Reviewerly/exHarmony huggingface dataset:
A Novel Track-Before-Detect Method Based on H-PMHT and the Polynomial Filter

In order to improve the detection performance of radar for maneuvering weak targets in complex battlefield environments, a histogram probabilistic multi-hypothesis tracking (H-PMHT) method based on the polynomial filter is proposed in this paper. This method uses histogram model to directly synthesize target measurement from multi-frame radar observation data, so as to advance the signal-to-noise ratio. Considering the maneuverability of weak targets, we combine the enhanced memory polynomial (EMP) with characteristic of self-initialization and the faded memory polynomial (FMP) which can track indefinitely as the pre-filter, use the Kalman filter as the main filter (EMP/FMP-KF) to obtain nonlinear state estimation from the mixed measurement and mixed proportion generated by the H-PMHT algorithm. Simulation results show that the EMP/FMP-KF based H-PMHT algorithm is an effective track-before-detect method for maneuvering weak targets, and it has greater advantages in tracking accuracy than the Kalman filter based H-PMHT algorithm.
Saliency-based Deep Multi-level Semantic Feature Fusion for Person Re-identification

Person re-identification (Re-ID) is a challenging problem due to external environmental disturbances and significant intra-class appearance variations. Discriminative person features should cover global and partial representations, and accordingly can describe high-level and middle-level semantic information of persons. To achieve this goal, a saliency-based deep multi-level semantic feature representation and fusion algorithm is proposed. Firstly, a feature fusion scheme at middle layer of our deep network is presented to effectively fuse global CNN features. Secondly, a part-based feature extraction method is designed to extract high-level semantic features. In addition, a parameter-free multi-scale saliency-based enhancement algorithm is proposed to compute patch-level saliency scores to enhance the distinctiveness of part-based features. Experimental results on three public datasets, namely, Market-1501, DukeMTMC-reID, and CHUK03, demonstrate the effectiveness of the proposed method compared with state-of-the-art Re-ID approaches.
Analysis and Inspiration of Current Situation of Innovative Maker Education in Secondary Vocational Schools in the Internet + Era

In the context of "Internet +", secondary vocational schools are exploring a new model of maker education by integrating resources such as computer networks with the spirit of educational innovation. In this paper, taking Jinan sixth vocational secondary school as an example, this paper analyzes the feasibility, development status, teaching characteristics and existing problems of the maker education of secondary vocational schools. Based on this, some inspirations for better developer maker education are put forward: creating a maker educational culture atmosphere, creating maker space, improving teachers' teaching level, and education reform and curriculum and evaluation system.
A Study of Applying Deep Learning-Based Weighted Combinations to Improve Defect Prediction Accuracy and Effectiveness

Software errors or bugs are the primary cause of poor software quality. Thus defect prediction is a prominent approach to enhance software quality. It is a common technique for identifying defect-prone programs, which help the practitioners allocate needed quality assurance efforts (e.g., testing and debugging). An accurate prediction may bring significant benefits. However, there is still space for improvements by applying different levels of instances or using some state-of-the-art techniques to construct the prediction models. In this paper, we propose a weighted combination method of activation functions to improve the effectiveness of defect prediction, comprising of weighted arithmetic, geometric, harmonic, contra-harmonic, and cubic combinations. When there are several kinds of classifiers, the method of weighted combinations can be applied to combine the strengths and create a new model or classifier. That is, weighted combination method(s) would be able to combine the advantage of different activation functions to train the neural network in deep learning. Six open-source projects are used to to evaluate the performance of weighted combination methods of activation functions: single, double- and triple-weighted approaches. Experimental results show that double-weighted combinations of activation function outperform single and triple-weighted combinations. It is also worth noting that single activation function outperform triple-weighted combination.
Menerapkan Aspek Keamanan Database Pada Website E-CRM Toko Pelangi

Website security or commonly known as Web Security is information security that specifically handles security on the website. One of the most dangerous hacking methods is SQL injection (SQL Injection). Overview m Enhancing research and foster a sense of trust to customer new or old customer , in maintain loyalty customer and for reach customer new , easy in inform goods new and bonus to loyal customers. Website Design Electronic Customer Relationship Management (E-CRM) there are several features that include displaying company data along with existing items, providing accounts for customers, this account is used to provide more detailed information to customers. Database security includes the implementation of an anti SQL Injection Script in the login feature and URL (Uniform Resource Locator). E-CRM website design method uses Incremental Development which is one method of software development included in Agile Software Development. The results provide k emudahan directly felt by customer because for do communication with party company can do When only and Where A, p elanggan can do ask answer with way post writing on the media already determined
KNN_SVM Based Information Transmission Method for Renewable Energy Power Generation

In order to ensure information security and to achieve real-time acquisition of renewable energy generation data, this paper proposes a KNN_SVM based information security transmission method for renewable energy generation. The digital information in the captured image is extracted and segmented, and classifier is used to classify it. The traditional KNN, SVM and KNN_SVM algorithms are designed to classify and recognize renewable energy data images, and the selection of relevant parameters is optimized to improve the efficiency of character recognition. The experimental results show that compared with KNN global traversal method, the joint classifier improves the recognition rate and the recognition efficiency greatly, and the recognition rate of the joint classifier is also significantly improved compared with SVM.
Realising angelic designs using logiak

© 2019 The authors and IOS Press. ANGELIC is a methodology for encapsulating knowledge of a body of case law. Logiak is a system intended to support the development of logic programs by domain experts, and provides an excellent environment for the rapid realisation of ANGELIC designs. We report our use of Logiak to realise ANGELIC designs, using both Boolean factors and factors with magnitude.
A French text-message corpus: 88milSMS. Synthesis and usage

In this article, firstly we briefly summarise the sud4science project and data collection (http://sud4science.org), ensuing processing/analysing stages, and the resulting corpus, 88milSMS (http://88milsms.huma-num.fr), through a synthesis of quotes and references to previous articles (§ 1). Secondly, we provide a state of the art on some research initiatives that use 88milSMS in various domains and frameworks, which will enable future cross-disciplinary insight (§ 2). Then, we present other usages of the 88milSMS corpus we identified through surveys (§ 3). Finally, we suggest future paths for textual data collection and analysis.
Usability Evaluation and Interface Redesign on Rumah Belajar Application with the User-Centered Design Approach

Rumah Belajar is a free web-based application which can be used as a supplementary learning platform for students in Indonesia. Since its release, there has not been any research on its usability. To increase the satisfaction and comfort of the users, research about usability, evaluation is needed. This research is conducted to better understand the user needs and analyze the flaws of the application. Evaluation was done using a questionnaire and a system usability scale as the quantitative method along with usability testing and a contextual interview as the qualitative method. Through the user-centered design, research involved the users to make sure that the application is aligned with the users’ expectations. The results of the research suggest that the usability of the Rumah Belajar application needs to be improved. The design is made in the form of a prototype that will solve the raised issues. The designed prototype was tested on the users through usability testing. After being tested, the new suggested design could solve the majority of the usability issues.
Algorithm to Avoid Overlapping Vowel Signs in Latin to Balinese Script Transliteration Method

In this study, we have a tendency to found overlapping results of transliteration method in Latin to Balinese Script applications. There have been three applications (web and mobile) that show these overlapping results, i.e. LBTrans, Noto Bali, and Nulisa Aksara Bali. In Balinese Script there have been vowel signs (pangangge suara and pangangge tengenan). These will and to some extent are combined in complete syllables. If they are, Noto Sans Balinese renders them smashed along. During this study for the primary time, we are going to modify the algorithm to unravel this issue. The modified algorithm can be done to take care of space Unicode character that available in Noto Sans Balinese. From the implementation, the new modified algorithm fixes the difficulty and show the vowel signs clearly. This new modified algorithm should be enclosed once building a brand new application or on applications that exist already.
A revisited systematic literature mapping on the support of requirement patterns for the software development life cycle

In the past few years, the literature has shown that the practice of reuse through requirement patterns is an effective alternative to address specification quality issues, with the additional benefit of time savings. Due to the interactions between requirements engineering and other phases of the software development life cycle (SDLC), these benefits may extend to the entire development process. This paper describes a revisited systematic literature mapping (SLM) that identifies and analyzes research that demonstrates those benefits from the use of requirement patterns for software design, construction, testing, and maintenance. In this extended version, the SLM protocol includes automatic search over two additional sources of information and the application of the snowballing technique, resulting in ten primary studies for analysis and synthesis. In spite of this new version of the SLM protocol, results still point out a small number of studies on requirement patterns at the SDLC (excluding requirements engineering). Results indicate that there is yet an open field for research that demonstrates, through empirical evaluation and usage in practice, the pertinence of requirement patterns at software design, construction, testing, and maintenance.
Predicting the Capability of Carboxylated Cellulose Nanowhiskers for the Remediation of Copper from Wastewater Effluent Using Statistical Approach

This study focused on remediation of spiked Cu(II) from wastewater effluent obtained from a real wastewater treatment plants (WWTPs) using oxidised cellulose nanowhisker (CNW) adsorbents.Response surface methodology (RSM) and artificial neural network (ANN) were used to develop an approach for the remediation of spiked Cu(II) from wastewater effluent.As remediation processes from wastewater are often complicated due to the variation in wastewater compositions, results obtained from the benchmark experiments are included as one of the independent variables for ANN modelling.This novel approach and the outcomes are allowed for the first time, since most studies do not consider matrix variability and its impact when evaluating the efficiency of an adsorbent.Moreover, to confirm the model suitability, additional 10 unseen experiments, which were not used in developing both models, were chosen to represent the system of conditions both inside and outside the system.This study found that the ANN model accounting for wastewater variability was superior to the RSM model and to the ANN model not including wastewater variability, in terms of the coefficient of determination (R 2 ), the absolute average deviation (AAD) and root mean squared error (RMSE) when predicting the efficiency of Cu(II) removal from the wastewater matrix.
Intercomprendersi – a teacher training experience

This contribution presents a teacher-training program delivered by the Italian Educational Department in the Autonomous Province of Bolzano (Italy) centred on Intercomprehension theory.It was addressed to teachers in service with the aim of combining both purposes of plurilingual and intercultural education.Considered that linguistic competence is essential for developing knowledge in every school subject, the purpose of this training was to offer practical suggestions to become aware of how interlinguistic, receptive skills and decoding strategies can be applied in combination to facilitate comprehension tout court and to achieve mediation skills and language awareness.The paper offers some considerations about today´s language teaching/learning processes in schools and about the changing role of teachers.The third section introduces the concept of Intercomprehension and its value for teacher training.The paper focuses on a description of the phases of the teacher training program conducted in 2017.It illustrates briefly the context, the needs assessment process, the aims and contents.The fourth section provides a summary and analysis of participants´ feedback, with ideas for future developments and conclusive remarks.
Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks.

We challenge the longstanding assumption that the mean-field approximation for variational inference in Bayesian neural networks is severely restrictive, and show this is not the case in deep networks. We prove several results indicating that deep mean-field variational weight posteriors can induce similar distributions in function-space to those induced by shallower networks with complex weight posteriors. We validate our theoretical contributions empirically, both through examination of the weight posterior using Hamiltonian Monte Carlo in small models and by comparing diagonal- to structured-covariance in large settings. Since complex variational posteriors are often expensive and cumbersome to implement, our results suggest that using mean-field variational inference in a deeper model is both a practical and theoretically justified alternative to structured approximations.
A real-time destructive method to counter information hiding

Nowadays, information hiding can be used to spread illegal content and is more diverse and difficult to detect. So the researchers propose many more complex and high-dimensional steganalysis methods to improve the detection effect. However, traditional steganalysis methods have huge time consumption and can be used only off-line, which are thus useless in the real-time network. Therefore, this paper proposes a real-time destructive method to counter information hiding. Experimental results present that extracting error rate of secret information is around 50after using our method, which means that the method can destroy the covert communication completely. At the same time, the quality of images is acceptable. And the average time using our method is milliseconds to operate a image.
Integrated Approach for Calculating the Mineral Assemblages and Oxide Percentage of Crystalline Rock based on Object-Based Image Analysis and Electron Probe Microscopic Analyzer

Identification of minerals in thin sections using intelligent methods is a very complex and challenging task, which mineralogists and computer scientists are faced with. In order to effectively calculate the the mineral assemblages and oxide percentage of crystalline rock, we proposed an integrated approach based on object-based image analysis (OBIA) and electron probe microscopic analyzer (EPMA). This approach contains the following steps: 1) acquisition of mineral chemistry data and back-scattered electron (BSE) images; 2) image segmentation and classification; 3) calculating the oxide percentage based on the proportional relationship. It is worth noting that the optimal scale parameter for image segmentation is essential for subsequent image classification, which also defines the proportional relationship for calculating the oxide percentage. The experimental results show that the novel method has characteristics of simple extraction approach, little extraction parameter, easy implementation and accurate recognition ratio.
Global Solar Radiation, Sunshine-hour Distribution, and Clearness Index: A Case Study of Sub-Sahara Region in Nigeria

Global solar radiation is the sum total of all radiation reaching the earth surface i.e. it includes: the direct and the diffused solar radiation reaching the earth surface. The instrument used for measuring this very important component arriving from the whole hemisphere is the pyranometer. This is one of the most important parameter for applications, developments and researches related to alternative source of clean and renewable energy. In cases where these data are not available, it is very common to use computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloud cover and sunshine hours, among others. In this research work, the baseline data for mean monthly global solar radiation and sunshine hours for three (3) geopolitical zones of Nigeria (sub-sahara regions of Nigeria) with Sokoto (North-western Nigeria) (12.910N, 5.200E), Maiduguri (North-eastern Nigeria) (11.850N, 13.080E) and Ilorin (North- Central Nigeria) (8.430N, 4.500E) were obtained from the Nigeria Metrological Agency (NIMET), Nigeria which spread from 1996 to 2010. A linear regression correlation model was developed and clearness index estimated for each station in this study. The result shows the Angstrom coefficients and for estimating global solar radiation for zone respectively, using the Angstrom-Prescott model. The average global solar radiation for these stations was estimated, results subjected to statistical tests proven to be good estimates. The study concluded that the Angstrom- Prescott model plays a significant role in predicting and estimating solar energy potentials in these regions
Development of Emergency Image in Students Psychological-Pedagogical Problems

In this work, the didactic possibilities of using elements of the traditional developed a system of graphic exercises based on Eastern architectural traditions; determined the ways and methods of their use in the process of teaching the course “Descriptive Geometry” in higher educational institutions of artistic direction. Separate research results are published in scientific and methodical journals and collections of scientific papers.
A Design of Mandibular Motion Tracking System Based on Video Processing

In clinical medical research, mandibular motion tracking is an important indicator for the diagnosis of mandibular related diseases. This paper proposes a new mandibular motion tracking system with video processing. Four cameras are used as a multi-vision system to complete the mandibular motion tracking by measuring the checkerboard markers on the face bow, and the tracking system is implemented on the heterogeneous platform of ARM + FPGA. A new checkerboard corner detection algorithm is implemented on FPGA to overcome the challenge brought by the recognition speed improving of the checkerboard mark. While the high-precision 3D coordinate transformation algorithm is implemented on ARM to improve the accuracy of the system. The experimental data show that the system can process images of 1280×1024@91fps in real-time with an accuracy of 60um.
Oracle-bone-inscription image segmentation based on simple fully convolutional networks

Oracle bone inscriptions (OBIs) are invaluable materials for recovering the economic and social forms for Shang Dynasty, one of the most ancient dynasties in China. It is very important to get the original OBIs from scanned images of oracle bone rubbings. To this end, researchers have to employ a very time-consuming method that they follow the inscriptions by handwritten tools, pixel by pixel and image by image. In this paper, an image segmentation method was proposed to overcome this limitation based on fully convolutional networks (FCN). In order to speed up training as well as boost the segmentation performance, a simple FCN with only convolutional layers was designed, where batch normalization was incorporated. The proposed method was tested on a real OBI image set (320 samples). Experimental results show that the proposed method is effective enough to get the OBIs from scanned images of oracle bone rubbings.
Patternless Adversarial Attacks on Video Recognition Networks

Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that is practically unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. The attack was implemented on several target models and the transferability of the attack was demonstrated. These properties allow us to bridge the gap between simulated environment and real-world application, as will be demonstrated in this paper for the first time for an over-the-air flickering attack.
An Improved Potential Field Method of the Manipulator Obstacle Avoidance Planning Based on Polishing Path

Aiming at the problem of local minimum of traditional artificial potential field, this paper proposes a partial potential field by offset virtual obstacle correction strategy of potential field method. Determining the escape direction of the manipulator at the extreme value according to the offset direction. By improving the potential function, the manipulator can escape from the local minimum; at the same time, the strategy of continuous potential field is put forward, which can be used in polishing complex surface. The processing sequence of workpiece surface is generated by modifying greedy algorithm, and then path generation is realized by using modified potential field method. Finally, the simulation of the above strategies has done in MATLAB. The results demonstrate the offset virtual obstacle strategy is better and the generated trajectory is suitable.