Reviewerly/exHarmony huggingface dataset:
Adopting Fingerprint as an Authentication Factor on E-Ticketing Mechanism

E-ticketing system is one of the most commonly used system these days. One common use case for e-ticketing is in ticket sales. Unfortunately, there are risks involved in existing e-ticketing systems. One of the biggest causes of risks consists of when the physical tickets are involved in its identification system. This research focuses on increasing the number of physical ticket authentication factors. Moreover, this research involves two methods; survey and theoretical method. The survey method is a research method used to collect physical tickets mainly based on oral and/or written communication. This research also uses literature where the research method is based on research from conferences, journals, and data from the data industry. The e-ticketing systems nowadays are still at the what you have level, while those proposed systems have risen at the what you are level. With fingerprints as a biometric authentication factor, the results show that security testing successfully resolved the risks involved.
Types of overvoltages in 10-35 kV distribution networks and modern methods and means of their limitation

Abstract The basis for the transmission of electrical energy from sources to receivers are overhead power lines 10-35 kV. In these networks, an isolated transmission line or transmission line, grounded through resistors or using arc-quenching reactors, is used. The Republic of Mari El uses networks with isolated transmission line, the total length of which is more than 7000 km. When operating distribution networks, their insulation is affected by a number of internal factors such as long-term operating voltage and voltage fluctuations, short-term overvoltages associated with arc closures, wire breaks, and insulation overlaps. These networks are located on an open area, so they are exposed to lightning overvoltages, temperature, wind loads (mechanical forces), precipitation (snow, humidity). Overvoltages occurring in electrical systems are one of the main factors that significantly affect the reliability of power supply. Despite the short-term effects, overvoltages are characterized by a high multiplicity with respect to the long-term operating voltage, the impact on all electrically connected elements of the system, regardless of the place of occurrence. Overvoltage is the main cause of defects and accelerated aging of structural components. Currently, 90 % of 10-35 kV overhead lines are made on the basis of the use of reinforced concrete structures of supports that require grounding devices, depending on the value of the soil resistivity. Highspeed vacuum circuit breakers are used as switching devices in modern distribution networks of 10-35 kV. The specific properties of vacuum as an arc-quenching medium contribute to the transients in their switching, which require scientific understanding.
Training a Steerable CNN for Guidewire Detection

Guidewires are thin wires used in coronary angioplasty to guide different tools to access and repair the obstructed artery. The whole procedure is monitored using fluoroscopic (real-time X-ray) images. Due to the guidewire being thin in the low quality fluoroscopic images, it is usually poorly visible. The poor quality of the X-ray images makes the guidewire detection a challenging problem in image-guided interventions. Localizing the guidewire could help in enhancing its visibility and for other automatic procedures. Guidewire localization methods usually contain a first step of computing a pixelwise guidewire response map on the entire image. In this paper, we present a steerable Convolutional Neural Network (CNN), which is a Fully Convolutional Neural Network (FCNN) that can detect objects rotated by an arbitrary 2D angle, without being rotation invariant. In fact, the steerable CNN has an angle parameter that can be changed to make it sensitive to objects rotated by that angle. We present an application of this idea to detecting the guidewire pixels, and compare it with an FCNN trained to be invariant to the guidewire orientation. Results reveal that the proposed method is a good choice, outperforming some popular filter-based and learning-based approaches such as Frangi Filter, Spherical Quadrature Filter, FCNN and a state of the art trained classifier based on hand-crafted feature.
Critical Instances Removal based Under-Sampling (CIRUS): A solution for class imbalance problem1

The most critical issue in real world applications are class imbalance problems. Imbalanced data sets are common across different domain including banking, health care, finance and other. When such data sets are trained on typical classification algorithm they tends to be biased towards the majorit y class. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for binary classification datasets by removing overlapped data points called Critical Instances Removal based Under-Sampling (CIRUS). Our method is designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces loss of information. Extensive experiments using simulated and real-world datasets were carried out and the results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.
Weakly Supervised Image Semantic Segmentation with Bounding Box Annotation

Image semantic segmentation can understand and analyze image scenes for low-level computer vision. But it has always faced the challenges of complex feature extraction and difficult data annotation. In view of the time-consuming and expensive of pixel-level labeling, we mainly study a weakly supervised image semantic segmentation model on bounding box annotations. Firstly, the image pixel-level feature extraction is performed through a densely sampled fully convolution network based on pyramids modules, and then the GrubCut algorithm is used to process the weakly supervised data. Finally, the image features and labeled data are jointly trained to build a weakly supervised image semantic segmentation model for bounding box annotations. Experimental results show that the weakly supervised model constructed in this paper achieves better segmentation results than other weakly supervised models.
Embedding Interactive Python Web Applications into Electronic Textbooks

The technology of operative embedding of interactive web applications into electronic textbooks is considered in the article. The possibilities of using Pyviz Panel and Plotly Dash technologies to create interactive web applications embedded in electronic textbooks are analyzed. The technology allowing to convert Python functions into web applications is considered, approaches to embed the converted Python applications into electronic textbooks are proposed.
Estimation of ultraviolet solar irradiation of semi-arid area - case of Benguerir-

Any change in solar sources due to climate raises some of the issues related to the stability of the electrical system produced by photovoltaïque (PV), to start studies to predict all changes in solar radiation to have a more reliable electrical system. In this work, we focus on the estimation of the ultraviolet (UV) part of the solar spectrum because it considers it as a source of aging of the Ethylene-Vinyl Acetate (EVA) encapsulation layer. To achieve this objective, an Artificial Neural Networks (ANN) used with two inputs: the global horizontal irradiation (GHI) and the Solar Zenithal Angle (sza), which are easy to find for any work site.
A Research of Neural Style Transfer on Line Structure Based on Sequence to Sequence Learning

It has always been a challenge for drawing novices to start learning from a zero proficiency level, oftentimes the process itself can discourage them from continuing. Imagine if there was a method that could automatically transform line shapes into arbitrary styles, it would be a time-saving wonder. In this paper, based on the machine translation method of Sequence to Sequence Learning model, we approximately regard the lines in the image as “words”, and the long lines as “sentences”, using data extracted from paired images to train. Our model extracts line features, and then transfers the features to the lines that formed the input images to generate the output images, which could be understood as an emulation of the machine translation process between two languages. The performance of our model has achieved promising results, attesting that our model has an excellent performance when it comes to the line style transfer using Sequence to Sequence Learning. Our method can be used as a supplement to the GAN model and expand the application and research of image style transfer.
Tiny On-Chip Memory Realization of Weight Sparseness Split-CNNs on Low-end FPGAs

Considering implementation in a low-end FPGA with more restrictions on on-chip memory resources and external memory bandwidth, existing methods are Limited by communication with external memory. The on-chip memory in a low-end FPGA is small, hence cannot store an entire feature map. It is imperative to rely on external memory for buffering. Although an on-chip memory in an FPGA is fast, implementing sparse CNN in low-end FPGAs is hindered by their limited memory size. To address the limitations of external memory bandwidth and its size, we employ a split-CNN [1] that splits an input image into small spatial patches and tests each patch using a CNN model as shown in Fig. 1. Since the feature-map to be processed at one time is reduced by the splitting, the amount of memory required for buffering is reduced.
A New Fuzzy Cluster Forests Method For Big Data

With the accumulation of the large data size, clustering of big data is a challenging task. However, data reduction is considered as a powerful model which significantly reduces execution time. This work presents a new fuzzy clustering ensemble model based on cluster forests method (CF) that can simultaneously reduce the execution time and consists mainly of two steps: generation of clusters instances and aggregation of global models. In the beginning, this algorithm makes multiple clusters instances using fuzzy clustering bdrFCM technique. Secondly, it aggregates this clusters to obtain final results using Ncut spectral clustering. We call it as FCE-CF approach. This proposed method is guided by cluster validity index kappa. Experimental results demonstrate that the FCE-CF outperforms the existing clustering methods in terms of time and memory on big data UCI repository.
A SCRUM AGILE INTEGRATED DEVELOPMENT FRAMEWORK

Abstract With design methodologies, as Integrated Product Development, industry is continuously looking to improve their product development processes. Staying ahead concurrence forces them to deliver new and more complex products in shorter time. When it comes to fast delivery and requirement changes, product development can be inspired by agile methods. Although the application is difficult, the idea to implement these methods for development of products other than software comes out. To ease its implementation, this paper proposes to use IPD as a framework for agile product development.
An Improved Mapping Pattern for Digital Watermarking using Hilbert-Peano Pattern

Abstract In the present, watermark plays an important part in all copyrighted content whether it is physical or digital. The importance of watermark in digital data increases as the usage of internet in transferring data increases day by day as the technologies improves. The main objective of using digital watermark is to apply the highest security as possible to the digital image and in the same time to reduce as most disturbance as possible that can be visible to the human eye. This research proposed a hybrid mapping pattern which is Hilbert-Peano Curve to try to achieve the highest security as possible from alteration. This proposed digital watermark uses the Least Significant Bit (LSB) to store the watermark data, which consist of parity bits as the authentication data and recovery data. This is to reduce the disturbance to the original image as much as possible as it might alter the colour of the original image. The results are compared with the existing watermark, Hilbert-LSB to analyse which pattern is suitable to be implemented with the watermarking scheme. The results of this research shows Hilbert-Peano pattern can contribute better performance in terms of watermark data security, time taken to embed watermark data, and the imperceptibility level if compared to the original Hilbert mapping pattern.
A Hybrid Recommendation Algorithm Combing Naive Bayes Classifier and the Users’ Trust Relationship

With the rapid development of information technology, a huge amount of information is shown to the clients, thus leading to the so-called ‘information explosion’ or ‘information overload’. Recommend technique can follow the demand change of clients and automatically adjust the mode and contents of information service. Based on the problems of collaborative filtering recommendation of internal storage including the overdue calculation amount in large-scale system, difficult realization of real time recommendation, poor system expansibility, lack of novelty purely based on contents or the existing interests of clients, this paper proposes hybrid recommendation technique based on naive Bayesian model and client trust. On one hand, it establishes the hobby model of clients and apply Bayesian classification algorithm according to the interests of clients to recommend the classified products to clients. On the other hand, according to trust relation of clients, recommend products of higher praise to trusted clients and tap the potential interests of clients, which not only can guarantee the accuracy of recommendation, but also is of expansibility.
Upaya Peningkatan Pengetahuan dan Sikap Siswa SMP dalam Pencegahan Penggunaan NAPZA

Background: Ransiki is a district in South Manokwari Regency which was a new division in West Papua Province. Junior high school students in Ransiki are at risk of drug use. An effort should be made to improve the knowledge and attitude of junior high school students as a form of prevention of drug use. This study aimed to improve knowledge and attitude for the prevention of drug use in junior high school students in Ransiki using the training module "Pengenalan Bahaya NAPZA bagi Kesehatan".Method: This was a Pre Experimental study using the one group pre-test and post-test design. The study samples were 60 students. Samples were determined by purposive sampling. The intervention was conducted by providing module training for five meetings. The pre-test and post-test of students were analyzed using the Dependent T-test.Results: The results showed there was a significant relationship between the mean score of students’ knowledge before the intervention (39.44) and after the intervention (77.77) (p-value = 0.0001). There was a significant relationship between the mean score of students’ attitudes before the intervention (73.40) and after the intervention (82.40) (p-value = 0.0001). The training module "Pengenalan Bahaya NAPZA bagi Kesehatan" can be used to improve students' knowledge and attitude to prevent drug use in junior high school students of South Manokwari Regency
Estimating Low-Rank Region Likelihood Maps

Low-rank regions capture geometrically meaningful structures in an image which encompass typical local features such as edges, corners and all kinds of regular, symmetric, often repetitive patterns, that are commonly found in man-made environment. While such patterns are challenging current state-of-the-art feature correspondence methods, the recovered homography of a low-rank texture readily provides 3D structure with respect to a 3D plane, without any prior knowledge of the visual information on that plane. However, the automatic and efficient detection of the broad class of low-rank regions is unsolved. Herein, we propose a novel self-supervised low-rank region detection deep network that predicts a low-rank likelihood map from an image. The evaluation of our method on real-world datasets shows not only that it reliably predicts low-rank regions in the image similarly to our baseline method, but thanks to the data augmentations used in the training phase it generalizes well to difficult cases (e.g. day/night lighting, low contrast, underexposure) where the baseline prediction fails.
Modeling Cross-View Interaction Consistency for Paired Egocentric Interaction Recognition

With the development of Augmented Reality (AR), egocentric action recognition (EAR) plays an important role in accurately understanding demands from the user. However, EAR is designed to help recognize human-machine interaction in single egocentric view, thus difficult to capture interactions between two face-to-face AR users. Paired egocentric interaction recognition (PEIR) is the task to collaboratively recognize the interactions between two persons with the videos in their corresponding views. Unfortunately, existing PEIR methods always directly use linear decision function to fuse the features extracted from two corresponding egocentric videos, which ignore the consistency of interaction in paired egocentric videos. The consistency of interactions in paired videos, and features extracted from them, are correlated to each other. On top of that, we propose to derive the relevance between two views using bilinear pooling, which captures the consistency of two views in feature-level. Specifically, each neuron in the feature maps from one view connects to the neurons from the other view, which enforces the compact consistency between two views and then all possible paired neurons are used for PEIR. To be efficient, we use compact bilinear pooling with Count Sketch to avoid directly computing outer product. Experimental results on the PEV dataset shows the superiority of the proposed methods on the task PEIR.
Approach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City”

By analogy to nature, sight is the main integral component of robotic complexes, including unmanned vehicles. In this connection, one of the urgent tasks in the modern development of unmanned vehicles is the solution to the problem of providing security for new advanced systems, algorithms, methods, and principles of space navigation of robots. In the paper, we present an approach to the protection of machine vision systems based on technologies of deep learning. At the heart of the approach lies the “Feature Squeezing” method that works on the phase of model operation. It allows us to detect “adversarial” examples. Considering the urgency and importance of the target process, the features of unmanned vehicle hardware platforms and also the necessity of execution of tasks on detecting of the objects in real-time mode, it was offered to carry out an additional simple computational procedure of localization and classification of required objects in case of crossing a defined in advance threshold of “adversarial” object testing.
KnowID: An Architecture for Efficient Knowledge-Driven Information and Data Access

Modern information systems require the orchestration of ontologies, conceptual data modeling techniques, and efficient data management so as to provide a means for better informed decision-making and to keep up with new requirements in organizational needs. A major question in delivering such systems, is which components to design and put together to make up the required “knowledge to data” pipeline, as each component and process has trade-offs. In this paper, we introduce a new knowledge-to-data architecture, KnowID. It pulls together both recently proposed components and we add novel transformation rules between Enhanced Entity-Relationship (EER) and the Abstract Relational Model to complete the pipeline. KnowID's main distinctive architectural features, compared to other ontology-based data access approaches, are that runtime use can avail of the closed world assumption commonly used in information systems and of full SQL augmented with path queries.
CapableOf Reasoning: A Step Towards Commonsense Oracle

Commonsense knowledge is fundamental to make machines reach human-level intelligence. However, conventional methods of commonsense extraction generally do not work well because commonsense by nature is usually not explicitly stated in texts or other data. Besides, commonsense knowledge graphs built in advance are difficult to cover all the knowledge required for practical tasks due to the incompleteness of knowledge graphs. In this paper, we propose an online commonsense oracle to achieve knowledge reasoning. Specifically, we focus on the on-demand inference of specific commonsense propositions. We use capableOf relation as an example due to its notable significance in daily life. For more effective capableOf reasoning, informative supporting features derived from an existing commonsense knowledge graph and a Web search engine are exploited. Finally, we conduct extensive experiments, and the results demonstrate the effectiveness of our approach.
A Comprehensive Survey on various Security Authentication Schemes for Mobile Touch Screen

As technology is improving day by day, it is leading to a society in which there is a necessity of security for every system. Different strategies are proposed timely to overcome the security threats & vulnerabilities caused in previous strategies to protect the privacy of different users using mobile phones across the world as most of the crucial data of a user is saved in their smartphones. In this paper, we categorized the authentication scheme into four categories, namely PIN-based authentication, Biometric-based authentication, Pattern/Gesture-based authentication, and Graphical password authentication and development done in them over time. We further explore these categories and state there propose, limitation, result, vulnerabilities, and threats. A comprehensive taxonomy is provided for multiple authentication techniques for easy understanding. In addition, it also helps the students and the novice research to get an insight into the trending security techniques.
A Software Defined Radio Testbed for Over-the-air Cognitive Cycle Demonstration

Deep learning (DL) have been widely applied in cognitive radio, including cognitive jamming, cognitive communication and cognitive radar. Many functional properties of DL are amenable to numerous electromagnetic waveform recognition tasks. In fact, there exists a gap between the DL network design and the real time application. This prompts us to adopt the software defined radio testbed to realize the online "cognition-action" demonstration. Via an innovative artificial intelligent (AI)-baseband co-design, the system can realize the modulation recognition and demodulation adaption, which is associating with a demonstration of "cognition-action" cycle. In addition, to realize the online recognition and adaption, we design the over-the-air demodulation reconstruction method. By our experimental results, we demonstrates that such cognitive cycle can bring about noticable improvement in cognitive applications.