Reviewerly/exHarmony huggingface dataset:
3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.
Building complex event processing capability for intelligent environmental monitoring

Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications.
Modeling IoT Equipment With Graph Neural Networks

Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.
A Color Image Encryption Technique Based on Bit-Level Permutation and Alternate Logistic Maps

Abstract The paper presents an approach to encrypt the color images using bit-level permutation and alternate logistic map. The proposed method initially segregates the color image into red, green, and blue channels, transposes the segregated channels from the pixel-plane to bit-plane, and scrambles the bit-plane matrix using Arnold cat map (ACM). Finally, the red, blue, and green channels of the scrambled image are confused and diffused by applying alternate logistic map that uses a four-dimensional Lorenz system to generate a pseudorandom number sequence for the three channels. The parameters of ACM are generated with the help of Logistic-Sine map and Logistic-Tent map. The intensity values of scrambled pixels are altered by Tent-Sine map. One-dimensional and two-dimensional logistic maps are used for alternate logistic map implementation. The performance and security parameters histogram, correlation distribution, correlation coefficient, entropy, number of pixel change rate, and unified averaged changed intensity are computed to show the potential of the proposed encryption technique.
Secure Binary Image Steganography With Distortion Measurement Based on Prediction

In this paper, a binary image steganographic scheme is presented, which aims at minimizing the embedding distortions measured by prediction. A prediction model of the center pixel's value is established in a 3 × 3 local region. A concept of “uncertainty” is introduced to represent the prediction result and the uncertainty is defined as the proximity of probabilities about whether the center pixel is black or white. A pixel with high uncertainty means that it is hard to distinguish whether it has been flipped or not, and thus the distortion introduced by flipping this pixel is small. The uncertainty is an appended statistical explanation of human visual perception and the distortion measurement based on it can evaluate the embedding changes on both vision and statistics. Benefiting from the statistics, uncertainty can evaluate the distortion influence in an extended local region. To play the advantage of distortion measurement, the syndrome-trellis code (STC) is employed to minimize the embedding distortions. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high vision imperceptibility and statistical security.
<b>CoClust</b>: A <i>Python</i> Package for Co-Clustering

Co-clustering (also known as biclustering), is an important extension of cluster analysis since it allows to simultaneously group objects and features in a matrix, resulting in row and column clusters that are both more accurate and easier to interpret.This paper presents the theory underlying several effective diagonal and non-diagonal co-clustering algorithms, and describes CoClust, a package which provides implementations for these algorithms.The quality of the results produced by the implemented algorithms is demonstrated through extensive tests performed on datasets of various size and balance.CoClust has been designed to complete and easily interface with popular Python machine learning libraries such as scikit-learn.
Design of a Forgetting Blockchain: A Possible Way to Accomplish GDPR Compatibility

Practitioners as well as academics expect that blockchain technology is a game changer for a variety of use cases. This is due to transaction immutability enabled by keeping a history of all transactions.Nevertheless, this strength can become its biggest weakness.There already exists a lively discussion on scenarios where it is necessary to delete submitted data from the chain after it is no longer needed.This becomes even more crucial with the introduction of the European General Data Protection Regulation (GDPR).In this paper, we make use of a design science research (DSR) approach to design an IT artifact in the form of a prototype that maintains most of the key features of blockchain technology but deletes old data.We evaluate the prototype with the help from experts to investigate what to expect from blockchains that delete data and derive principles on how to design them.
Seq2Seq Attentional Siamese Neural Networks for Text-dependent Speaker Verification

In this paper, we present a Sequence-to-Sequence Attentional Siamese Neural Network (Seq2Seq-ASNN) that leverages temporal alignment information for end-to-end speaker verification. In prior works of speaker discriminative neural networks, utterance-level evaluation/enrollment speaker representations are usually calculated. Our proposed model, utilizing a sequence-to-sequence (Seq2Seq) attention mechanism, maps the frame-level evaluation representation into enrollment feature domain and further generates an utterance-level evaluation-enrollment joint vector for final similarity measure. Feature learning, attention mechanism, and metric learning are jointly optimized using an end-to-end loss function. Experimental results show that our proposed model outperforms various baseline methods, including the traditional i-Vector/PLDA method, multi-enrollment end-to-end speaker verification models, d-vector approaches, and a self attention model, for text-dependent speaker verification on a Tencent internal voice wake-up dataset.
Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization

The increase in massive data processing and computing in datacenters in recent years has resulted in the problem of severe energy consumption, which also leads to a significant carbon footprint and a negative impact on the environment. A growing number of IT companies with operating datacenters are adopting renewable energy as part of their energy supply to offset the consumption of brown energy. In this paper, we focused on a green datacenter using hybrid energy supply, leveraged the time flexibility of workloads in the datacenter, and proposed a thermal-aware workload management method to maximize the utilization of renewable energy sources, considering the power consumption of both computing devices and cooling devices at the same time. The critical knob of our approach was workload shifting, which scheduled more delay-tolerant workloads and allocated resources in the datacenter according to the availability of renewable energy supply and the variation of cooling temperature. In order to evaluate the performance of the proposed method, we conducted simulation experiments using the Cloudsim-plus tool. The results demonstrated that the proposed method could effectively reduce the consumption of brown energy while maximizing the utilization of green energy.
Asymmetric color cryptosystem based on compressed sensing and equal modulus decomposition in discrete fractional random transform domain

In this paper, we propose an asymmetric encryption and compression method for a color image based on compressed sensing and equal modulus decomposition in the discrete fractional random transform (DFrRT) domain. In this scheme, a color image is firstly encrypted into a single channel image, which is divided into low-frequency image and high-frequency image by the discrete wavelet transform. Subsequently, the high-frequency image is compressed into two matrices by compressed sensing. One of the matrices is combined with the low-frequency image into one input of DFrRT. The other acts as another input of DFrRT. Finally, the output result of DFrRT is encrypted by the equal modulus decomposition in DFrRT domain to create an effective trapdoor one-way function. Compared to the other transformations, the private key of DFrRT is the high-frequency component related to the plaintext in this paper. Thus, the proposed cryptosystem is able to resist various types of attacks and maintain the asymmetric characteristics of the cryptosystem. Numerical simulations are presented to demonstrate the feasibility and robustness of the proposed method.
СТАН, ТЕХНОЛОГІЇ ТА ПЕРСПЕКТИВИ ДИСТАНЦІЙНОГО НАВЧАННЯ У ВИЩІЙ ОСВІТІ УКРАЇНИ

On the basis of the analysis of queries in Google Trends, Google Scholar, and the database of Volodymyr Vernadskyi National Library of Ukraine, a sample of historically structured knowledge systems has been obtained which can be used to describe the contribution of distance learning studies during the time slots available for each resource. According to the survey of students of four institutions of different education types (classical, technological, pedagogical and marine ones), the modern state-of-the-art of the application of distance learning technologies at higher education institutions is established. The analysis of the progressive ideas and practical achievements of countries of Europe, North America and Asia, which in recent years have made the significant progress in reforming their educational systems and the implementation of innovative technologies, has allowed to distinguish the following technological advances for the implementation of distance learning technologies: the adaptive learning technology, mobile learning, the virtual, supplemented and hybrid reality, the Internet of Things, systems of the next generation learning management, artificial intelligence and natural user interfaces. The following perspectives of the distance learning development in Ukraine are highlighted: updating of software and technical support and material resources of higher education institutions; provision of higher education institutions of Ukraine with the broadband Internet access; organization of cooperation of software developers for distance learning, distance education methodologists and lecturer; improvement the staffing of distance learning; development and distribution of platforms with intuitive non-complex software interfaces for creating distance courses; creation or adaptation of information technologies and electronic educational and methodological developments for the support of new technologies of distance learning at higher education institutions of Ukraine; studying the effectiveness of technological advances in the IT industry in the process of higher education teaching and learning, ensuring the process of obtaining an educational degree (bachelor’s degree, master’s degree) at higher education institutions of Ukraine through training at mass open distance courses.
Fuzzy Multilayer Clustering and Fuzzy Label Regularization for Unsupervised Person Re-identification

Unsupervised person reidentification has received more attention due to its wide real-world applications. In this paper, we propose a novel method named fuzzy multilayer clustering (FMC) for unsupervised person reidentification. The proposed FMC learns a new feature space using a multilayer perceptron for clustering in order to overcome the influence of complex pedestrian images. Meanwhile, the proposed FMC generates fuzzy labels for unlabeled pedestrian images, which simultaneously considers the membership degree and the similarity between the sample and each cluster. We further propose the fuzzy label regularization (FLR) to train the convolutional neural network (CNN) using pedestrian images with fuzzy labels in a supervised manner. The proposed FLR could regularize the CNN training process and reduce the risk of overfitting. The effectiveness of our method is validated on three large-scale person reidentification databases, i.e., Market-1501, DukeMTMC-reID, and CUHK03.
SQIL: Imitation Learning via Regularized Behavioral Cloning.

Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation. Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon. Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training. We propose a simple alternative that still uses RL, but does not require learning a reward function. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states. We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior. Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm. Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation. Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo.
Rapid inversion of heavy metal concentration in karst grain producing areas based on hyperspectral bands associated with soil components

Heavy metal pollution in soil has become a prominent problem affecting agricultural security and ecological health. Hyperspectral remote sensing is used as a rapid method to predict soil heavy metal concentrations. The processing of spectral data and the variables of the estimated model has an important impact on the predictive model of soil heavy metal elements. In this paper, smoothed and resampled spectral reflections are preprocessed by using three preprocessing methods, namely, standard normal variate (SNV), multiplication scatter correlation (MSC) and normalization (NOR). Then, first and second order differential (FD and SD, respectively) and absorbance transformation (AT) are performed. Based on the adsorption and retention of heavy metals by various soil components, the relevant spectral bands are extracted as modeling variables. An extreme learning machine algorithm (ELM) is used to establish the model, and the effects of different factors on the model are compared. Results show that the combination of the three preprocessing methods (SNV, MSC and NOR) with spectral transformation can enhance the stability and predictive ability of the resulting model. The combination of SNV and FD can predict the contents of Cr, Ni and Pb. The R2 of the model is 0.85, 0.87 and 0.80 respectively. The optimal model of Cu is derived from the combination of NOR and SD (R2 = 0.84), and the spectral responses of soil Cr, Ni, Cu and Pb, are closely related to clay mineral-related and organic matter-related bands. The model established by the clay-related bands enhances the stability of the prediction of Ni content, and the RPD value was increased from 2.46 to 2.72 compared with the full-band model. The combination of bands associated with organic matter and clay minerals can accurately predict the content of Cr and Cu in soil; indeed, the predict model R2 for these elements reaches 0.88. Accurate prediction of soil Pb by the full-band model indicates that the Pb concentration in the study area is related to a various of soil chemical components. The prediction effects of the four heavy metal elements show the order Cr > Cu > Ni > Pb. The results of the current study complement the theoretical basis for estimating the heavy metal content of soil by hyperspectral spectroscopy, and provide important insights into the application of hyperspectral remote sensing to monitor other heavy metals.
A New Method of Privacy Protection: Random k-Anonymous

A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved. Since the process of finding anonymous equivalence is very time consuming, a two-step clustering method is used to divide the original data set into equivalence classes. First, the original data set is divided into several different sub-datasets, and then the equivalence classes are formed in the sub-datasets, thus greatly reducing the computational cost of finding anonymous equivalence classes. The experiments are conducted on three different data sets, and the results show that the proposed method is more efficient and the information loss of anonymous dataset is much smaller.
Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA)

Biochemistry has been broadly defined as “chemistry of molecules included or related to living systems”, but is becoming increasingly hard to be distinguished from other related fields. Targets of its studies evolve rapidly; some newly emerge, disappear, combine, or resurface themselves with a fresh viewpoint. Methodologies for biochemistry have been extremely diversified, thanks particularly to those adopted from molecular biology, synthetic chemistry, and biophysics. Therefore, this paper adopts topic modeling, a text mining technique, to identify the research topics in the field of biochemistry over the past twenty years and quantitatively analyze the changes in its trends. The results of the topic modeling analysis obtained through this study will provide a helpful tool for researchers, journal editors, publishers, and funding agencies to understand the connections among the diverse sub-fields in biochemical research and even see how the research topics branch out and integrate with other fields.
PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model

Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary data are available at Bioinformatics online.