Reviewerly/exHarmony huggingface dataset:
Online Layered Multiple Object Tracking Using Residual-Residual Networks

When dealing with multiple object tracking in the real world, it faces several challenges: (a) The number of targets to be tracked will change over time, (b) The data association of the target at different times will be affected by occlusion, (c) The problem of estimating the continuous state of all targets and deciding whether the targets leave the screen and then stop tracking. In this paper, a novel multiple object tracking method that consists of a residual-residual network and a four-layer data association scheme. The residual-residual network combines a deep residual classification network and a deep residual feature network. The deep residual classification network is used to remove unwanted background noise from the frame and corrects the target position of the missing ones. It can accurately track the position of multiple targets, link their positions in each time period, combine layered target data association method, and stepwise pair the trajectories and target candidates according the features generated from the deep residual feature network. The experiments using MOT16 that is a multi-object tracking database, show that the proposed method leads most existing researches in several evaluation criteria including the accuracy, speed and false positive.
Uji Keamanan Website Terhadap Serangan Path Traversal (Studi Kasus Website Pendataan Warga)

Website security is very important because website is targeted hacker to exploit actions that can harm the website itself. One of them is a Path Traversal attack that allows an attacker to access file, directory and commands that are potentially outside the root directory of web document. To find out how a website was broken or in other words hack with Path Traversal technique, then make an analysis of the Path Traversal attack. The purpose of this research is to study Path Traversal attack, find out how to anallyze website security and find solutions to overcome wibsite security problem against Path Traversal attack. In this research conducted using the OWASP ZAP scaning application program to clearly see the vulnerability on a website. Based on the analysis of Path Traversal attack on the citizens collection website, it can be seen that when access to the directory is not protected it will be a gap for the attacker can access or retrieve important files in the app. To minimize Path Traversal attack add an .htaccess file or index.php of all folder.
DESAIN SISTEM: VISUAL BASIC FOR APPLICATION EXCEL 2010 UNTUK PENGGAJIAN GURU MADRASAH ALIYAH MUHAMMADIYAH I MALANG

In the world of education in Indonesia, computers have been introduced and used in schools ranging from basic education to tertiary institutions. Computers make it easy to find and provide learning materials can also be obtained, for example by the concept of electronic libraries (e-libraries) or electronic books (e-books).&#x0D; MA Muhammadiyah I Malang is a school that is equivalent to a high school (SMA) that has used a computer as a tool for the process of teaching and learning activities, finance, administration and so forth. Computers are tools that help humans in making reports or in producing information that can be achieved as a basis for decision making and can contribute more to data storage and data retrieval processes. Currently in the financial section for the processing of teaching fees and employee honorariums still use Microsoft Excel. Separate financial data in several sheets that require separate data input for each sheet so that financial calculations and reporting are less efficient and fast.&#x0D; The bookkeeping process is sometimes very difficult, complicated or even troublesome for some people. But today's computer technology has been able to change this process to be easier, simpler, faster and more efficientPayroll system is a computer application created to make it easier for employees in the financial department to make, calculate, report and analyze school financial turnover so that the application can provide optimal services to teachers and employees as a support for timely decision making.
Innovation Workshop Documentation for Following Software Engineering Activities

[Context & motivation] Requirements engineering (RE) can be seen as creative problem solving (CPS), overlapping with user experience (UX) and design activities. Creative processes, such as innovation workshops (IWs), are often facilitated group activities. They provide an understanding of challenges and user needs, leading to increased software quality. A large number of results from IWs needs to be documented in a suitable manner for later use, as not all results can be followed up upon immediately. [Question/problem] With current means of IW documentation, it is hard to extract the required information (e.g., photo minutes), or they are inefficient to produce or digest (e.g., audio and video recordings, textual documentation). Documentation of only the results leads to the loss of any discussions, decisions, reasons, and discarded alternatives, as these are usually not written down during an IW. The interpretation of the documentation depends on the viewer’s memory and understanding of the IW and the results, which is prone to misinterpretation and errors unless enriched with context information from the IW planning. [Principal ideas/results] We explored the limitations of IW documentation during a workshop with 29 experts from the usability and UX domain. Problems with using the results in later software engineering (SE), RE, and UX activities arise from misalignment between IW result documentation and activity requirements. The experts created a set of initial solution ideas, but no concrete solutions. [Contributions] We address the need for reasonable methods for documenting the results of IWs so that they can be used efficiently in later activities. The design and preliminary results of the expert workshop are presented. Furthermore, we discuss a research roadmap towards making targeted improvements to IW documentation by understanding subsequent activities.
Obtaining the potential number of object models/atlases needed in medical image analysis

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.
A method of posture monitoring and falling detection based on physiological and behavioral characteristics of the elderly

At present, the problem of population aging has become a hot spot of international concern, especially in China, and the international community urgently needs a universally applicable health care system for the elderly. Recent research shows that falling is the biggest threat to the health of the elderly. Based on thihe physiological and behavioral characteristics of the elderly, the paper discusses an algorithm for the recognition of motion state and fall detection of elderly applied to wearable devices to ensure timely rescue after a fall. The algorithm continuously acquires acceleration information during the movement of the elderly through a six-axis acceleration sensor. Firstly, the acceleration data is filtered, then the combined acceleration is calculated, and multiple features of the continuous data are extracted, and then the softmax method is used to classify the different motion states to realize the alarm of the fall. The algorithm extracts the feature vector by the magnitude of the combined acceleration, which solves the problem that the single acceleration in the traditional algorithm must solves the coordinate axis, which may waste much calculating time. The algorithm is validated by using the existing data set, and the accuracy of the algorithm is up to 89%. It is an effective way to detect falls.
Cloud Computing/Anything as a Service (XaaS)

No AccessVon Augmented Reality bis KI - Die wichtigsten IT-Themen, die Sie für Ihr Unternehmen kennen müssenMar 2020Cloud Computing/Anything as a Service (XaaS)Thomas BartonThomas BartonSearch for more papers by this authorhttps://doi.org/10.3139/9783446464353.002SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2020Pages: 27-40Print ISBN: 978-3-446-45915-1eISBN: 978-3-446-46435-3 Copyright & Permissions© 2020 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Collaboration Exercises in Emergency Work : Outcomes in Terms of Learning and Usefulness

In this study we reviewed scientific papers which measured the effect of collaboration exercises in terms of learning and usefulness. The question to be answered was: Do collaboration exercises con ...
Surface profile measurement based on continuous wavelet transform in line-scan dispersive interferometry

This paper presents a signal processing method based on continuous wavelet transform in our line-scan dispersive interferometric system. Our method enables us to extract phase information from the spectrum by applying continuous wavelet transform, which is more effective and reliable in analyzing non-stationary signal compared with FT transform. Experimental results obtained by measuring a step standard with a height of 1.806&mu;m reveal that this method has high accuracy and a good performance over noise resistance, which makes it suitable for complicated in-line measurement condition.
PENGELOLAAN PENILAIAN HASIL BELAJAR MATA PELAJARAN BAHASA INGGRIS MTs PADA ASPEK PENGETAHUAN

The purpose of writing this article to describe how to process the results of the assessment of the aspects of knowledge made by MTs English teachers, both in assessing the learning process and student learning outcomes. Assessments made by teachers in determining the student's final grade, consisting of the Daily Test Score , the Mid-Term Test Score and the End-Year Test Score . This assessment is carried out to measure students' abilities in the form of factual, conceptual, procedural, and metacognitive knowledge, as well as low to high level thinking skills. The competency measured in this activity is related to the achievement of Basic Competence (KD) on KI-3 conducted by MTs English subject teachers. The assessment technique used is in accordance with the characteristics of each. These include written tests, oral tests, and assignments. The method used in writing this article is descriptive analytic, to describe the ability of English MTs teachers, in making decisions using information obtained through measurement of student learning outcomes. As for the results of this writing, teachers are expected to be able to process the results of the assessment of knowledge used for the benefit of learning. For this reason, teachers must be able to process the results of the assessment of knowledge for various purposes, including to determine the success of learning, to do remedial or for enrichment. Key words: Evaluation; Knowledge; test.
A Hybrid Features for Signature Recognition Using Neural Network

In automatic personal recognition systems, biometric features is used as recognition measure based on biological traits such as face, iris, fingerprint, etc…or gait, signature which is considered behavioral characteristics.Signature verification is one of the authentication methods which can provide security at maintenance and low cost.The most essential and challenging stage of any off-line signature system is feature extraction stage.The accuracy and robust of the recognition system depends basically on the usefulness of the signature features extracted by this system.If the extracted features from a signature's image doesn't robust this will cause to higher verification error-rates especially for skilled forgeries in hacker the system.In this paper, we present a new offline handwritten signature recognition system based on combination of global with Statistical and GLCM (Grey Level Co-occurrence Matrix) features using neural network as classifier tool.The global, Statistical and GLCM features are combined to consist a vector of 14 features for the authentication of the signature.Verification of signatures is decided using neural network.The experimental results obtained by using a database of 7 individuals" signatures.A total number of 70 images are collected with 10 signatures for each person, 5 of the signatures are used in training phase, and the remaining 5 signatures are used in testing phase.In this proposed method the results show 100% recognition accuracy for training and 97.1% recognition accuracy for the testing.
Deep learning allows enhanced detection of surface plasmon scattering (Conference Presentation)

We investigate a way to detect images of surface plasmon scattering using deep learning approach. Unlike fluorescence imaging, the image of surface plasmon scattering shows much worse resolution due to propagation length of surface plasmon polariton. In this work, deep learning approach is taken to address this issue and to discriminate multiple target objects under complex and noisy environment. Conventional detection method based on fourier filtering and deconvolution was employed to compare the performance of the proposed method. It was shown that deep learning improves the accuracy by about six times, and especially more useful in noisy environment.
Security of Cloud Storage: A Survey

Cloud computing is gaining increased adoption for everyday computing and storage needs. Its use starts from storing personnel files to storing Government data, enterprise business data, data from IOT devices, satellite imagery data, and data from social media applications running on the cloud and so on. However, due to lack of awareness and appropriate security measures, cloud users become victims of the security vulnerabilities exposed by the cloud storage. The cloud user and Cloud Storage Provider (CSP) should deploy appropriate techniques and follow best practices to overcome the security issues proactively. In this paper, the security vulnerabilities existing in cloud storage are discussed and recent state-of-the-art techniques used to enhance its security are analyzed. Further discussion is carried out on the best practices and security policies framed by various organizations. This survey shall enable researchers to gain an overall picture of cloud storage security aspects and find out open issues that can be further researched. Further, it shall enhance cloud storage providers to adopt stateof-the-art techniques to prevent security incidents. The end-users shall be aware of the risks of cloud storage and demand the appropriate security measures from the CSPs.
S3D-R2R: An Automatic Stereoscopic 3D Image Recomposition to Retargeting Method with Depth Modification

Stereoscopic image adaptation to the target display devices while minimizing the distortion of significant features and stereoscopic properties is a challenging problem. Conventional methods either fail to preserve the image context or unable to improve the image aesthetics with improved depth perception in the retargetedimages. In this paper, we present an automatic warping-based stereoscopic 3D image recomposition to retargeting method, shortly S3D-R2R that improves the stereo image composition in the retargeting results. Our S3D-R2R method resizes both the left and right stereo image pair using a global optimization algorithm thatminimizes a set of aesthetic quality errors. These errors are formulated based on the selected photographiccomposition rules and modify the depth perception. To improve the depth perception of the stereo image pair,the disparity consistency has been modified within the comfort disparity range. Experimental results showthat our automatic method changes the position of the salient object in the target image scale and improves thedepth perception within the comfort depth range. Empirical user studies indicate that our retargeting resultsreceive more attention than state-of-the-art methods
Silent speech interfaces for speech restoration: current status and future challenges

Speech production is a rich and complex process, the acoustic signal being just one of thesignals stemming from it. In the last few years, the automatic processing of these non-audible speech-related signals has become a research area on its own: silent speech. One of the most exciting applications of silent speech is the possibility of restoring speech to individuals who have lost the ability to communicate orally after a disease or trauma. In this talk I will present an overview of my previous research on silent speech interfaces, where magnetic sensing is used to monitor the movement of the speech articulators and deep learning techniques are used to synthesize speech from the sensor data. In addition to my own research, I will present the current status of silent speech interfaces and discuss common challenges and solutions in this emerging research area.
HUBUNGAN ANTARA TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) DENGAN TECHNOLOGY INTEGRATION SELF EFFICACY (TISE) GURU MATEMATIKA DI MADRASAH IBTIDAIYAH

&lt;em&gt;RELATIONSHIP BETWEEN TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPAK) WITH TECHNOLOGY INTEGRATION SELF EFFICACY (TISE) MATH TEACHER IN THE ELEMENTARY SCHOOL .&lt;/em&gt; &lt;em&gt;The study aims to determine the TPACK level of MI mathematics teachers in Gebog Kudus. It will also look at the TISE level of MI mathematics teachers at Gebog Kudus and the level of significance of the relationship between TPACK and TISE on MI mathematics teachers at Gebog Kudus. The design of this study uses quantitative research approaches with correlational designs. Subjects in the study were 143 mathematics teachers, namely madrasah teachers from first grades through sixth. Data collection techniques using the TPACK and TISE Questionnaire, observation and interviews. The results showed that: 1) the TPACK level of MI mathematics teachers at Gebog Kudus at a simple level, 2) the TISE level of MI mathematics teachers at Gebog Kudus at a simple level, and 3) there was a significant relationship between TPACK and TISE at MI mathematics teachers at Gebog Kudus.&lt;/em&gt;
Analysis of the interaction of robots in decentralized multi-robot system for transport tasks during start of the movement

The article presents the analysis of the influence of control signal time delays on the start of movement of a distributed group of robots and on the implementation of their programmed trajectory. Static analysis was carried out in a distributed group of robots, which were imposed by constraint resulting from connecting the robots with a transport pallet. Then, dynamic analysis of loads occurring as a result of control signal delays in MSC Adams View software was performer.
Action Recognition Using Motion History Information and Convolutional Neural Networks

Human action recognition is a key step in video analytics, with applications in various domains. In this work, a deep learning approach to action recognition using motion history information is presented. Temporal templates capable of capturing motion history information in a video as a single image, are used as input representation. In contrast to assigning brighter values to recent motion information, we use fuzzy membership functions to assign brightness (significance) values to the motion history information. New temporal templates highlighting motion in various temporal regions are proposed for better discrimination among actions. The features extracted by a convolutional neural network from the RGB and depth temporal templates are used by an extreme learning machine (ELM) classifier for action recognition. Evidences across classifiers using different temporal templates (i.e. membership function) are combined to optimize the performance. The effectiveness of this approach is demonstrated on MIVIA video action dataset.
An API for Bare Machine Computing Applications

Conventional computing systems require some form of an OS or kernel to run applications. These include minimal OSs and small kernels such as tiny Linux kernels, and embedded OSs. In a bare machine computing (BMC) system there is no OS or kernel, thus enabling applications to be completely self-supporting while eliminating OS vulnerabilities and overhead. These applications, which are currently written in C/C++ with some assembly code, run on desktops or laptops with Intel x86 CPUs, and integrate the necessary BMC software and hardware interfaces. This paper provides internal details of several such BMC interfaces showing how they are designed, implemented and integrated with OS-independent applications. BMC interfaces could be adapted in future for use in database servers for Big Data applications, controllers for software defined networking, and IoT devices or smart phones with a view towards improving security and performance.
Classification-Friendly Sparse Encoder and Classifier Learning

Sparse representation (SR) and dictionary learning (DL) have been extensively used for feature encoding, aiming to extract the latent classification-friendly feature of observed data. Existing methods use sparsity penalty and learned dictionary to enhance discriminative capability of sparse codes. However, training dictionary for SR is time consuming and the resulted discriminative capability is limited. Rather than learning dictionary, we propose to employ the dictionary at hand, e.g., the training set as the class-specific synthesis dictionary to pursue an ideal discriminative property of SR of the training samples: each data can be represented only by data-in-class. In addition to the discriminative property, we also introduce a smoothing term to enforce the representation vectors to be uniform within class. The discriminative property helps to separate the data from different classes while the smoothing term tends to group the data from the same class and further strengthen the separation. The SRs are used as new features to train a sparse encoder and a classifier. Once the sparse encoder and the classifier are learnt, the test stage is very simple and highly efficient. Specifically, the label of a test sample can be easily computed by multiplying the test sample with the sparse encoder and the classifier. We call our method Classification-Friendly Sparse Encoder and Classifier Learning (CF-SECL). Extensive experiments show that our method outperforms some state-of the-art model-based methods.