Reviewerly/exHarmony huggingface dataset:
Feature Fusion Recommendation Algorithm Based on Collaborative Filtering

In view of the different consumption behaviors and habits of users in different regions of a city, if the behavior data of users in the whole network are used for model training without distinction, some unique consumption habits of users in a certain region may be ignored. This paper proposes a feature fusion algorithm based on Collaborative Filtering. First, it makes full use of the mature regional data to learn a model with strong generalization ability, and then adds sparse data to conduct feature fusion, so as to recommend items that users with sparse features may buy more accurately. The validity of the collaborative filtering algorithm is proved by the order data of two different regions in a city.
Person Search via Deep Integrated Networks

This study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active research issue in the academic and industrial fields. Although various studies have been proposed, following the protocols of the person re-identification (ReID) benchmarks, most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. However, for person search, manual processing is unavailable in practical applications, thereby causing a gap between the ReID problem setting and practical applications. One fact is also ignored: an imperfect auto-detected bounding box or misalignment is inevitable. We design herein a framework for the practical surveillance scenarios in which the scene images are captured. For person search, detection is a necessary step before ReID, and previous studies have shown that the precision of detection results has an influence on person ReID. The detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module, instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network.
Photovoltaic Power Generation Forecast by Using Estimator Model and Kalman Filter

This paper presents an approach to forecast photovoltaic (PV) power generation by using Kalman filter and Auto Regressive Integrated Moving Average (ARIMA). This method is suitable for real time forecast with high resolution time step and we use it to forecast for five-minute time step in this paper. However, Kalman filter requires real time measurement data to adjust forecast value, hence we propose an estimator model to help this approach to perform reliable forecast even when real time measurement data is unavailable. The dataset for building estimator model is set of historical data of power generation from neighbor PV rooftops and distance between PV rooftops. We use ARIMA model to estimate transition matrix for running Kalman filter. The performance of the test is measured by the Root Mean Square Error (RMSE) and Skill Score (SS). The obtained result shows that ARIMA model has lower accuracy compared to Kalman filter and estimator model. The real time data estimation from the estimator model can be used in Kalman filter to forecast PV power generation with good accuracy.
Adult or Child: Recognizing through Touch Gestures on Smartphones

With the increasing popularity of smartphones and its audience including children as young as 2 year old, smartphones can be a hazard for young children in terms of health concerns, time wastage, viewing of inappropriate material and conversely children who are too young can be a threat to the smartphone as well e.g, causing battery drainage, making unwanted calls/text messages, doing physical damage etc. In order to protect the smartphone and children from each other, we require user identification on our devices so the device could perform certain functions for instance restricting adult content once a user is identified as a child. This paper is a user study that aims at detecting the touch patterns of adults and children. To this end we collected data from 60 people, 30 adults and 30 children while they were asked to perform the 6 basic tasks that are performed on touch devices to find the differences in the touch gestures of children from adults. We first perform an exploratory data analysis. We then model the problem as a supervised binary classification problem and use the data as input for different machine learning algorithms to find whether we can classify a user previously unknown to the machine as an adult or a child. Our work shows there are differences in touch gestures among children and adults which are sufficient for user group identification.
Design of a relief materials delivery system based on UAV

Abstract The quality and efficiency of traditional rescue work are seriously affected by the destruction of roads and road traffic. In view of these existing problems, the current work investigates the delivery of relief materials using an unmanned aerial vehicle (UAV). At present, studies investigating the delivery of emergency relief materials are few, the technology is not mature, the disadvantage is bigger. In this paper, the UAV material delivery system can realize the function of automatic navigation and automatic delivery of materials. The experimental results show that, in a large number of delivery tests, 97% of deliveries contained a delivery error within 4 m, 64% of deliveries contained a delivery error within 2 m. The average delivery error was 1.86 m. The results of the experiment show that the accuracy of the UAV material delivery system is high, and the test results can provide reference for the further study of the robot.
Deep Learning Image Analysis for Angular Measurements in Wind Tunnels

Wind tunnel tests are part of a thorough process of development and certification for tomorrow’s aircraft and launch vehicles. The flight conditions are accurately simulated with a model in a controlled environment which includes the proper setting of its orientation in pitch, roll and yaw. Onboard sensors using accelerometers and conventional optical techniques such as photogrammetry are regularly used to determine the orientation of the model. An alternative approach is herewith presented, consisting of image analysis based on Convolutional Neural Networks to predict the continuous orientation of wind tunnel models. Several training data sets were constructed using a) synthetic images and photographs of a generic airplane rotating in pitch only; b) synthetic perspective views of CAD models and photographs of a model airplane at different pitch, yaw, and roll angles; and c) wind tunnel pictures of a half-span model in wind-on conditions. Results show that an accuracy of ± 0.5° is easily achieved for each Euler angle prediction, and an accuracy of ± 0.1° was achieved for pitch predictions in wind tunnel environment. It also proved effective to use synthetic views of CAD models as a training data set. This allows large training data sets to be rapidly generated without having to obtain photographs of the actual model in the wind tunnel.
Extracting Phrases as Software Features From Overlapping Sentence Clusters in Product Descriptions

Extracting software features from the public product descriptions in the natural language is beneficial for developing new products. Because software features are often expressed in phrases, many approaches currently propose to define phrase patterns and extract phrases as features from product descriptions accordingly. However, there are often lots of noisy phrases extracted because public product descriptions are described freely by different designers and it is difficult to obtain accurate phrase patterns in practice. It is also not suitable to filter those noisy phrases according to frequencies because some important features may be infrequent. To address such issues, this paper proposes a feature extraction approach by extracting phrases as features from the sentence clusters among product descriptions rather than directly from the product descriptions. Considering that more than one feature can be described in one sentence, a new algorithm is designed to detect the overlapping sentence clusters from public product descriptions. It can detect all potential sentence clusters and reduce the affection of noisy descriptions. By taking bigram collocations as the phrase pattern, the bigram collocations containing cluster keywords are elicited as features from each detected sentence cluster. The evaluations conducted on the public software product descriptions from the application market of Softpedia.com, have shown that the proposed approach has better performance than the competitive approaches in terms of precision and time consumption.
Method for Searching of an Optimal Scenario of Impact in Cognitive Maps During Information Operations Recognition

In this paper, we consider cognitive maps as an additional tool for building a knowledge base of the DSS. Here we present the problem of choosing the optimal scenario of the impact between nodes in the cognitive maps based on of the introduced criteria for the optimality of the impact. Two criteria for the optimality of the impact, which are called the force of impact and the speed of implementation of the scenario, are considered. To obtain a unique solution of the problem, a multi-criterial assessment of the received scenarios using the Pareto principle was applied. Based on the criteria of a force of impact and the speed of implementation of the scenario, the choice of the optimal scenario of impact was justified. The results and advantages of the proposed approach in comparison with the Kosko model are presented. Also we calculate rank distribution of nodes according to the degree of their impact on each other to reveal key and the most influential components of the cognitive map that corresponds some subject domain.
Text Readability Assessment for Chinese Second Language Teaching

This paper proposes a multi-type and multi-granularity text readability feature set for Chinese second language teaching, which takes into account the dynamic and static features of the texts and integrates three linguistic units: character, word and sentence. On this basis, this paper analyses and compares various text readability assessment methods, and discusses how to effectively use various features for text readability assessment.
eRoc: A Distributed Blockchain System with Fast Consensus

A consensus mechanism is one of the most important techniques in blockchain systems. However, existing consensus mechanisms have the problems of high latency and tending to centralization. To address these issues, we present a consensus algorithm eRoc with decentralization property. In eRoc, a verifiable random function (VRF) is employed to achieve cryptographic sortition such that a certain amount of blocks and votes will be broadcasted in the system. And the blocks and votes will be verified by all users. Thus, in each round all blocks are generated in a distributed way, and each block must be voted by most voters to become a consensus block. We propose a generic eRoc construction, analyze its security and liveness, and compare with related schemes. We also develop an eRoc prototype in C++ to evaluate its performance. The experimental results demonstrate that eRoc can reach consensus in 13 seconds for 30 blocks of 2M size, where the numbers of nodes, expected votes of each block, and threshold votes of a consensus block are 10k, 500, and 200, respectively.
Segment-Search vs Knowledge Graphs: Making a Key-Word Search Engine for Web Documents

It is becoming increasingly popular to publish data on the web in the form of documents. Segment-search is a semantic search engine for web documents. It presents a query language. It is suitable for skilled and semi-skilled domain experts, who are adept at the use of a specific collection of documents. It returns suitable documents selected by using document fragments, that satisfy user's query. In contrast to knowledge graph approach, the technique is based on performing web page segmentation as per user perceived objects. Thus, it allows users' to query without the knowledge of complex query languages or learning about the data organization schemes. The proposed system is scalable and can cater to large scale web document sources.
Research on Gaussian-wavelet-type Activation Function of Neural Network Hidden Layer Based on Monte Carlo Method

Artificial neural networks have developed rapidly in recent years and have been applied in the fields of image recognition, natural language processing, and pattern recognition. The activation function, as an integral part of the neural network, plays a huge role in the neural network. The appropriateness of the activation function determines the accuracy of the neural network results. In this paper, a Monte Carlo method combined with the Gaussian-wavelet-type activation function to design a neural network and apply it to the image classification of convolutional neural networks. The Gaussian-wavelet-type activation function and the Monte Carlo method are combined to select the most suitable activation function to ensure the stability of the whole training and improve the accuracy of the classification results on the data set.
Positive Deviance Against Malaria Events in Majene District

Essentially behavioral factors are actions or activities of human beings themselves which results in an imbalance between agent, host and environment that affects human health status, including the incidence of malaria caused by one of the factors which is good human activity that can be observed directly or indirectly. So this study aims to determine the effect of positive deviance on malaria incidence in Majene District. This research was conducted in Manyamba Village, Majene Regency, West Sulawesi Province, July-September 2018. This study used a mixed-method research design. Quantitative data is used to explain the relationship between the variables of the study and the incidence of malaria and qualitative data is used to determine positive deviance behavior using the focus group discussion method. The data obtained will be processed using SPSS V. 24. Based on the data obtained in the field there are several variables related to the incidence of malaria, namely positive deviance (p = 0.039), knowledge (p = 0.018) and work (p = 0.035). Multivariate analysis to see the effect on malaria, obtained results that the job variable has the strongest relationship to the incidence of malaria, with a value of p = 0.001. The results of the data obtained from the FGD obtained that there were 4 positive deviance behaviors in the community of Manyamba Village, Tamerodo District, Majene District; plant the toga, keep the lead head fish, burn the egg rack and bring it to the paranormal. Positive deviance behavior in the community can be used to prevent the occurrence of malaria through the active role of health cadres by forming healthy communities in the community.
Blockchain Enabled Cyber-Physical Society Framework

Blockchain is a disrupting technology that is transforming the technological ecosystem that drives and governs the transactions. Establishing the distributed trust in the cyber-physical society without any intermediaries has the potential to impact and change many industries. This paper focuses on the possibility of blockchain in transforming cyber-physical society and proposes a blockchain enabled cyber-physical society framework. The framework will help various stakeholders to develop distributed and decentralized trust-based cyber-physical society.
A methodology for sender-oriented anti-spamming

A massive number of daily spam demands a method of anti-spam filtering that can efficiently detect unsolicited emails. Traditional spam filtering techniques block spam at receiver's side, unable to stop their propagation on the network, resulting in wastage of network bandwidth, processing power, a nd disk space. To address these issues, this paper proposes a methodology for sender oriented anti-spamming that identifies and filters spam at its origin, thus not only discourages spam but also reduces the resource wastage. To the best of our knowledge, addressing this wastage by spam has not been done before. Along with blocking spam at origin, this research proposes a diagonal hash algorithm to optimize image spam filtering, and email hash token generator to promote email authentication and discourage zombie accounts. Experiments suggest that the proposed technique has the potential to achieve better performance and accuracy compared to the current approaches.
Automatic Identifying Interaction Components in Collaborative Cyber-Physical Systems

Due to diverse set of heterogeneous computing devices communicating with one another and fusing with physical components in Cyber-Physical Systems, software engineers may use different tools and/or modeling languages to formally describe or verify the system properties. As a result, the integration of these diverse constituents poses key challenges such as task for identifying interactions of components to be synthesized for a function in the systems. Although existing studies such as ontology and integration semantic languages have been used for specifying interactions of components in a Cyber-Physical System, these are still not applicable to discover the component interactions in collaborative Cyber-Physical Systems. It is due to the fact that functionalities of Cyber-Physical Systems are generally realized through interactions among multiple systems in a collaborative environment. This paper proposes a model interaction language, CyPhyML+ which can identify component interactions of realized functions in collaborative Cyber-Physical Systems. We show the proposed approach validity and applicability via an Automatic Incident Detection System.
Integration of Crowdsourcing into Ontology Relation Extraction

Ontology learning is a continuous process that is always being researched and developed. A learning method for one domain may not be applicable to another because of the different characteristics of the data involved. Researchers have been developing various methodologies to build the highest quality of ontology efficiently. As identified in the previous works, one problem which could not be solved my machine alone is the extra-logical errors. These errors can only be identified by human judges and are usually related to the domain of the ontology. In this research, we aim to catalogue available methods, specifically for relation extraction, and the online incremental algorithms which will allow integration of crowdsourcing into ontology learning—to handle said challenge. We also briefly discussed an existing ontology editor called OntoCop, which may be used as a reference for further research. Henceforth, we propose a framework based on our review to improve the current relation extraction method.
Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases

Spatial High Utility Itemset Mining (SHUIM) aims to discover all itemsets in a spatiotemporal database that satisfy the user-specified minimum utility (minUtil) and maximum distance (maxDist) constraints. The popular adoption and successful industrial application of SHUIM suffers from the following two limitations: (i) Since SHUIM determines the interestingness of an itemset without taking into account its support within the data, SHUIM facilitates sporadic itemsets with high utility to be generated as SHUIs. In particular, items in long transactions can combine with each other and be generated as SHUIs. (ii) SHUIM is a computationally expensive process because the generated itemsets do not satisfy the downward closure property. This paper introduces Spatial High Utility Frequent Itemset Mining (SHUFIM) to address these two issues. A SHUI in a spatiotemporal database is said to be a SHUFI if its support is no less than the user-specified minimum support (minSup) constraint. The usage of minSup not only facilitates the proposed model to be tolerant to the long transactions within the data but also facilitates us to employ additional pruning techniques to reduce the computational cost. A single scan fast algorithm has also been proposed to discover all SHUFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also demonstrate the usefulness of the proposed model with a real-world application.
Research on Image Matching in Printing Defects Detection Based on Machine Vision

A new similarity algorithm based on image difference and template matching is proposed to solve the problem of real-time matching between the printing image and the template image based on the similarity algorithm in online printing detection. According to this similarity, besides judging whether the printing image matches with the template, whether the printing image is defective or not, it can also judge whether the printing image is offset or deflected relative to the template. If there is a deflection, Hough transform based on mathematical morphology is used to calculate the deflection angle of the printing image relative to the template image, and then the printing image is corrected according to the deflection angle. If there is an offset, the offset of the printing image relative to the template image is calculated according to the principle of phase consistency, and then the printing image is corrected according to the offset. At the end of this paper, the comparative experiments with the similarity-based image matching method are carried out. The experimental results have verified the superiority of the image matching based on the similarity which is proposed in this paper. In addition, the experiments on the offset and deflection of the printing image relative to template image have been respectively carried out. The results verify the feasibility and validity of the proposed method in the paper.
Combined Approach to Detect Anomalies in Health Care Datasets

Big data characterizing the quality of medical health care services provided to the population contain anomaly observations, which are either results of technical errors in filling databases, or falsified data. In the context of insurance medicine, the actual task is to identify such observation. The paper presents an approach to identify outliers in the database of medical health care information system based on the combination of Isolating Forest algorithm and subsequent neural network classifier.