Reviewerly/exHarmony huggingface dataset:
Machine Learning based Decision Stratigies for Physical Layer Authentication in Wireless Systems

In this paper, machine learning (ML) based decision strategies for physical layer authentication are presented. The intelligent authenticators learn the channel features and then classify the received message based on the channel attributes into two categories, legitimate or illegitimate. The training set construction using different features of the estimated channel fading coefficients explored. In addition, ML based physical layer authentication is compared with the statistical discriminative function formulated in binary hypothesis test with a pre-defined threshold. Simulation results demonstrated that the performance of intelligent authenticators outperform the statistical decision scheme as significant improvement can be achieved in the detection rate with minimum false alarm rate. The overall authentication accuracy measured in terms of the area under the receiver operating characteristic curve (AVC) confirmed the superior performance of the the support vector machine (SVM) based physical layer authentication compared with other ML approaches. In addition, it is concluded that using two distinct features improves the authentication performance compared with feature space constructed only from test statistic metrics.
Field of Junctions: Extracting Boundary Structure at Low SNR

We introduce a bottom-up model for simultaneously finding many boundary elements in an image, including contours, corners and junctions. The model explains boundary shape in each small patch using a ‘generalized M-junction’ comprising M angles and a freely-moving vertex. Images are analyzed using non-convex optimization to cooperatively find M + 2 junction values at every location, with spatial consistency being enforced by a novel regularizer that reduces curvature while preserving corners and junctions. The resulting ‘field of junctions’ is simultaneously a contour detector, corner/junction detector, and boundary-aware smoothing of regional appearance. Notably, its unified analysis of contours, corners, junctions and uniform regions allows it to succeed at high noise levels, where other methods for segmentation and boundary detection fail.
A Portable System for ToF Camera based Human Body Detection and Pose Estimation

In recent years, human motion detection and analysis has been a research hotspot in the field of machine vision. It has been widely used in sports fitness, sports rehabilitation, sports analysis and virtual reality. Most of the existing human motion detection systems are based on optical equipment using retroreflective markers, which have many defects such as high deployment cost and complex operation. To this end, this article develops a household fitness motion evaluation system based on ToF camera with a mobile phone. The system firstly uses the ToF camera to collect the 3D human body point cloud information. Then Huawei augmented reality engine is employed to track the human skeleton. As all the spatial coordinates of joints are acquired, virtual human model can be reconstructed remotely on the phone. The mobile phone, working as the edge node between the person and the cloud server, transmits the captured human motion model parameters to the cloud platform. Then a hierarchical co-occurrence neural network recognizes and scores the detected user actions online. The experimental results show that the system proposed in this paper is portable and inexpensive. Real-time capabilities and measurement accuracy can also satisfy the requirements of evaluation for physical movements in sports rehabilitation.
Mutual Information based Method for Unsupervised Disentanglement of Video Representation

Video Prediction is a challenging but interesting task of predicting future frames from a given set context frames that belong to a video sequence. Video prediction models have prospective applications in maneuver planning, healthcare, autonomous navigation and simulation. One of the major challenges in future frame generation is the high dimensional nature of visual data. To handle this, we propose a Mutual Information Predictive Auto-Encoder (MIPAE) framework that reduces the task of predicting high dimensional video frames by factorising video representations into content and low dimensional pose latent variables. Our approach leverages the temporal structure in the latent generative factors of video sequences by applying a novel mutual information loss to learn disentangled video representations. A standard LSTM network is used to predict these low dimensional pose representations. Content and the predicted pose representations are decoded to generate future frames. We also propose a metric based on mutual information gap (MIG) to quantitatively access the effectiveness of disentanglement on DSprites and MPI3D-real datasets. MIG scores corroborate the visual superiority of frames predicted by MIPAE. We also compare our method quantitatively on LPIPS, SSIM and PSNR evaluation metrics.
Feature Point Matching in Cross-Spectral Images with Cycle Consistency Learning

Feature point matching is an important problem because its applications cover a wide range of tasks in computer vision. Deep learning-based methods for learning local features have recently shown superior performance. However, it is not easy to collect the training data in these methods, especially in cross-spectral settings such as the correspondence between RGB and near-infrared images. In this paper, we propose an unsupervised learning method for general feature point matching. Because we train a convolutional neural network as a feature extractor in order to satisfy the cycle consistency of the correspondences between an input image pair, the proposed method does not require supervision and works even in cross-spectral settings. In our experiments, we apply the proposed method to stereo matching, which is a dense feature point matching problem. The experimental results, which simulate cross-spectral settings with three different settings, i.e., RGB stereo, RGB vs gray-scale, and anaglyph (red vs cyan), show that our proposed method outperforms the compared methods, which employ handcrafted features for stereo matching, by a significant margin.
Optimizing Model Training in Interactive Learning Scenarios

In the last years, developments in data collection, storing, processing and analysis technologies resulted in an unprecedented use of data by organizations. The volume and variety of data, combined with the velocity at which decisions must now be taken and the dynamism of business environments, pose new challenges to Machine Learning. Namely, algorithms must now deal with streaming data, concept drift, distributed datasets, among others. One common task nowadays is to update or re-train models when data changes, as opposed to traditional one-shot batch systems, in which the model is trained only once. This paper addresses the issue of when to update or re-train a model, by proposing an approach to predict the performance metrics of the model if it were trained at a given moment, with a specific set of data. We validate the proposed approach in an interactive Machine Learning system in the domain of fraud detection.
Boundary bagging to address training data issues in ensemble classification

The characteristics of training data is a fundamental consideration when constructing any supervised classifier. Class mislabelling and imbalance are major training data issues that often adversely affect machine learning algorithms, including ensembles. This work proposes extended bagging algorithms to better handle noisy and multi-class imbalanced classification tasks. These algorithms upgrade the sampling procedure by taking benefit of the confidence in ensemble classification outcome. The underlying idea is that a bagging ensemble learning algorithm can achieve greater performance if it is allowed to choose the data from which it learns. The effectiveness of the proposed methods is demonstrated in performing classification on 10 various data sets.
Lightweight, Single-Clock-Cycle, Multilayer Cipher for Single-Channel IoT Communication: Design and Implementation

The area of lightweight cryptography for constrained nodes has been quite well researched since the advent of RFID tags. However, the important issue of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated</i> design of a secure, ultra-low power, small-footprint IoT <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transceiver</i> has not received sufficient attention. The objective of this paper is to present such a transceiver and evaluate its data rate and power in the presence of the cryptographic overhead and compare its security with that of prior lightweight cryptographic algorithms. The proposed secure transceiver design is based on two main novel ingredients. The first is the Edge-Coded Signaling (ECS) protocol - a recently introduced technique for single-channel, high-data rate, low-power dynamic signaling that does not require any clock and data recovery. The second ingredient is a novel high-speed symmetric block cipher, HSA5/1, that is based on the well-known <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A5/1$ </tex-math></inline-formula> cipher and that generates a full keystream in one clock cycle. Both ingredients are combined to design a lightweight, high-speed, multilayer cipher with a tunable impact on ECS’s power consumption, energy efficiency, and data rate. The multilayer cipher provides additional layers of packet security that make it difficult for an attacker to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">even</i> receive a legitimate packet, let alone decrypt its data by attacking the HSA5/1 protocol itself. In addition, the multilayer encryption method may be easily configured to modulate the communication security overhead as a function of the number of clock cycles made available to the encryption/decryption stage. The secure ECS transceiver is prototyped and verified on both an embedded and an FPGA platform. It has also been synthesized for an ASIC implementation in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$65nm$ </tex-math></inline-formula> CMOS technology that is shown to preserve the low-power operation of ECS, consuming only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27~\mu W$ </tex-math></inline-formula> of power at a clock frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25MHz$ </tex-math></inline-formula> while requiring only one clock cycle for executing 148-bit-key encryption/decryption unit.
Application of Ontology in Semantic Web Searching of Flight Ticket as a Study Case

The development of technology allows people to obtain information easily. Information about goods or services that society needs can be obtained easily as many websites are built to provide that information. But this is also a challenge for the community. People should search and compare price of the cheapest goods or services according to their needs as well as in purchasing flight tickets. People also should look for flight ticket prices on many websites and compare the tickets themselves for each website. Thus, we need a website that can integrate information from heterogeneous website as a source of data and can help people comparing the price of flight tickets easily. Semantic web is considered as one solution to the problem. Semantic Web requires appropriate scrapping of data to retrieve the online data from specified websites. By using ontology, the semantic web can collect the data and enhance the search process of flight ticket.
Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures

Multimedia hashing is a useful technology of multimedia management, e.g., multimedia search and multimedia security. This paper proposes a robust multimedia hashing for processing videos. The proposed video hashing constructs a high-dimensional matrix via gradient features in the discrete wavelet transform (DWT) domain of preprocessed video, learns low-dimensional features from high-dimensional matrix via multidimensional scaling, and calculates video hash by ordinal measures of the learned low-dimensional features. Extensive experiments on 8300 videos are performed to examine the proposed video hashing. Performance comparisons reveal that the proposed scheme is better than several state-of-the-art schemes in balancing the performances of robustness and discrimination.
Developing the eXtended Tangible User Interface as an Experimental Platform for Geo CPS

Geotechnology, including GIS, remote sensing, and GNSS, is finding many applications in disaster risk reduction. Information flows in conventional systems tend to be one-way, however, from the physical world to cyber world, and their effectiveness often depends on professional training while support for participation is weak. This chapter overviews the history of the concept of the tangible user interface (TUI), which intuitively bridges GIS content in the cyber world onto a “tangible table,” a mock-up of the physical world for intuitive communication. After examining the common features of TUIs, we extend the concepts to recent applications in cyber-physical systems (CPSs) in the geographic dimension. We show how a Geo CPS platform (a CPS spatialized in the geographic world) with eXtended TUI (XTUI) can enhance the integration of information within cyber, physical and social spaces as well as the interactions among them in the application context. The system architecture and functions of Geo CPS with XTUI are constructed to reflect intuitive interactions of sensing, process and actuation (iSPA). Finally, we present a community trial at the Urban Living Lab in Yokohama City and discuss the potential for the Geo CPS platform with XTUI for disaster risk reduction.
Vehicle Detection and Count in the Captured Stream Video Using Machine Learning

The technology of vehicle detection in the captured video has implementation in the variety of fields. This emerging technology when implemented over the real-time video feeds can be beneficial for the missions of search and rescue in remote areas, where access is hampered by mountains and vast land areas without road networks. Areas afflicted by natural disaster (earthquake, flood) may be aided and improved by autonomous UAV systems, in military applications and civilians due to its ability to operate over large, difficult terrain. Rescue or surveillance missions include people and vehicle detection from aerial platforms. The supreme benefit of vehicle detection in the real-time streaming video feed is to track the terrorist intrusion over the borders and hit them. The recent terrorist attack occurred in the Indian soil at Uri, Pulwama, which could be avoided by using the enhanced vehicle detection techniques implied in the live stream video by machine learning. This chapter presents to recognize and identify the moving objects in the live video stream for specific interest. This chapter aims to explore the existing challenging issue in the area of unsupervised surveillance and security. The detection of vehicles is implemented with enhanced algorithms and machine learning libraries like OpenCV, TensorFlow, and others. The various approaches are used to identify and track the specific object through the trained model from the captured video.
A Deep Learning Method for Chinese writer Identification with Feature Fusion

Abstract Writer identification is one of the research hotspots of computer vision and pattern recognition, and it is of great significance in the fields of judicial authentication, file security protection, historical document analysis, and so on. However, many problems are still challenging due to the different writing sources, the common features of learning local features, and the implicit features of handwriting style. This paper uses the fusion of depth features and manual features to obtain handwriting style features from handwriting pictures. Firstly, the handwriting picture is pre-treatment and divided into small pixel blocks, and then the depth feature and LPQ feature information are extracted from each pixel block, and the depth features are reduced by PCA, and then the local features are encoded into global features through the Vlad algorithm. So far, the depth global feature and the LPQ global feature of a page of handwriting pictures are obtained, and the two features are combined as the global feature vector of the page of handwriting. And our method has achieved good results on the CASIA-HWDB data set.
CNN-based repetitive self-revised learning for photos' aesthetics imbalanced classification

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the low likelihood photo samples at the middle levels of aesthetics from the training data set based on the previously trained network. Further, the retained two networks are used in extracting highlight regions of the photos related with the aesthetic assessment. Experimental results show that the CNN-based repetitive self-revised learning is effective for improving the performances of the imbalanced classification.
Designing a mobile user experience student knowledge management system using Lean UX

Student Affairs at Telkom Purwokerto Institute of Technology has a lot of information and knowledge that all students need to know. However, this information and knowledge have not been maximally conveyed to students. Student affairs have a scope of work to handle the Student Creativity Program (PKM), Student Organization (Ormawa), scholarships, Student Activity Transcripts (TAK), and lectures. Therefore, one of the efforts to facilitate Student Affairs in conveying various knowledge is a Student Knowledge Management System mobile application. User Experience student KMS mobile application designed for mobile devices that focus on interface design. The development of this application interface uses the Lean UX method. The Lean UX method has four stages: Declare Assumptions, Create an MVP, Run an Experiment, and Feedback and Research. The application design is evaluated using the Enhanced Cognitive Walkthrough (ECW) to produce a prototype of a knowledge management system or Knowledge Management System for IT Telkom Purwokerto student affairs according to the user's wishes. The test results show success in all tasks, so it can be concluded that the application design has achieved a good user experience.
Real‐Time Frequency Adjustment of Images and Videos

Abstract We present a technique for real‐time adjustment of spatial frequencies in images and videos. Our method allows for both decreasing and increasing of frequencies, and is orthogonal to image resizing. Thus, it can be used to automatically adjust spatial frequencies to preserve the appearance of structured patterns during image downscaling and upscaling. By pre‐computing the image's space‐frequency decomposition and its unwrapped phases, these operations can be performed in real time, thanks to our novel mathematical perspective on frequency manipulation of digital images: interpreting the problem through the theory of instantaneous frequencies and phase unwrapping. To make this possible, we introduce an algorithm for the simultaneous phase unwrapping of several unordered frequency components, which also deals with the frequency‐sign ambiguity of real signals. As such, our method provides theoretical and practical improvements to the concept of spectral remapping, enabling real‐time performance and improved color handling. We demonstrate its effectiveness on a large number of images subject to frequency adjustment. By providing real‐time control over the spatial frequencies associated with structured patterns, our technique expands the range of creative and technical possibilities for image and video processing.
Exploring Uncertainty Measures for Image-caption Embedding-and-retrieval Task

With the significant development of black-box machine learning algorithms, particularly deep neural networks, the practical demand for reliability assessment is rapidly increasing. On the basis of the concept that “Bayesian deep learning knows what it does not know,” the uncertainty of deep neural network outputs has been investigated as a reliability measure for classification and regression tasks. By considering an embedding task as a regression task, several existing studies have quantified the uncertainty of embedded features and improved the retrieval performance of cutting-edge models by model averaging. However, in image-caption embedding-and-retrieval tasks, well-known samples are not always easy to retrieve. This study shows that the existing method has poor performance in reliability assessment and investigates another aspect of image-caption embedding-and-retrieval tasks. We propose posterior uncertainty by considering the retrieval task as a classification task, which can accurately assess the reliability of retrieval results. The consistent performance of the two uncertainty measures is observed with different datasets (MS-COCO and Flickr30k), different deep-learning architectures (dropout and batch normalization), and different similarity functions. To the best of our knowledge, this is the first study to perform a reliability assessment on image-caption embedding-and-retrieval tasks.
Finding Keystone Citations for Constructing Validity Chains among Research Papers

New discoveries in science are often built upon previous knowledge. Ideally, such dependency information should be made explicit in a scientific knowledge graph. The Keystone Framework was proposed for tracking the validity dependency among papers. A keystone citation indicates that the validity of a given paper depends on a previously published paper it cites. In this paper, we propose and evaluate a strategy that repurposes rhetorical category classifiers for the novel application of extracting keystone citations that relate to research methods. Five binary rhetorical category classifiers were constructed to identify Background, Objective, Methods, Results, and Conclusions sentences in biomedical papers. The resulting classifiers were used to test the strategy against two datasets. The initial strategy assumed that only citations contained in Methods sentences were methods keystone citations, but our analysis revealed that citations contained in sentences classified as either Methods or Results had a high likelihood to be methods keystone citations. Future work will focus on fine tuning the rhetorical category classifiers, experimenting with multiclass classifiers, evaluating the revised strategy with more data, and constructing a larger gold standard citation context sentence dataset for model training.