Reviewerly/exHarmony huggingface dataset:
DESSE: A Dynamic Efficient Forward Searchable Encryption Scheme

Under the cloud computing environment, Searchable Symmetric Encryption (SSE) is an effective method to solve the problem of encrypted data retrieval, and helps to protect the users' privacy. Recent researches show that some attacks may bring great security threats to SSE, and forward privacy can effectively prevent these attacks, so forward privacy is very necessary for SSE scheme. Most of the existing forward privacy SSE schemes fall into two types: ORAM-based and Bost-based. The former is simple, but it has large communication overhead and low dynamic update efficiency. The latter is better than the former, but it is based on asymmetric encryption primitives. Based on symmetric encryption primitives, we propose a dynamic efficient forward privacy scheme DESSE in this paper. DESSE uses pseudo-random permutation to realize forward privacy, and uses delete list to identify the final state of the same file added and deleted repeatedly, so as to realize the dynamic update of data. The method proposed in this paper is simple and flexible in structure, takes up less additional space, and can significantly improve the efficiency of updating. At the same time, it can achieve real-time updating of data. The correctness of our proposed scheme is tested using the Enron email data set in the end.
INVITED: AI Utopia or Dystopia - On Securing AI Platforms

Today we are witnessing the widespread deployment of AI algorithms on many computing platforms already to provide various services, thus driving the growing market for AI-based platforms. On the one end, AI support is demanded for resource-constrained embedded devices, e.g., integrated into smart homes and vehicles. On the other end, hi-tech giants and cloud services require AI platforms with increasing computational power to feed their data-hungry neural networks. Neglecting security and privacy aspects on both such low-end and high-end AI platforms can have devastating consequences for end users (privacy and safety) as well as for the AI service providers (IP theft). The utopia of a world where intelligent devices ease the human life can easily turn into a dystopia where the ownership of personal data is threatened.In recent years, tremendous effort has been invested in the development of security architectures that protect sensitive services in isolated execution contexts, called enclaves, which provide protection beyond that of commodity operating systems. In this paper, we elaborate on the most well-known enclave-based security architectures to protect AI services. We point out their shortcomings in providing the security guarantees needed for existing and emerging AI services and discuss new ideas and research directions.
PerformViz

To visually present the overall performance of several algorithms tested on several benchmark problems on one plot, we present a machine learning approach, called performViz. It allows one to clearly see, from a single plot, which algorithms are most suited for a given problem, the influence of each problem on the overall algorithm performance and similarities among both algorithms and problems. It consists of four steps: i) selecting a performance measure, ii) selecting a statistic to calculate a representative value of the performance measure for each algorithm on each problem, iii) performing hierarchical clustering, and iv) using heatmaps to visualize the clustering result.
Performance Analysis of the ML Prediction Models for the Detection of Sybil Accounts in an OSN

The Online Social Networks (OSNs) as such have significantly become huge platforms for information sharing and social interactions for a variety of users across the globe. In the backdrop of fast transformations, these OSNs are undergoing illegal activities especially in the form of security attacks, and have already started reflecting serious harmful effects on these interactions. One of the prominent attacks in such environments, the Sybil attack, is jeopardizing various categories of social interactions as the number of users having Sybil accounts on these social platforms is experiencing phenomenal growth. The existence of such Sybil accounts on OSNs may threaten to defeat the very purpose of these OSNs. The presence of these Sybil accounts of malicious users is really almost impossible to control and, very difficult to detect. In this paper, with the help of Machine Learning (ML), an attempt has been made to uncover the presence of such Sybil accounts on an OSN such as Twitter. After the acquisition and preprocessing of available datasets, the Correlation with Heatmap and Logistic Regression-Recursive Feature Elimination (LR-RFE) feature selection techniques were applied to get a set of optimal features from these datasets. Then the prediction models were trained on these datasets by using Random Forest (RF), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM) classifiers. Further, the effects of biasing of genuine accounts with fake accounts on feature selection and classification have been presented. It is concluded that the prediction models using the DT algorithm outperformed all other classifiers.
INNOVATIVE CLOUD COMPUTING: CHALLENGES FOR EDUCATION

The article deals with the problems inherent in the development of such innovations in education as cloud computing, which belong to the key trends of IT sphere development in the 21st century. Taking into account the purpose of the study, the cloud computing paradigm is described, the development of cloud computing in the business sector and the challenges for the education are explored. Today, educational institutions, in particular universities, along with many other organizations and enterprises, recognize cloud computing as useful for simplifying process management and administration as well as improving overall employee communication. The state of implementation of cloud projects in the high school is described. The preconditions for the introduction of C), хмарні обчислення (технології) (Cloud Computing) тощоloud computing in education are determined and the components of the educational process in the high school using the cloud technologies are determined. An example of new cloud discipline and cloud computing projects in universities, in particular, at the Banking University Lviv Institute, is presented. Described approaches to creating a cloud-based learning environment (informatioC), хмарні обчислення (технології) (Cloud Computing) тощоBLE) in high school that can be implemented through cloud services, moving Learning Management System (informatioLMS) to the cloud, and also integrating Learning Management System (informatioas well as the expansion of the functionality of its subsystems and resources), software, and cloud services. Examples of these approaches implementation at Ukrainian universities, in particular Banking University Lviv Institute, are presented. The outlined trends and prospects for cloud computing development taking into account their advantages and risks for use in the education sector.
Optimization of the ISP Parameters of a Camera Through Differential Evolution

Within the design and development of a smartphone, an important phase arises regarding time, which is related to the tuning of the ISP (image signal processor) of the camera. The ISP is an element that allows the adjustment of the images captured by a sensor in order to achieve the best image quality. The ISP implements different image improvement algorithms such as white balancing, denoising, and demosaicing as well as other image enhancement algorithms. The purpose of the ISP tuning process is to configure the parameters of these algorithms so that the processed images are of the highest quality. This task is carried out by the camera tuning engineer, who iteratively adjusts the ISP parameters through trial and error procedures until the desired quality is achieved. The complete adjustment process can be extended to several weeks and even months. The authors present a novel solution based on differential evolution, which allows a first-adjusted approximation of the ISP in a few hours. This work presents an architecture based on an optimization through a differential evolution algorithm with which different ISP tuning tests are carried out, and the good results in quality and time are verified.
A Development of Healthcare Monitoring System Based on Internet of Things Effective

The Recently there has been a growing interest in health care due to the COVID-19 situation.In this paper, we intend to develop a healthcare monitoring system to provide users with smart healthcare systems in line with the healthcare 3.0 era.The system consists of a wireless network between various sensors, Android smartphones, and OLEDs using Bluetooth, and through this, a health care monitoring system capable of collecting user's biometric information and managing health by receiving data values of sensors connected to Arduino.In conclusion, the user's BPM value was calculated using the heart rate sensor, and the exercise intensity can be adjusted through this.In addition, a step derivation algorithm is implemented using an acceleration sensor, and calorie consumption can be measured using the step and weight values.As such, the heart rate, step count, calorie consumption data can be transmitted to a smartphone application through a Bluetooth module and output, and can be output to an OLED for users who are not easy to access the smartphone.This healthcare monitoring system can be applied to various groups and technologies.
ACO-HCO: Heuristic Performance Tuning Scheme for the Hadoop MapReduce Architecture

Hadoop MapReduce is a widely-used cloud computing technology for big data processing. However, the Hadoop configuration parameters settings can significantly change the execution performance. Manual adjustment of the Hadoop parameters will be a time consuming and difficult task. In this paper, we propose ACO-HCO, a Hadoop configuration tuning scheme for MapReduce applications. We use MapReduce applications job history records to generate specific job profiles. Based on these profiles, an objective function for execution time is constructed with gene expression programming algorithm by mining the correlation among the core Hadoop configuration parameters and input data size. Leveraging the objective function, an ACO-based configuration optimizer is able to heuristically search for the optimal configuration for a given application. Experimental results show that ACO-HCO enhances the performance of Hadoop significantly compared with the default configuration. Moreover, ACO-HCO performs better than heuristic approach and the cost-based model in Hadoop performance tuning.
Sequential Nonlinear Encoding; A Low Dimensional Regression Algorithm with Application to EEG based Driving Fatigue Detection

We introduce a novel Regularized Kernel Projection Pursuit Regression method which is a two-step nonlinearity encoding algorithm tailored for such very low dimensional problems as fatigue detection. This way, the data nonlinearity can be investigated from two different perspectives, first by transforming the data into a high dimensional intermediate space and then by using their spline estimations to the output variables which allows for a hierarchical unfolding of data. Experimental results on the SEED database shows an average RMSE value of 0.1080% and 0.1054% respectively for the temporal and posterior areas of the brain. Our method is also validated by conducting some experiments on Parkinson's disease prediction which further demonstrate the efficiency of our method for low-dimensional regression problems.Traditional off-the-shelf regression methods like SVR, KSVR, and GLM methods all require their link functions to be previously selected which limits their effectiveness for encoding the nonlinearity of a highly complex low dimensional data set. Moreover, conventional PPR does not deal with the very low dimensionality of data. This paper proposes a novel regression algorithm to address the encoding problem of a highly complex low dimensional data, which is usually encountered in bio-neurological prediction tasks like EEG based driving fatigue detection.
Data Analysis Time Series For Forecasting The Greenhouse Effect

&lt;span lang="EN-US"&gt;The greenhouse effect is a term used to describe the earth having a greenhouse effect where the sun's heat is trapped by the earth's atmosphere. This study aims to model the greenhouse effect and then predict the greenhouse effect in the coming period using the Autoregressive Integrated Moving Average (ARIMA) method. In this case, time series analysis and reference data for 31 months are used, from the period January 2017 - July 2019, the results of the ARIMA model that are suitable for forecasting the greenhouse effect are ARIMA (4.2.0) with Mean Square Error (MSE) of 161885&lt;/span&gt;
Data-Dependent Conditional Priors for Unsupervised Learning of Multimodal Data

One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for data generations. In this paper, we propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), with a two-level generative process for the observed data where continuous z and a discrete c variables are introduced in addition to the observed variables x. By learning data-dependent conditional priors, the new variational objective naturally encourages a better match between the posterior and prior conditionals, and the learning of the latent categories encoding the major source of variation of the original data in an unsupervised manner. Through sampling continuous latent code from the data-dependent conditional priors, we are able to generate new samples from the individual mixture components corresponding, to the multimodal structure over the original data. Moreover, we unify and analyse our objective under different independence assumptions for the joint distribution of the continuous and discrete latent variables. We provide an empirical evaluation on one synthetic dataset and three image datasets, FashionMNIST, MNIST, and Omniglot, illustrating the generative performance of our new model comparing to multiple baselines.
3D Reconstruction as a Form of Interpretation: Example of Excavation of the Medieval Peťuša Castle (Central Slovakia)

The paper presents a case study from the research of the High Medieval Peťuša Castle (Zvolen District) in Central Slovakia.Its aim is to present documentation and analytical procedures based on the application of 3D techniques in archaeology.During the archaeological excavation of the Peťuša Castle, several methods were used to document the castle hill, archaeological contexts and artefacts.The aim of the paper is to reference the possibilities of 3D visualization, which serves not only as an interesting presentation of archaeological results to the general public, but also for the work of the archaeologists themselves.The primary goal is to use 3D technology in the analysis and subsequent interpretation in a way that is not common in archaeological procedures.These methods are applied in the interpretation of the architecture of the castle, walking ground level and the surrounding environment.The main contribution is the clarification of spatial relations, visualization of the examined structures, and the opening of polemics within the interpretation levels.
Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling

Depth maps play an important role in the representation of 3D information.They are often simultaneously acquired with color images; however, their resolution is significantly lower than that of color images owing to hardware limitations.In this paper, we propose a novel approach to upsample depth maps by using geometric deformation instead of pixel value refinement, which is employed in a majority of existing methods.This approach, known as grid warping, displaces the position of blurred pixels around the edge towards the center of the edge.The displacement vector for warping is obtained from an analysis of the corresponding high-resolution color image.Furthermore, we propose an edge signal and displacement vector modeling for a more effective analysis.The experimental results show that the proposed method significantly improves the quantitative and visual performance, as compared to state-of-the-art methods.The source codes of the proposed method will be available at https://github.com/yym064/DeepGridWarp.
Marine Disaster Prediction Based on Mathematical Model

Wang, G.-A., 2020. Marine disaster prediction based on mathematical model. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 230-233 . Coconut Creek (Florida), ISSN 0749-0208.China is a large marine country with a long coastline, which is 18000 kilometers long. Therefore, marine disasters occur frequently in China's coastal areas. Since 1989 to 2017, marine disasters have caused serious economic losses to China, totaling about 346.8 billion yuan, and the number of deaths and missing persons has reached 7054. At the same time, with the aggravation of global warming, green tide, red tide, typhoon and other natural disasters gradually increase. With the increasing frequency of marine disasters, great economic losses have been caused in coastal areas of China. Marine disasters have many characteristics, which are open and dynamic. By analyzing and solving the causes of marine disasters, we can better predict disasters. Therefore, we need to predict marine disasters, which will better plan and use marine resources and reduce the loss of marine disasters. Firstly, this paper analyzes the basic characteristics of marine disasters. Then, this paper introduces the data model of SVM. Then, the paper forecasts the green tide disaster. Finally, some suggestions are put forward.
Mean Annual Weather Cycles of some Weather Variables over Warri, Delta State, Nigeria during 2009 to 2018

The importance of analysing the weather cycles of any region cannot be over emphasized considering the dynamic nature of weather and its undesirable impacts on the environment. This study examines the mean annual cyclic behaviour of some weather variables over Warri, Delta State, Nigeria using recent weather data from NiMet for a period of ten years (2009 to 2018). It was observed from the cyclic behaviours from the various weather variables for the ten years period under consideration, that the annual maximum and minimum temperature, vapour pressure and cloud amount cycles follow similar trends, likewise the undulation of the annual soil heat flux cycles. The annual rainfall, wind speed, maximum and minimum relative humidity cycles were found to fluctuate in different patterns. In periods of limited rainfall, relative humidity is at the threshold of ~ 50.0%. A roughly curvilinear fit was noticed between the annual rainfall and relative humidity. While, rainfall appears insensitive to relative humidity changes during the months with lower relative humidity, which tends to be linear during the months with higher relative humidity. Hence, as the rainfall amount increases, there is an increasing tendency towards a curvilinear relationship. The occurrence of the maximum in temperature preceding that of wind speed indicates that the prevalent winds over Warri could be thermally obsessed. The results obtained would assist in providing appropriate panacea to mitigating weather induced environmental hazards, thereby improving agriculture, efficient economic productivity and advance scientific research.
Coupling network vertex representation learning based on network embedding method

Network representation learning is a basic problem in network data analysis. By learning network representation vectors, network vertices can be represented more accurately. With the development of deep learning, embedding methods have been widely used for network vertex representation learning. Providing that network data have changed in terms of their scale and modality, the research focus gradually shifted from single network mining to coupling network mining. This paper first analyzes the research status of embedding methods for single networks and then compares their advantages and disadvantages. Furthermore, the paper presents a model called CWCNE for coupling network embedding. The random walk and training algorithms of the proposed model are improved to adapt to coupling network features. The validity of the proposed model was verified using social, academic, film, poetry, and work coupling network data. Good results were obtained on community detection, entity recognition, and label classification tasks.
SuVashantor: English to Bangla Machine Translation Systems

This paper presents the system description of Machine Translation (MT) systems for English-Bangla language pair.Our goal was to create two benchmark MT systems that produce a better quality translation and comparatively higher evaluation score than existing MT systems for English to Bangla.In our experiments, we implemented two baseline MT systems using both statistical and neural methods for the said language pair.Our phrase-based statistical model and 2-layer LSTM neural model were trained and evaluated with a large dataset that is carefully pre-processed and contains unique training data to avoid biases from the cross-validation and test data.We achieved the highest scoring BLEU for our experiments with these setups.Furthermore, we improved the performance of the neural model using pre-trained embedding and synthetic monolingual data which are cutting-edge technology for neural models.
Evaluation of Global Solar Radiation using Sunshine Hour, Temperature and Relative Humidity at Low Land Region of Nepal

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.