Reviewerly/exHarmony huggingface dataset:
How Meaning Can Inform Fashion Design – The Semantic Turn

This review presents humans-centered design and shows how it can inform fashion design. The core insight of human-centered design is that people do not respond to things (material and immaterial) as they are but act according to what they mean. Thereby meaning is co-created on different levels of artificiality. These levels reach from pure product to any kind of discourse by which people refer to their experiences or thoughts and feelings. One important conclusion is that designers do not create the meaning of the designed alone but with a whole community involved and connected to the designed.
HMCA-Contour: A Visual Basic Program Based on Surfer Automation for Soil Heavy Metal Spatial Distribution and Contamination Assessment Mapping

Soil heavy metal contamination has become a major concern in many parts of the world. Grasping the spatial distribution patterns of heavy metals and evaluating soil heavy metal contamination are of great significance to policy makers for land use planning and pollution identification. However, these processes usually involve repetitive calculation and plotting, which are time-consuming and laborious, especially in a geochemical survey. In this paper, HMCA-Contour, which is a program written in Visual Basic based on Surfer 13.0, has been designed to simplify the pollution indices recalculation and plotting routines. This software is comprised of three functions, i.e., data processing, plotting, and template setting. With user-friendly interfaces, the user can easily and efficiently calculate pollution indices and generate batch publication-quality maps of heavy metal spatial distribution and pollution levels. To demonstrate the advantage of HMCA-Contour, a case study from the Gaerqin Mine area is used in this paper. HMCA-Contour, as a brand new software, is promising and will become a useful tool for environmental management.
Image registration between MRI and spot mammograms for X-ray guided stereotactic breast biopsy: preliminary results

Breast cancer is the most common cancer type among women. Approximately 40,000 women are expected to die from breast cancer every year. While digital mammography has a central role in the early diagnosis of breast cancer, many cancers are not visible in mammography, for example in women with dense breast tissue. Contrast enhanced magnetic resonance imaging (CE-MRI) of the breast is often used to detect lesions not visible in mammography. Lesions with suspicious characteristics on CE-MRI need to be further assessed with MRI-guided biopsy. However, MRI-guided biopsy is expensive, time consuming, and not widely available. In this paper, a novel method for a matching tool between MRI and spot mammograms is proposed. Our aim is to transfer information that is only visible in MRI onto mammographic spot projections, to enable X-ray guided biopsy even if the lesion is only visible in MRI. Two methods of registration in combination are used; a biomechanical model based registration between MRI and full view X-ray mammograms and a subsequent image based registration between full mammograms and spot mammograms. Preliminary results assessed for one patient from the Medical University of Vienna are presented. The target registration error (TRE) of biomechanical model based registration is 2.4 mm and the TRE of the image based registration is 9.5 mm. The total TRE of the two steps is 7.3 mm.
A minimal model for classification of rotated objects with prediction of the angle of rotation

In classification tasks, the robustness against various image transformations remains a crucial property of CNN models. When acquired using the data augmentation it comes at the price of a considerable increase in training time and the risk of overfitting. Consequently, researching other ways to endow CNNs with invariance to various transformations is an intensive field of study. This paper presents a new reduced, rotation-invariant, classification model composed of two parts: a feature representation mapping and a classifier. We provide an insight into the principle and we show that the proposed model is trainable. The model we obtain is smaller and has angular prediction capabilities. We illustrate the results on the MNIST-rot and CIFAR-10 datasets. We achieve the state-of-the-art classification score on MNIST-rot, and improve by 20% the state of the art score on rotated CIFAR-10. In all cases, we can predict the rotation angle.
Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window

The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflow so that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on the reservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored. Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. The window size represents the number of days as an input layer variable in the backpropagation network architecture to predict the next day's tpaw. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results. The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plant as an alternative in making turbine operation decisions based on the predicted results of reservoir water level.
Evaluation of Performance of MLP Neural Networks and RBF Neural Networks in Adjusting Time Series of the Development of the Trade Balance between the USA and the PRC

Artificial neural networks are widely used for predicting values, for solving possible future problems and are able to provide various solutions in problem estimation, regression or optimisation. They are useful for predicting time series too. The aim of the paper is to analyse and evaluate the performance of multilayer neural networks (hereinafter referred to as "MLP") and neural networks of radial basis function (hereinafter referred to as "RBF) in adjusting time series on the example of the trade balance between the United States and the People's Republic of China. Regression was performed using neural structures. We generated multilayer perceptron networks and neural networks of radial basis function and we generated two sets of artificial neural networks. Time was the continuous independent variable. We determined the trade balance of the USA and the PRC as a dependent variable. We can state that due to the great simplification of reality, it is not possible to predict the emergence of extraordinary situations and their impact on the trade balance of the USA and the PRC. We can state that when an adjusted time series is derived from a single variable, time, RBFs perform better than MLPs. In order to make the prediction more accurate and its calculation easier, it seems appropriate to use RBF networks, which brings a relatively high degree of accuracy.
Fuzzy Logic Modeling for Integrating the Thematic Layers Derived from Remote Sensing Imagery: A Mineral Exploration Technique

In this study, fuzzy logic modeling was implemented to fuse the thematic layers derived from principal components analysis (PCA) in order to generate mineral prospectivity maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used. A spatial subset zone of the Central Iranian Terrane (CIT), Iran was selected in this study. The PCA technique was implemented for the processing of the datasets and for the production of alteration thematic layers. PCA4, PCA5, and PCA8 were selected as the most rational alteration thematic layers of ASTER for the generation of a prospectivity map. The fuzzy gamma operator was used to fuse the selected alteration thematic layers. The PCA3, PCA4, and PCA6 thematic layers (most rational alteration thematic layers) of WV-3 were fused using the fuzzy AND operator. Field reconnaissance, X-ray diffraction (XRD) analysis, and Analytical Spectral Devices (ASD) spectroscopy were carried out to verify the image processing results. Subsequently, mineral prospectivity maps were produced showing high-potential zones of Pb-Zn mineralization in the study area.
Development and Implementation of a Video Watermarking Method Based on DCT Transform

This paper presents a new color video watermarking technique based on the one-dimensional Discrete Cosine Transform (DCT). This approach uses a differential embedding technique to insert the bits of the watermark into the video frames so that the extraction process is blind and straightforward. To further ensure the security of the method, the binary image watermark is scrambled using Arnold transform before embedded into the video segment. Also, a color space transformation from Red, Green and Blue (RGB) to YUV is performed in order to deal with the color nature of the video segments. The proposed approach exhibits good robustness against a wide range of attacks such as video compression, cropping, Gaussian filtering, and noise adding. Finally, we propose an implementation of the video watermarking technique using the Raspberry Pi 3 platform. Nearly the same remarks may be made as in the simulation resultsconcerning the robustness against video compression attacks
Cloud computing in logistic and Supply Chain Management environment

Today in the era of digitalization Cloud computing is the cornerstone of modern business models in every industry. Cloud computing is a high-tech platform that will facilitate companies or organizations to manage and host their services without worrying about the stability and security of information telecommunication. Rapid driving technological changes are unstopped process in this Industry 4.0. world. Cloud computing is a trend with enormous implications. Cloud computing provides access to large-scale remote resources in a very efficient and quick manner, with huge potential to adapted and dramatically changed business models in line with current needs and requirements. Despite all the benefits of digital technology, the loss of data and security is the primary concern for companies, which are boosting companies from moving to the cloud entirely. Cloud technologies allow companies to quickly adopt emerging technologies like augmented reality, stability, security, advanced mobile apps, and advanced analytics. Their application is becoming ubiquitous and standardized in the logistics and Supply chain management sector. The logistics industry is characterized by many parties collaboration, where actors that must be integrated and synchronized along the supply chain. Logistics companies get enormous benefits with saving costs, time, and efforts on establishing their own IT infrastructure by applying cloud computing in supply chain management. In this digital transformation, the cloud computing has a key role, it becomes the catalyst for this transformation. Cloud computing, as one of the forces for digitalization, can foster collaboration among the parties in a supply chain, can promote innovation and embed it into organizations, and enable new competitors to enter the market with innovative offers. This makes the usage of cloud computing a very promising issue in global market competitional condition.
An Analysis of the Lack of Modern Educational Technology in the Teaching of Secondary Vocational English Subjects and its Teaching Strategies——Take Pingli Vocational Education Center as an Example

The report of the Educational sector in China advocates modern education technology, and now a new round of curriculum reform has thoroughly revised the curriculum standards of middle school students in various subjects, and penetrated modern educational technology into the teaching of various subjects. In recent years, the education department has raised the importance of vocational education, changed the concept of education for teachers and students, vocational education is in the fastest-growing period, the majority of secondary vocational education students has greatly improved, the curriculum is colorful. We should make good use of all kinds of the up-to-dated educational technologies in various subjects. However, in the process of education, the basic level of secondary vocational students is relatively poor phenomenon slowly appeared, which is a common problem in secondary vocational education, so we should strengthen the modern education technology in English subjects of the value of education. English, like our mother tongue, has a wealth of humanistic knowledge and spiritual connotation. Many English teachers in secondary vocational school simply teach basic English knowledge aspects and English grammars. It only lets the most vocational students to learn only the most basic English textbook knowledge, and then English teachers find many bad aspects, such as: students do not understand foreign culture; Therefore, secondary vocational English teachers in the English classroom to penetrate modern education technology, is the most important measure. So, as an English teacher, how to modernize secondary school students in classroom teaching? This article mainly talks about the teaching of secondary English subjects lack modern education technology and its teaching strategies.
Robust Visual Tracking with Occlusion Handling Using Gaussian Mixture Modeling

Computer vision and its automation has the ability to transform the working of our gadgets. Detection and Tracking is one such powerful technology for making equipment and robots independent of any human aid. This paper present a new and pertinent approach to detect and track any moving unknown vehicle in a video. The proposed algorithm is based on the machine learning methodology to model the image data. The Gaussian Mixture Model (GMM) is applied for clustering analysis to represent data. Selection of target is carried out using GMM with background subtraction method followed by morphological operations. The location of the detected foreground mask in each frame is the Local Motion Vector of the target vehicle. Kalman Filtering is used for trajectory prediction. The performance analysis of the algorithm proves its robustness and effectiveness in detecting and tracking vehicles.
Learning behaviour trees for simulated fighter pilots in airborne reconnaissance missions : A grammatical evolution approach

Fighter pilots often find themselves in situations where they need to make quick decisions. Therefore an intelligent decision support system that suggests how the fighter pilot should act in a spec ...
ModGuard: Identifying Integrity & Confidentiality Violations in Java Modules.

With version 9, Java has been given the new module system Jigsaw. Major goals were to simplify maintainability of the JDK and improve its security by encapsulating modules’ internal types. While the module system successfully limits the visibility of internal types, it does not prevent sensitive data from escaping. Since the module system reasons about types only, objects are allowed to escape even if that module declares the type as internal. Finding such unintended escapes is important, as they may violate a module’s integrity and confidentiality, but is a complex task as it requires one to reason about pointers and type hierarchy. We thus present ModGuard , a novel static analysis based on Doop which complements the Java module system with an analysis to automatically identify instances that escape their declaring module. Along with ModGuard we contribute a complete formal definition of a module’s entrypoints, i.e., the method implementations that a module actually allows other modules to directly invoke. We further make available a novel micro-benchmark suite MIC9Bench to show the effectiveness but also current shortcomings of ModGuard , and to enable comparative studies in the future. Finally, we describe a case study that we conducted using Apache Tomcat, which shows that a migration of applications towards Jigsaw modules does not prevent sensitive instances from escaping, yet also shows that ModGuard is an effective aid in identifying integrity and confidentiality violations of sensitive instances.
Do Augmented and Virtual Reality Technologies Increase Purchase Intention?: The Role of Cognitive Elaboration and Shopping Modes

<p class="MsoNormal" style="margin-bottom:0in;margin-bottom:.0001pt"><span style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">The purpose of this study is to examine the effects of the “virtual try-on” technology (AR) and the “3D virtual store” (VR) incorporated in an apparel retail website on purchase intentions. This study highlights the mediating role of cognitive elaboration in the process through which these technologies influence purchase intentions, and </span><span style="font-size:12.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">examines the way consumers’ shopping goals (searching vs. browsing) </span><span style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">interact with the website technology and influence their responses. The two experiments demonstrated that, for browsers, the website with </span><span style="font-size:12.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">VR was more effective in increasing purchase intentions than were the website with AR or a regular website with no technology, while for searchers,&nbsp;</span><span style="font-size:12.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">both the website with AR and the website with VR were more effective than was a regular website. In addition, </span><span style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">cognitive elaboration</span><span style="font-size:12.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif"> mediated the interaction between a technology and a shopping goal on purchase intentions </span><span style="font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:106%;font-family:&quot;Times New Roman&quot;,serif">for browsers, while such a mediating effect was not found in searchers.<o:p></o:p></span>
Attention‐based video object segmentation algorithm

To improve the segmentation performance on videos with large object motion or deformation, a novel scheme is proposed which has two branches. In one branch, the attention mechanism is first utilized to highlight objects-related features. Then, to well consider the temporal coherence of videos, Conv3D is integrated to capture short-term temporal features, and the designed attention residual convolutional long–short-term memory is adopted to capture the long–short-term temporal information of objects under the interference of redundant video frames. Meanwhile, considering the negative effect of background motion, in another branch, the optical flow-based prediction model is introduced to predict objects regions in subsequent video frames with the annotated initial frame. At last, based on the fused results of two branches, the global thresholds and noising area clean method are employed to obtain segmented objects. The experiments on DAVIS2016 and CDnet2014 exhibit the competitive performance of the proposed scheme.
Exploring Applications for Autonomous Nonverbal Human-Robot Interaction

Non-verbal Human-Robot Interaction (nHRI) encompasses the study of the exchange of human-robot gaze, gesture, touch, body language, paralinguistic, facial and affect expression. nHRI has advanced beyond theoretical and computational contributions. Progress has been made through a variety of user studies and laboratory experiments as well as practical efforts such as integration of nonverbal inputs with other HRI modalities including domain specific implementations. This workshop seeks to promote collaboration between two threads of research: experimental nHRI, and application domains that can benefit from its use.
Basketball Technology Simulation Application Based on Virtual Reality

The combination of virtual reality (VR) technology and basketball technology simulation can make the players have a real experience and experience, so as to effectively improve the quality of basketball training. It can also, according to the individual needs of different players, promote their physical and mental health development and improve the enthusiasm of basketball training, so as to improve the level of basketball technology. Immersive feeling and interest of human-computer interaction are the essential characteristics of virtual reality. The real conception of space-time environment, that is, the process of enlightening thinking and obtaining basketball technology simulation information, is the ultimate goal of virtual reality. In this experiment, five high-level basketball players in our city were selected and numbered from no. 1 to no. 5. Before the experiment, it is necessary to make the players warm up fully before the experiment and then test the forward turning technical movements of basketball. The test variables are based on different angles and distances. After that, standardized selection of basketball technical statistical indicators includes shooting hit rate, two-point hit rate, three-point hit rate, and free throw hit rate. According to the results of the experiment, it took the most time for the curve technique to lower the buffer stage, and it took up 29% of the total time of the entire precursor shot and turned back and the aerial shot stage is 23% and 22%, respectively. There is 20% time in the takeoff phase and 6% time in the brake phase. It has a great influence on the results of the game.
Proxy-Oriented Multiple File Uploading and Integrity Checking of files in Multi-Cloud Environment

Cloud servers have become an option for the organizations to store their files and data.Security in the hybrid and multi-cloud environment is always a challenge to be considered.Client can access the cloud servers to upload the files and documents.In a particular time, when cloud servers restrict access to client for storing the files, then the client can assign its proxy to process the files and upload them.Contrarily, integrity checking is also a significant security issue.It allows customers to verify if their outsourced data is kept intact without the entire data being downloaded.In Hybrid and Multi Cloud environments, a new proxy-oriented multiple file uploading and integrity checking model is proposed to resolve security problems.This concrete safety protocol is built with the aid in the Multi-Cloud Environment.