Reviewerly/exHarmony huggingface dataset:
A Patient Centric Agent Assisted Private Blockchain on Hyperledger Fabric for Managing Remote Patient Monitoring

Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database.
Revisiting Bayesian Autoencoders With MCMC

Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge. This has been addressed with variational autoencoders so far. Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling has faced several limitations for large models; however, recent advances in parallel computing and advanced proposal schemes have opened routes less traveled. This paper presents Bayesian autoencoders powered by MCMC sampling implemented using parallel computing and Langevin-gradient proposal distribution. The results indicate that the proposed Bayesian autoencoder provides similar performance accuracy when compared to related methods in the literature. Furthermore, it provides uncertainty quantification in the reduced data representation. This motivates further applications of the Bayesian autoencoder framework for other deep learning models.
Unmanned Aircraft System Traffic Management (UTM) Simulation of Drone Delivery Models in 2030 Japan

An unmanned aircraft system traffic management (UTM) system to support flights beyond visual line-of-sight is considered necessary for the promotion of commercial drone use. In the research and development of UTM systems, cost and time constraints make it difficult to actually fly a large number of drones in the same airspace, so research is mainly conducted using a simulator. This paper presents details of a UTM simulator named the “scalable simulator for knowledge of low-altitude environment” (SKALE) developed by the Japan Aerospace Exploration Agency (JAXA), with respect to the construction of a model case of drone delivery model set in 2030 in Japan. Moreover, UTM concepts for airspace safety and efficient airspace utilization (parcel transport) are proposed and evaluated using JAXA’s UTM simulator and drone delivery model cases. Simulation results are discussed, and the knowledge gained for the improvement of airspace safety and airspace utilization (parcel transport) efficiency is documented.
Exploring Structural Sparsity in CNN via Selective Penalty

Although massive pruning methods are emerging for achieving structural sparsity in convolutional neural networks (CNN), most of them target structures such as ResNet. Meanwhile, previous works take more interest in pruning filters inside a residual block and keep the shortcut connection intact, leading to an imbalanced network structure. In this paper, we focus on the penalty-based method to prune already compact networks. In contrast to the broadly used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> constraint, which shrinks the parameters uniformly, we propose a novel penalty term that is similar in shape to an upside-down Laplace distribution. The penalty allows us to impose more pressure on potential weak channels but protects others during training to avoid damaging crucial channels, especially for compact architectures. We also design a candidate selection strategy to cooperate with the penalty-based training procedure. Besides, we address the residual block pruning problem by a scaling factor elimination skill, which is often ignored in other research. Our method reduces 50% parameters of MobileNet v1/v2 with a tolerable accuracy degradation. We further conduct pruning on MobileNetv1-SSDLite to compress parameters by 60%, manifesting the ability to generalize to different visual tasks. The experiment results demonstrate that our method outperforms pruning frameworks based on channel importance without a complicated tuning for hyper-parameters like search-based methods.
Cross-Domain Access Control Encryption: Arbitrary-policy, Constant-size, Efficient

Access control is a fundamental keystone in security. Damgard, Haagh, and Orlandi (TCC 2016) introduced access˚ control encryption (ACE) that enforces no-read and no-write rules without revealing the senders, receivers, or the content of the encrypted traffic. Existing designs of ACE for arbitrary policy (covering all possibilities of read/write relationship) rely on indistinguishability obfuscation or lattice-based assumptions, with either exponential-size ciphertexts or circuit realization of policy. Also, their designs mandate a private sanitizer key to remain perpetually online for sanitization. The only existing scheme that can afford a public sanitizer key supports only simple policies. To summarize, state-of-the-art ACE schemes only feature at most two of the following desirable properties: arbitrarypolicy, constant-size (ciphertext), and efficient (sanitization). This paper introduces an ACE scheme for arbitrary policy without sanitizer key, which solves the open question posed by Kim and Wu (Asiacrypt 2017). We also put forth the notion of cross-domain ACE, separating the key generator into the sender-authority and receiver-authority. Our scheme requires structure-preserving signatures, non-interactive zero-knowledge proof, and sanitizable identity-based broadcast encryption as the building blocks. It can be instantiated directly from pairing-based assumptions and features constant ciphertext size. We also prototyped our scheme and demonstrated its practical efficiency.
Rainfall prediction using Extreme Gradient Boosting

Abstract Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made. Previous research on rainfall prediction uses models that have their limitations and thus produce poor performance. This study aims to build a multivariate rainfall prediction model using the best performing technique to date namely the Extreme Gradient Boosting. This model is built based on 7 years of historical weather data collected by the weather station. The result had demonstrated that the model is capable of producing accurate predictions for daily rainfall estimates with training RMSE of 2.7 mm and the testing MAE of 8.8 mm.
A Supervised Machine Learning Approach to Detect Fake Online Reviews

With increasing use of internet, online business platforms are becoming the largest market place of the world. Purchase of online product is heavily dependent on user reviews. Some dishonest groups of people misuse this fact by posting fake reviews to promote their own products or demote their competitors. Detection of fake online reviews can be considered as a binary classification task that models a classifier to tell whether a review is fake or true. In this paper, we have developed an effective supervised machine learning approach to classify fake online reviews using a dataset that contains hotel reviews from online websites.
Sequence tagging for biomedical extractive question answering

Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passage pair. This setting is natural for general domain EQA as the majority of the questions in the general domain can be answered with a single span. Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.In this article, we investigate the question distribution across the general and biomedical domains and discover biomedical questions are more likely to require list-type answers (multiple answers) than factoid-type answers (single answer). This necessitates the models capable of producing multiple answers for a question. Based on this preliminary study, we propose a sequence tagging approach for BioEQA, which is a multi-span extraction setting. Our approach directly tackles questions with a variable number of phrases as their answer and can learn to decide the number of answers for a question from training data. Our experimental results on the BioASQ 7b and 8b list-type questions outperformed the best-performing existing models without requiring post-processing steps.Source codes and resources are freely available for download at https://github.com/dmis-lab/SeqTagQA.Supplementary data are available at Bioinformatics online.
Uncertainty quantification in extreme learning machine: Analytical developments, variance estimates and confidence intervals

Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literature make strong assumptions on the data, ignore the randomness of input weights or neglect the bias contribution in confidence interval estimations. This paper presents novel estimations that overcome these constraints and improve the understanding of ELM variability. Analytical derivations are provided under general assumptions, supporting the identification and the interpretation of the contribution of different variability sources. Under both homoskedasticity and heteroskedasticity, several variance estimates are proposed, investigated, and numerically tested, showing their effectiveness in replicating the expected variance behaviours. Finally, the feasibility of confidence intervals estimation is discussed by adopting a critical approach, hence raising the awareness of ELM users concerning some of their pitfalls. The paper is accompanied with a scikit-learn compatible Python library enabling efficient computation of all estimates discussed herein.
Learning Differentially Private Mechanisms

Differential privacy is a formal, mathematical definition of data privacy that has gained traction in academia, industry, and government. The task of correctly constructing differentially private algorithms is non-trivial, and mistakes have been made in foundational algorithms. Currently, there is no automated support for converting an existing, non-private program into a differentially private version. In this paper, we propose a technique for automatically learning an accurate and differentially private version of a given non-private program. We show how to solve this difficult program synthesis problem via a combination of techniques: carefully picking representative example inputs, reducing the problem to continuous optimization, and mapping the results back to symbolic expressions. We demonstrate that our approach is able to learn foundational algorithms from the differential privacy literature and significantly outperforms natural program synthesis baselines.
Reaserch and implementation of social distancing monitoring technology based on SSD

In the fight against the Covid-19, social distancing has proven to be a very effective measure to mitigate the spread of the disease. As resumption of work, production and classes accelerates, it is necessary to limit people’s social distance to reduce the rate of the virus spread. To solve this problem, a method for monitoring social distancing based on SSD object detection technology is proposed in this study. This method utilizes SSD300 model to detect people in a video or picture, and labels a Red Line as warning on the people whose distances are less than the default one, implementing real-time social distancing monitoring, and the mAP reaches 88.44%.
TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.
A multi-granularity locally optimal prototype-based approach for classification

Prototype-based approaches generally provide better explainability and are widely used for classification. However, the majority of them suffer from system obesity and lack transparency on complex problems. In this paper, a novel classification approach with a multi-layered system structure self-organized from data is proposed. This approach is able to identify local peaks of multi-modal density derived from static data and filter out more representative ones at multiple levels of granularity acting as prototypes. These prototypes are then optimized to their locally optimal positions in the data space and arranged in layers with meaningful dense links in-between to form pyramidal hierarchies based on the respective levels of granularity accordingly. After being primed offline, the constructed classification model is capable of self-developing continuously from streaming data to self-expend its knowledge base. The proposed approach offers higher transparency and is convenient for visualization thanks to the hierarchical nested architecture. Its system identification process is objective, data-driven and free from prior assumptions on data generation model with user- and problem- specific parameters. Its decision-making process follows the "nearest prototype" principle, and is highly explainable and traceable. Numerical examples on a wide range of benchmark problems demonstrate its high performance.
Structured subspace learning-induced symmetric nonnegative matrix factorization

• Based on the similarity space to be learned, SSLSNMF further learns a latent subspace, which considers the global and local structure of the data. • Since SSLSNMF is formulated in a semi-supervised way, the supervisory information with constraints of cannot-link and mustlink is also utilized. • To guarantee that the latent similarity subspace to be discriminative, the global and local structures of the data as well as structural consistencies for limited labels in a semisupervised manner are learned simultaneously in the proposed framework. • An effective alternating iterative algorithm is proposed with proved convergence. Symmetric NMF (SNMF) is able to determine the inherent cluster structure with the constructed graph. However, the mapping between the empirically constructed similarity representation and the desired one may contain complex structural and hierarchical information, which is not easy to capture with single learning approaches. Then, we propose a novel Structured Subspace Learning-induced Symmetric Nonnegative Matrix Factorization (SSLSNMF) in this paper. Based on the similarity space to be learned, SSLSNMF further learns a latent subspace, which considers the global and local structure of the data. Since SSLSNMF is formulated in a semi-supervised way, the supervisory information with constraints of cannot-link and must-link is also utilized. To guarantee that the latent similarity subspace to be discriminative, the global and local structures of the data as well as structural consistencies for limited labels in a semi-supervised manner are learned simultaneously in the proposed framework. An effective alternating iterative algorithm is proposed with proved convergence. Experiments conducted on six benchmark datasets show that better results can be obtained compared with state-of-the-art methods.
Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks

Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and constitute, hence, the earliest and most reliable behavioral cues for intention estimation. In this study, we present an experiment aimed at analyzing human behavior in simple teleoperated pick-and-place tasks in a simulated scenario and at devising a suitable model for early estimation of the current proximal intention. We show that scan paths are, as expected, heavily shaped by the current intention and that two types of Gaussian Hidden Markov Models, one more scene-specific and one more action-specific, achieve a very good prediction performance, while also generalizing to new users and spatial arrangements. We finally discuss how behavioral and model results suggest that eye movements reflect to some extent the invariance and generality of higher-level planning across object configurations, which can be leveraged by cooperative robotic systems.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search

Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channels for marketing. As the fundamental basis of search ads, relevance modeling has attracted increasing attention due to the significant research challenges and tremendous practical value. Most existing approaches solely rely on the semantic information in the input query-ad pair, while the pure semantic information in the short ads data is not sufficient to fully identify user's search intents. Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling. In this paper, we extensively investigate how to naturally fuse the semantic textual information with the user behavior graph, and further propose three novel AdsGNN models to aggregate topological neighborhood from the perspectives of nodes, edges and tokens. Furthermore, two critical but rarely investigated problems, domain-specific pre-training and long-tail ads matching, are studied thoroughly. Empirically, we evaluate the AdsGNN models over the large industry dataset, and the experimental results of online/offline tests consistently demonstrate the superiority of our proposal.
Evolutionary Rao algorithm

This paper proposes an evolutionary Rao algorithm (ERA) to enhance three state-of-the-art metaheuristic Rao algorithms (Rao-1, Rao-2, Rao-3) by introducing two new schemes. Firstly, the population is split into two sub-populations based on their qualities: high and low, with a particular portion. The high-quality sub-population searches for an optimum solution in an exploitative manner using a movement scheme used in the Rao-3 algorithm. Meanwhile, the low-quality one does in an explorative fashion using a new random walk. Secondly, two evolutionary operators: crossover and mutation, are incorporated to provide both exploitation and exploration strategies. A fitness-based adaptation is introduced to dynamically tune the three parameters: the portion of high-quality individuals, mutation radius, and mutation rate throughout the evolution, based on the improvement of best-so-far fitness. In contrast, the crossover is implemented using a standard random scheme. Comprehensive examinations using 38 benchmarks: twenty-three classic functions, ten CEC-C06 2019 benchmarks, and five global trajectory optimization problems show that the proposed ERA generally outperforms the four competitors: Rao-1, Rao-2, Rao-3, and firefly algorithm with courtship learning (FA-CL). Detailed investigations indicate that both proposed schemes work very well to make ERA evolves in an exploitative manner, which is created by a high portion of high-quality individuals and the crossover operator, and avoids being trapped on the local optimum solutions in an explorative manner, which is generated by a high portion of low-quality individuals and the mutation operator. Finally, the adaptation scheme effectively controls the exploitation-exploration balance by dynamically tuning the portion, mutation radius, and mutation rate throughout the evolution process.