent on the prior identification of the relational structures. Graphs are natural descriptors of the relationships between entities of complex environments. The underlying graph is not readily detectable in many cases and one has to infer the topology from the observed signals which admit certain regularity over the sought representation. Assuming that signals are smooth over the latent class-specific graph is the notion we build upon. Firstly, we address the problem of graph signal classification by proposing a novel framework for discriminative graph learning. To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph while maintaining non-smoothness with respect to the graphs corresponding to other classes. Discriminative features are extracted via graph Fourier transform (GFT) of the learned representations and used in downstream learning tasks. Secondly, we extend our work to tackle increasingly dynamic environments and real-time topology inference. To this end, we adopt recent advances of GSP and time-varying convex optimization. We develop a proximal gradient (PG) method which can be adapted to situations where the data are acquired on-the-fly. Beyond discrimination, this is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly. The introduced online framework is guaranteed to track the optimal time-varying batch solution under mild technical conditions. The validation of the proposed frameworks is comprehensively investigated using both synthetic and real data. The proposed classification pipeline outperforms the-state-of-the-art methods when applied to the problem of emotion classification based on electroencephalogram (EEG) data. We also perform network-based analysis of epileptic seizures using electrocorticography (ECoG) records. Moreover, by applying our method to financial data, our approach infers the relationships between the stock-price behavior of leading US companies and the recent events including the COVID-19 pandemic.
Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network

Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. Specially, entity embedding learning is not properly designed to combine user-item interaction information with KG context information. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.
PREFER

Point-of-Interest (POI) recommendation is significant in location-based social networks to help users discover new locations of interest. Previous studies on such recommendation mainly adopted a centralized learning framework where check-in data were uploaded, trained and predicted centrally in the cloud. However, such a framework suffers from privacy risks caused by check-in data exposure and fails to meet real-time recommendation needs when the data volume is huge and communication is blocked in crowded places. In this paper, we propose PREFER, an edge-accelerated federated learning framework for POI recommendation. It decouples the recommendation into two parts. Firstly, to protect privacy, users train local recommendation models and share multi-dimensional user-independent parameters instead of check-in data. Secondly, to improve recommendation efficiency, we aggregate these distributed parameters on edge servers in proximity to users (such as base stations) instead of remote cloud servers. We implement the PREFER prototype and evaluate its performance using two real-world datasets and two POI recommendation models. Extensive experiments demonstrate that PREFER strengthens privacy protection and improves efficiency with little sacrifice to recommendation quality compared to centralized learning. It achieves the best quality and efficiency and is more compatible with increasingly sophisticated POI recommendation models compared to other state-of-the-art privacy-preserving baselines.
Urban blight remediation strategies subject to seasonal constraints

Urban blight is often defined as abandoned or poorly maintained real estate properties, often with overgrowth, trash-filled yards and alleys, graffiti, and broken windows. It has a detrimental impact on neighborhood safety and reputation. Blight also creates low-value areas in urban centers, reduces property values and business investment, and increases unemployment. These effects make urban blight an issue of growing interest as the need to eradicate blighted properties has significantly increased worldwide. This study adopted a constructivist, socio-technical stance that combined cognitive mapping, moving averages, and the decision-making trial and evaluation laboratory method. This methodological approach facilitated the identification of prevention strategies that control urban blight occurrences and subsequent eradication subject to seasonal constraints. Based on real-world cases, data was collected directly from a panel of urban blight experts. The members analyzed different prevention initiatives in terms of five major dimensions—governance, community involvement, economic context, territorial planning, and operations—which were previously identified during the development of a collective causal map. Practical applications of the proposed decision-support system were analyzed and validated by two independent experts from the Direção Geral do Território (General Directorate of the Territory of Portugal). No prior research was found that has adopted the proposed methodology to address urban blight. Recommendations facilitating the development of prevention strategies to eradicate urban blight are also provided.
Analysis and Evaluation of Language Models for Word Sense Disambiguation

Abstract Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting.We also perform an in-depth comparison of the two main language model based WSD strategies, namely, fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.
FedeRank: User Controlled Feedback with Federated Recommender Systems

Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several data breaches and privacy scandals, the users are now worried about sharing their data. In the last decade, Federated Learning has emerged as a new privacy-preserving distributed machine learning paradigm. It works by processing data on the user device without collecting data in a central repository. We present FedeRank ( https://split.to/federank ), a federated recommendation algorithm. The system learns a personal factorization model onto every device. The training of the model is a synchronous process between the central server and the federated clients. FedeRank takes care of computing recommendations in a distributed fashion and allows users to control the portion of data they want to share. By comparing with state-of-the-art algorithms, extensive experiments show the effectiveness of FedeRank in terms of recommendation accuracy, even with a small portion of shared user data. Further analysis of the recommendation lists' diversity and novelty guarantees the suitability of the algorithm in real production environments.