Reviewerly/exHarmony huggingface dataset:
Combination of WRF Model and LSTM Network for Solar Radiation Forecasting—Timor Leste Case Study

A study of a combination of Weather Research and Forecasting (WRF) model and Long Short Term Memory (LSTM) network for location in Dili Timor Leste is introduced in this paper.One calendar year's results of solar radiation from January to December 2014 are used as input data to estimate future forecasting of solar radiation using the LSTM network for three months period.The WRF model version 3.9.1 is used to simulate one year's solar radiation in horizontal resolution low scale for nesting domain 1 × 1 km.It is done by applying 6-hourly interval 1˚ × 1˚ NCEP FNL analysis data used as Global Forecast System (GFS).LSTM network is applied for forecasting in numerous learning problems for solar radiation forecasting.LSTM network uses two-layer LSTM architecture of 512 hidden neurons coupled with a dense output layer with linear as the model activation to predict with time steps are configured to 50 and the number of features is 1.The maximum epoch is set to 325 with batch size 300 and the validation split is 0.09.The results demonstrate that the combination of these two methods can successfully predict solar radiation where four error metrics of mean bias error (MBE), root mean square error (RMSE), normalized MBE (nMBE), and normalized RMSE (nRMSE) perform small error distribution and percentage in three months prediction where the error percentage is obtained below the 20% for nMBE and nRMSE.Meanwhile, the error distribution of RMSE is obtained below 200 W/m 2 and maximum bias error is 0.07.Finally, the values of MBE, RMSE, nMBE, and nRMSE conclude that the good performance of the combination of two methods in this study can be applied to simulate any other weather variable for local necessary.
Human scanpath estimation based on semantic segmentation guided by common eye fixation behaviors

To explore the dynamic process of complex human eye movement behavior, we proposed a new model to simulate human scanpath when subjects observed natural images freely. Previous methods almost focused on finding effective and advanced technology, such as machine learning or deep learning, for estimating human scanpath. In contrast, our proposed method devoted to find a new way that could use the intrinsic property of eye-tracking data between different races to guide the design of a deep network. Inspired by that, the model of human scanpath estimation was established, which based on a semantic segmentation module guided by common eye fixation behaviors between people with different cultures. The semantic segmentation module could deal with locating fixations positions and the fixations ranking problem in parallel and generate human scanpath combined with the output of common attention portions (CAP) generator. The common attention portions (CAP) generator was designed to optimize the performance of semantic segmentation module and extract the common eye fixation behaviors between people with different cultures. We evaluated the performance of our model on three public eye-tracking datasets by comparing the result generated from our model with the ground truth of scanpath produced by a new method in this work. The proposed model also achieved the encouraging performance compared with some classic and fashionable models.
A novel teaching and training system for industrial applications based on augmented reality

Along with the high demand on automation, more industrial robot manipulators are being introduced to factories. In addition to their manufacturing, it is also very crucial on the deployment and training of the robots. It thus solicits the development of new types of manipulative devices and methods for tackling these tasks of high complexity. To achieve effective task execution, this paper proposes a novel AR-based teaching and training system, along with several assistive strategies. The technique of augmented reality (AR), which combines virtual objects with real scenes, is used for providing visual immersion. The assistive strategies, including that of collision-free path planning, occlusion effect removal, and visual guidance, are designed to ease the user from the load during the complicated teaching process. We conduct a series of experiments for performance evaluation. Questionnaires based on the system usability scale are also administered to obtain feedback from users.
Application of Sentiment Analysis on Product Review E-Commerce

The lack of buying product via online are the consumer cannot touch, try, or even see it directly. Then how does the consumer believe the product they like, is the correct product to be bought. The main key is product review from the consumer who have bought and try the product. The more number of product review on certain product or the popular product caused difficulties for the consumer to decide which product they should choose. For that we require a solution, which is the writer will apply sentiment analysis to classify every product review into positive orientation, negative or neutral and also produce summary of product review based on product feature to help reading process, product review and decision making. The writer step is (1) data collecting and preprocessing, (2) product feature extraction using Double Propagation, (3) deciding sentiment orientation, (4) classifying by using Naïve Bayes Classifier and Support Vector Machine, and (5) summary generation based on product feature. These stages run in a simulator and the result of the classification from both methods compared.
Privacy-Preserving and Efficient Sharing of Drone Videos in Public Safety Scenarios using Proxy Re-encryption

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.
An OpenCV-based Framework for Table Information Extraction

Portable Document Format (PDF), as one of the most popular file format, is especially useful for educational documents such as text books, articles, or papers in which we can preserve the original graphic appearance and conveniently share online. Detecting and extracting information from tables in PDF files can provide a plethora of structural data to construct educational knowledge graphs. However, most of the existing methods rely on PDF parsing tools and natural language processing techniques, which generally require training samples and are frail in handling cross-page tables. In light of this, in this paper, we propose a novel OpenCV-based framework to extract the metadata and specific values from PDF tables. Specifically, we first highlight the visual outline of the tables. Then, we locate tables using horizontal and vertical lines and get the coordinates of tabular frames in each PDF page. Once the tables are successfully detected, for each table, we detect the cross-page scenarios and use the Optical Character Recognition (OCR) engine to extract the specific values in each table cell. Differing from other machine learning based methods, the proposed method can achieve table information extraction accurately without labeled data. We conduct extensive experiments on real-world PDF files. The results demonstrate that our approach can effectively deal with cross-page tables and only need 6.12 seconds on average to process a table.
Access control encryption without sanitizers for Internet of Energy

With the increasing popularity of Internet of Energy (IoE), more and more physical devices connected to IoE depend on the information system for energy control and coordination. Under this condition, how to achieve information flow control in IoE becomes a great challenge. Access control encryption (ACE) is a promising technology to address the problem. However, existing ACE requires a centralized sanitizer, hindering its successful application in IoE. In this paper, we construct a new kind of ACE without sanitizers for IoE. We first construct a basic ACE scheme based on number-theoretic assumptions (i.e., DBDH assumption), and this scheme can control not only what users can read but also what they can write. To resist against the quantum attacks, we further construct a more secure ACE scheme based on learning with errors (LWE). We formally prove that our proposed two ACE schemes are secure under the proposed security definition, and we also evaluate the applicability and the efficiency of them in experiments.
Gait Patterns Monitoring Using Instrumented Forearm Crutches

Crutches are one of the most common assistive devices used in rehabilitation for lower limbs. Improper use of them results in extended recovery periods and even cause damage and pain to the limb. Many existing studies demonstrated that correctly using crutches requires an understanding of the disability or injury of the patient, gait patterns as well as user and crutch interaction during rehabilitation. In this work, a prototype was developed to monitor in real-time the exerted axial force and the tilt angles involved in each gait cycle at a prescribed gait pattern. The prototype is composed for an instrumented forearm crutch with a wireless measurement system. Four gait patterns were tested experimentally in three healthy users. Promising results were obtained that induces the possibility to identify automatically the performed pattern and even typical errors while using forearm crutches. The proposed system opens up a valid alternative to individualize therapy by monitoring the user gait using crutches in the rehabilitation process.
Intelligent Instrument Recognition Scheme Based on Unattended Substation Inspection

As the level of automation improves, unattended solutions become possible. Based on the substations with greater demand for intelligent inspection, this paper proposes a new instrument intelligent identification scheme, which has the following innovations: 1. Based on the structure of the inspection robot and the image acquisition process, this paper designs a digital substation pointer reading method based on digital image processing technology. 2. Analyze and study the methods of image graying, image smoothing, edge detection and line detection, especially the detailed research on Canny edge detection algorithm. 3. Combined with the unmanned substation patrol inspection practice, in view of the interference of complex lighting to the reading of the instrument, this paper proposes an adaptive Gamma enhancement algorithm based on Retinex theory. The details and colors of the image are binarized using a double threshold algorithm. The pointer instrument reading system designed in this paper has an average accuracy rate of 97%, which meets the actual requirements of inspection.
Upgradeability Concept for Collaborative Blockchain-Based Business Process Execution Framework

Inter-organizational business processes involve different independent participants to interact with each other to run a collaborative business process. To date, central, trusted third parties mediate between non-trusting participants adding additional process complexity as well as administrative and run costs. Recent research showed that Blockchain and Smart Contracts can replace the role of a central trusted authority in collaborative execution of processes. Smart Contracts, that represent business process logic, cannot be altered due to immutability constraints of Blockchain systems. Yet, with the help of advanced technical upgradeability concepts, upgrading contracts, therefore enabling versioning of processes on a Blockchain is possible. This paper analyzes and implements three different upgradeability concepts. The implemented patterns are evaluated with regard to an existing blockchain-based execution framework for inter-organizational business processes. Our findings suggest the Unstructured Storage Proxy pattern to be the most promising for practical use, especially regarding cost-effectiveness and minimal added complexity. The findings are derived from simulations on a real-world use case stemming from a large German electronics manufacturing company.
Semantic Embeddings for Medical Providers and Fraud Detection

A medical provider's specialty is a significant predictor for detecting fraudulent providers with machine learning algorithms. When the specialty variable is encoded using a one-hot representation, however, models are subjected to sparse and uninformative feature vectors. We explore three techniques for representing medical provider types with dense, semantic embeddings that capture specialty similarities. The first two methods (GloVe and Med-Word2Vec) use pre-trained word embeddings to convert provider specialty descriptions to short phrase embeddings. Next, we propose a method for constructing semantic provider type embeddings from the procedure-level activity within each specialty group. For each embedding technique, we use Principal Component Analysis to compare the performance of embedding sizes between 32-128. Each embedding technique is evaluated on a highly imbalanced Medicare fraud prediction task using Logistic Regression (LR), Random Forest (RF), Gradient Boosted Tree (GBT), and Multilayer Perceptron (MLP) learners. Experiments are repeated 30 times and confidence intervals show that all three semantic embeddings significantly outperform one-hot representations when using RF and GBT learners. Our contributions include a novel method for embedding medical specialties from procedure codes and a comparison of three semantic embedding techniques for Medicare fraud detection.
Bayesian Learning of Occupancy Grids

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.
Privacy, adoption, and truthful reporting: A simple theory of contact tracing applications

This paper analyzes the trade-offs associated with the deployment of contact tracing applications to support policy responses in a pandemic. In many jurisdictions, the government cannot force individuals to adopt such applications. We therefore analyze a simple model that highlights the importance of individuals’ incentives to voluntarily adopt a reporting application and reveal their infection status to the government who can then undertake contact monitoring. We discuss the consequences of various policy options, such as security, communication and anonymization policies, in terms of the size and representativeness of the sample of infection data that contract tracing applications generate.
Emotion Analysis and Classification of Movie Reviews Using Data Mining

This paper proposes a model for classification of movie reviews by using Data Mining. The paper also proposes the method of creating word cloud from word frequency in movie reviews, for the purpose of partially helping in analyzing for interested topic and opinion of reviewer. The research uses movie review data from Metacritic website. The review data consists of reviews from 21 movies, separated into two parts to be used as training set and test set. Training set have 462 reviews and test set have 238 reviews. The data preparation process started collecting review data by removing special symbols case and preprocessing into Weka program. Change the review text into structured data by using StringToWordVector filter. This process includes removing stop words with Rainbow stop words list, change word that have the same root origin into word stem by using Snowball Stemmer algorithm and then given weight value by using TF-IDF technique. After that, Naïve bayes, Random Forest and J48 algorithms were used to classify the review data into positive and negative groups. The experimental result given is 80.25%, 79.83% and 68.06%, respectively.
TransP: A New Knowledge Graph Embedding Model by Translating on Positions

Embedding knowledge graph into continuous space(s) is attracting more and more research attention, and lots of novel methods have been proposed. Among them, translation based methods achieved state-of-the-art experimental results. However, most of existing work ignore following two facts. First, once a relation is fixed, its linked head and tail entities will be fixed to a certain extent. Second, in a triplet, if one of its entities and the relation are fixed, the other entity's candidates will also be fixed to a certain extent. Taking these two facts into consideration, we propose a new knowledge graph embedding model named TransP, which defines a head entity space and a tail entity space for each relation. During embedding, TransP first projects entities into these two position spaces. Then the entities in these two position spaces are further projected into a common transformation space, in which the relation is converted into two transformation matrices. A symmetrical score function is designed to connect a correct triplet's head and tail entity in the common space. The basic idea behind this score function is that if a correct triplet holds, its head (tail) entity should be able to be converted into its tail (head) entity when taking the relation's transformation matrix as an intermediate bridge. Viewing the transformation matrices as decoders, this process is just like a common translation process. We evaluate TransP on triplet classification task and link prediction task. Extensive experiments show that TransP achieves much better performance than other baseline models.
Decentralized patient-centric data management for sharing IoT data streams

Analytics over data streams from Internet of Things (IoT) devices have become valuable information sources of user data, benefiting both healthcare service providers and patients. Current approaches of connecting IoT devices directly to central cloud architectures come with a number of disadvantages. One of the main disadvantages is that patient health data cannot be easily shared among different healthcare applications or service providers, limitating the full potential of data-driven analysis over healthcare data streams. In this paper, we present a new decentralized and permissionless data management system which empowers patients to securely and selectively share their own personal data among other patients or healthcare service providers. We depart from current decentralized data management approaches that often involve high transaction fees, scalability problems or a high computational overhead that are not acceptable for resource-constrained IoT devices. The contribution of our work lies in coupling the IOTA Tangle technology as auditable and distributed data storage of the patients encrypted time-series IoT data streams with an efficient key management scheme in order to define fine-grained stream-specific access policies. Based on a reference implementation, different experimental tests were made to highlight the feasibility and applicability of our decentralized data management system for end-to-end encrypted IoT data streams.
Consensus in Wireless Blockchain System

In recent years, blockchain system has attracted more and more attention from people around the world, not only due to its popular application: Bitcoin [8], but also because it is an important building block in city Internet-of-Things. In this paper, we study the consensus problem in wireless blockchain system. As the first one considering the challenges of message transmission in wireless blockchain system, we propose an optimal consensus protocol for the devices in wireless blockchain system, by executed which all devices can achieve a consensus within $$O(\log n)$$ time steps with high probability. We believe that this consensus protocol can facilitate many fundamental operations in wireless blockchain system, and make the smart city system more efficient and robust.
Design of a novel quantum neural network

With the overwhelming success in the field of quantum computing, much attention has been paid to constructing a quantum neural network by combining a classical neural network with quantum computing. In this paper, we propose a novel quantum neural network model based on a quantum version of the sigmoid function, which skillfully combines the non-linear dissipation dynamics of neural computation with the linear unitary dynamics of quantum computation. Moreover, we also add connections from the input layer to the output layer to increase the non-linear expression ability of the network and the similarity to the human brain's information processing. The specific steps and relevant formulas of the conjugate gradient algorithm in the learning stage of the quantum network parameters are also given in this paper. Finally, the feasibility and properties of the model are demonstrated by MATLAB simulation with a encryption and decryption experiment.
Cross-MapReduce: Data transfer reduction in geo-distributed MapReduce

The MapReduce model is widely used to store and process big data in a distributed manner. MapReduce was originally developed for a single tightly coupled cluster of computers. Approaches such as Hierarchical and Geo-Hadoop are designed to address geo-distributed MapReduce processing. However, these methods still suffer from high inter-cluster data transfer over the Internet, which is prohibitive for processing today’s globally big data. In line with our thinking that there is no need to transfer the entire intermediate results to a single global reducer, we propose Cross-MapReduce, a framework for geo-distributed MapReduce processing. Before any massive data transfer, our proposed method finds a set of best global reducers to minimize transferred data volumes. We propose a graph called Global Reduction Graph (GRG) to determine the number and the locations of the global reducers. We conducted extensive experimental evaluations using a real testbed to demonstrate the effectiveness of Cross-MapReduce. The experimental results show that Cross-MapReduce significantly outperforms the Hierarchical and Geo-Hadoop approaches and reduces the amount of data transfer over the Internet by 40%.