Reviewerly/exHarmony huggingface dataset:
Training of Multiple and Mixed Tasks with a Single Network Using Feature Modulation

In recent years, multi-task learning (MTL) for image translation tasks has been actively explored. For MTL image translation, a network consisting of a shared encoder and multiple task-specific decoders is commonly used. In this case, half parts of the network are task-specific, which brings a significant increase in the number of parameters when the number of tasks increases. Therefore, task-specific parts should be as small as possible. In this paper, we propose a method for MTL image translation using a single network with negligibly small task-specific parts, in which we share not only the encoder part but also the decoder part. In the proposed method, activation signals are adjusted for each task using Feature-wise Linear Modulation (FiLM) which performs affine transformation based on task conditional signals. In addition, we tried to let a single network learn mixing of heterogeneous tasks such as a mix of semantic segmentation and style transfer. With several experiments, we demonstrate that a single network is able to learn heterogeneous image translation tasks and their mixed tasks by following our proposed method. In addition, despite its small model size, our network achieves better performance than some of the latest baselines in most of the individual tasks.
A Feature-Cascaded Correntropy LSTM for Tourists Prediction

Forecasting the number of tourists is significant to public safety, which can enable the government to control the sudden influx of tourists timely. The temporal dependence (closeness and period), external factors such as holidays, government policy, as well as outliers in real data, make the prediction challenging. Our data obtain a mixture of short-term contact and long-term repeating patterns and external factors, for which Autoregressive, Exponential Smoothing models and Extreme Learning Machine may fail. In our paper, we propose a novel Feature-Cascaded framework with Correntropy criterion for Long Short-Term Memory network (FC-C-LSTM). For temporal dependence and external factors, we first extract feature information from data in close dates and corresponding period, then integrate them as an independent input of LSTM to simulate temporal pattern and solve the problem of time-lag. In view of an important and unavoidable feature of real datasets, there are amounts of outliers. We adopt the correntropy of Gaussian kernel instead of mean square error as cost function, so that outliers get smaller weights and suppress the influence of outliers back-propagation. Experiments on real tourism datasets of several cities in Yunnan Province show that FC-C-LSTM model achieves the better performance than that of other baselines.
Deep Neural Network Incorporating CNN and MF for Item-Based Fashion Recommendation

In fashion electronic commerce services, two item-based recommendation approaches, image similarity-based and click likelihood-based, are used to improve the revenue of a website. To improve accuracy, in this paper, we propose a hybrid model, a deep neural network (DNN) that predicts click probability of a target fashion item by incorporating both image similarity and click likelihood. To create an image similarity feature, we acquire a latent image feature through a CNN-based classification of fashion color, type and pattern. To create a click likelihood feature, we calculate matrix factorization (MF) and use decomposed item features as latent click log feature. To solve a cold-start problem (recommendation of new items), we complement the latent log features of new items with those of existing ones. An offline evaluation shows that the accuracy of proposed model (both log and image) improved by 14% compared with matrix factorization (log only) and 56% the image-only model. Moreover, the complement of latent log features changes the new item ratio to six times.
An Advanced Framework for Critical Infrastructure Protection Using Computer Vision Technologies

Over the past decade, there has been unprecedented advancements in the field of computer vision by adopting AI-based solutions. In particular, cutting edge computer vision technology based on deep-learning approaches has been deployed with an extraordinary degree of success. The ability to extract semantic concepts from continuous processing of video stream in real-time has led to the investigation of such solutions to enhance the operational security of critical infrastructure against intruders. Despite the success of computer vision technologies validated in a laboratory environment, there still exists several challenges that limit the deployment of these solutions in operational environment. Addressing these challenges, the paper presents a framework that integrates three main computer vision technologies namely (i) person detection; (ii) person re-identification and (iii) face recognition to enhance the operational security of critical infrastructure perimeter. The novelty of the proposed framework relies on the integration of key technical innovations that satisfies the operational requirements of critical infrastructure in using computer vision technologies. One such requirement relates to data privacy and citizen rights, following the implementation of General Data Protection Regulation across Europe for the successful adoption of video surveillance for infrastructure security. The video analytics solution proposed in the paper integrates privacy preserving technologies, high-level rule engine for threat identification and a knowledge model for escalating threat categorises to human operator. The various components of the proposed framework has been validated using commercially available graphical processing units for detecting intruders. The performance o the proposed framework has been evaluated in operational environments of the critical infrastructure. An overall accuracy of 97% is observed in generating alerts against malicious intruders.
WITHDRAWN: Hybrid facial test for encephalopathy

Abstract The face is the reader of mind. One can read the mindset and mood of a person from his facial expression. The existing system assesses the criminal mental disorders manually. But the method does have assessment errors for the system cannot predict accurate solutions at all times because the human mood varies from time to time. An effective technique for criminal rehabilitation is proposed. The proposed method involves PEN test and IQ test. The human emotion and mood of a person subjected to the above two tests is monitored at constant intervals. Image processing techniques implementing Raspberry Pi with convolution neural network is implemented for obtaining the human mental state.
Toward Explanation-Centered Story Generation

This study proposes the need for explanation-centered story generation, with the objective of extending the method of doing so. Explanation-Centered Story Generation is a concept that generates a story from an explanation, offering a mechanism for the story generation system to generate multiple forms of stories in this manner.
An approach to the development of decision support systems with knowledge-driven automated service composition

Abstract The development of a decision support system typically requires a significant effort from both domain modelling and technical perspectives. The paper proposes an approach for reducing the complexity of decision support system development leveraging automated service composition. The rationale behind the approach is that today many software systems (including decision support systems) are based on service-oriented architecture and to some extent the development of such systems can be represented as building composition of services satisfying the specified requirements. The problem of building such compositions can be successfully addressed by automated planning algorithms. Particularly, the paper presents the functional framework of decision support systems, requirements analysis for configurable service-oriented decision support systems and their main components, and a conceptual model of a configurable service-oriented decision support system.
User Abnormal Viewing Behavior Detection Method Based On Canopy-FCM and Improved Isolation Forest Algorithm

Abstract In order to analyze the users’ viewing behavior more accurately, allow IPTV service providers to provide better services to users, and improve the user’s viewing experience, this paper proposes a detection method of abnormal user viewing behavior based on Canopy-FCM and improved isolation forest algorithm. First, the collected viewership data is preprocessed and stored with the user viewing behavior matrix, then the Canopy-FCM clustering algorithm is used to classify users with the same rating behavior. Then, an improved isolated forest algorithm is used to perform anomaly detection and analysis on the users classified above, and finally screen out abnormal user viewing behaviors. Finally, NMI indicators and ROC curves are used to evaluate the performance of the proposed algorithm to verify the feasibility of the proposed anomaly detection method. The results show that the method proposed in this paper has a high precision and recall rate for detecting users with abnormal ratings.
Image Cryptography using Multichannel Filterbank

In this paper, we present a multichannel filter bank based approach for image cryptography. The proposed method consists of designing a uniform multichannel filterbank using translation of the prototype filter and use the designed filterbank for image encryption with new methodology. This method is based on designing multiband analysis filterbank by translating the prototype filter followed by the design of complementary multiband synthesis filterbank. The designed filterbank has various user defined parameters (key) like, prototype lowpass filter, number of bands in the spectrum and level of decomposition. Based on the selection of these parameters, we can design a unique filterbank which can be used for the image encryption. At the decryption end, the original image can be reconstructed only if user selected parameters are known. We present few design examples for the illustration purpose. We give details of complexity analysis of the entire cryptography system and also present time analysis. The proposed approach provides more number of user parameters as compared to state-of-art methods which can be used as secure key. It is also observed that the computation time of the proposed approach is comparable.
Software Environment Modelling Method

Abstract The influence of software environment and its changes on software functions and performance is becoming more and more important. Through literature investigation, this paper studies the concept, composition and characteristics of software environment, and analyzes the relationship between software environment and software requirements. This paper also presents the environment modelling method based on feature model and ontology model, and proposes the procedure and the important steps, together with the differences between the two models. Finally, case studies are given for each model.
Research on fast Matching of Moving objects in Internet of Things based on machine vision sampling

In order to improve the matching speed of moving objects in the Internet of Things, a fast matching method based on machine vision sampling is designed. Firstly, machine vision sampling method is adopted to acquire moving target image, and then image preprocessing is carried out. Finally, image background segmentation and dynamic background moving target detection are performed to realize fast matching of moving target in the Internet of Things. The experimental results show that the rapid matching method based on machine vision sampling for moving objects of Internet of Things in this study has higher matching speed and accuracy than the traditional method, and has practical application significance.
Survey on Interpretable Semantic Textual Similarity and its Applications

Both semantic representation and related natural language processing(NLP) tasks has become more popular due to the introduction of distributional semantics. Semantic textual similarity (STS)is one of a task in NLP, it determinesthe similarity based onthe meanings of two shorttexts (sentences). Interpretable STS is the way of giving explanation to semantic similarity between short texts. Giving interpretation is indeedpossible tohuman, but, constructing computational modelsthat explain as human level is challenging. The interpretable STS task give output in natural way with a continuous value on the scale from [0, 5] that represents the strength of semantic relation between pair sentences, where 0 is no similarity and 5 is complete similarity. This paper review all available methods were used in interpretable STS computation, classify them, specifyan existing limitations, and finally give directions for future work. This paper is organized the survey into nine sections as follows: firstly introduction at glance, then chunking techniques and available tools, the next one is rule based approach, the fourth section focus on machine learning approach, after that about works done via neural network, and the finally hybrid approach concerned. Application of interpretable STS, conclusion and future direction is also part of this paper.
Sociodemographic Determinant of Internet Use and its Impact on Family Planning Behavior Among Young Male in Ethiopia: Evidence from EDHS 2016

Background: Internet was preferred source of health information to retrieve relevant information. Internet was preferred source of health information retrieve relevant information.Objective: The study investigates sciodeterminant determinant of internet and its impact on family planning behavior among young male in Ethiopia, 2016.MethodsThe study was based on data from Ethiopia health and demographic survey 2016. The sample size was 4550 male youth age between 15-24 years include in EDHS 2016. Multilevel logistic regression was used to determine sociodemographic factor associated with internet utilization. The impact of internet use on family planning behaviors was assessed by propensity score matching using average treatment effect.Result: The magnitude of Internet use in Ethiopia was 14% and factor associated with internet use include age of respondent, educational level, resident, live in city administrative region, computer in household and mobile phone ownership. The average treatment impact of internet use on exposure to internet about FP, male condom uses and knowledge about FP increase by 12%, 7.2% and 4.5% percent point respectively.Conclusion: In spite of fast-growing nature of internet and popularity among youth people. In future integrating health information on internet in different platform will be most effective way of health promotion channel.Funding Statement: There is no any funding source for this reaserch which coverd by researcher.Declaration of Interests: The author declares there is no competing interest related to this study for publication. Ethics Approval Statement: The study has permission by ICF international after request is processed. Ethiopia demographic and health survey 2016 participant gave informed consent prior to study. This study ethically approved by continues education and research board of Dessie Health Science College.
Construction of Shipping Linked Data Lifecycle Model and Its Application in Semantic Navigation

Abstract As one of the most important government data assets, shipping data has attracted more and more attention from stakeholders. The term linked data refers to a set of best practices for publishing and linking structured data on the web. The application of linked data technology to open the shipping data can promote the transparency and reusability of data. Many shipping data have been placed on the network as linked data. However, the existing construction of shipping linked data does not consider the concept of lifecycle, and the concept of lifecycle naturally lies in the link data itself. Therefore, this paper attempts to propose a linked data lifecycle model to assist in publishing shipping data as linked data on the Web. We first conduct a systematic analysis of the typical lifecycle model. On this basis, a new linked data lifecycle model is proposed, and the role of each step of the model is also explained. In particular, we take domain modeling as the first step of the model and discuss the key role of it in publishing linked data. Taking the voyage data as an example, we show in detail how to use the proposed model to construct shipping linked data. Then, a specific semantic navigation case based on the above data is given to prove the added value of the linked data. The results show that the lifecycle model proposed in this paper is suitable for the publication of shipping data and is convenient for future expansion and maintenance.
Code Similarity Detection Technique Based on AST Unsupervised Clustering Method

Code similarity detection is one of the important means to maintain the healthy development of the software eco-environment. Early code similarity detection method is mainly based on the code attribute measurement technology, this method can quickly detect more obvious similar parts on small-scale code datasets, but when faced with large-scale datasets or code segments with similar syntax and semantics, the detection performance is not effective. The new code similarity detection technology based on tree-structure representation and machine learning can adapt to different data changes, thus providing new solutions. This paper proposes a new detection method that combines the abstract syntax tree (AST) structure and unsupervised clustering, this new method can improve the detection efficiency of code clustering analysis, because it not only considers the syntax and semantic information of the code, but also reduces the workload of code data preprocessing through unsupervised clustering.
Adaptive Fuzzy Tracking Control for a PMSM via Backstepping Approach

In this chapter, a speed tracking control is proposed for permanent magnet synchronous motor (PMSM) with parameter uncertainties [1, 2] and load torque disturbance. The nonlinearities are approximated by fuzzy logic systems and the controller is constructed by adaptive backstepping technique. The proposed controller can ensure that the position tracking error converges to a small neighborhood of the origin [3]. The simulation results show that the proposed control scheme can keep up with the desired signal well under the condition of parameter uncertainty and load torque disturbance without singularity and overparameterization.
TEDD: Robust Detection of Unstable Temporal Features

When working with real-world temporal data, it is common to encounter features whose distribution is changing over time. The naive employment of Machine Learning models on this unstable data might lead to rapidly degrading performance, especially if the new distribution is much different from what was previously seen during training. In order to cope with this problem, it is critical to automatically identify features that are changing over time. With these features detected, data scientists and other practitioners will be able to mitigate the issue (for instance, by applying data transformations), deploying more robust models that retain high performance for longer periods of time. In this paper, we describe which temporal changes a feature should not suffer from, and propose TEDD, a technique to a) identify when a dataset might lead to an unstable Machine Learning model and b) automatically detect which features cause such lack of robustness. In order to achieve it, we leverage a regression model to highlight which features contribute to a good prediction of an instance's timestamp. We compare our approach to other methods in real and synthetic data, testing their detection capability on all simple change patterns. We show that our method: detects all types of basic changes, both for numerical and categorical features; can detect multivariate drifts; returns a comparable value measuring the amount of change of each feature; requires no parameter tuning; and is scalable both on number of features and instances of the dataset.
Completeness Seeking Probabilistic Coverage Estimation using Uncertain State Estimates

This paper develops a coverage-centric adaptive path planner to visually survey a planar environment. This is achieved by modifying an existing path planning architecture to use a novel coverage estimation approach called convolved coverage estimation (CCE). The planner maximizes the probability of terrain coverage and exploits terrain features for loop closure to keep path uncertainty in check. The developed algorithm considers multi-dimensional uncertainty, operates in real-time, and does not require external correction methods like GPS. These characteristics are validated in high-fidelity simulation and flight tests on an unmanned aerial vehicle (UAV).
Application of Java Technology in Dynamic Web Database Technology

Abstract With the development of Internet technology, Java has been applied to a variety of software design, including Database technology (hereinafter referred to as DT), web technology, which can enhance enterprise information and data management. DT can improve the efficient operation of information system, which needs to ensure the access performance of database. With the rapid increase of data volume, the data transmission between dynamic web pages is busy, which will lead to more and more database data access. Therefore, we have higher and higher requirements for dynamic web DT, which requires fast and efficient data interaction with server database. Java is one of the most commonly used programming software, which can solve many problems of dynamic web database. Therefore, more and more attention has been paid to the research of database optimization technology by Java.
Permission Token Segmentation Scheme Based on Blockchain Access Control

The Internet of Things (IoT) is a large-scale complex network composed of a large number of heterogeneous devices, and due to the complexity of permission management in some scenarios of IoT, such as smart cities and smart healthcare, access control systems need to support strong flexibility. Permission delegation is an effective means to improve flexibility, but the delegated permission token in the current permission delegation scheme is a whole, and the recipient cannot re-delegate part of the permission in the received token. In this paper, a blockchain-based permission token segmentation scheme is proposed, which splits out part of the permissions in a permission token owned by the subject to generate a new token, so that the subject can control the fine-grained permissions in the token, enabling the subject to manage the permissions more flexibly. The permission combination scheme is then provided, and the permission invalidation problem in the token segmentation scheme is analyzed and discussed, and a token invalidation scheme is given. The security analysis shows that the scheme can reliably guarantee the security of authorized access.