Reviewerly/exHarmony huggingface dataset:
SE-dual path networks combined with a navigator for fine-grained classification

Recognizing fine-grained categories is difficult due to the challenges of discriminative region localization and fine-grained feature learning. To handle this circumstance, we propose a novel model termed SDN-Net for SE-DPN-Navigator Networks, which consists of DPN (Dual Path Networks), SE-blocks (Squeeze-and-Excitation Blocks) and a Navigator. DPN shares common features while maintaining the flexibility to explore new features. Moreover, we add SE-blocks into DPN to make up the SE-DPN which acts as a feature extractor of the proposed model, SE-blocks helps the model learn to use global information to selectively emphasize informative features and suppress less useful ones. We also use a Navigator to help the model to detect most informative regions without extra bounding box/part annotations. Our model can be trained end-to-end. With the great cooperation between these three components, we achieve state-of-the-art performance on two publicly available fine-grained recognition datasets (CUB-200-2001 and Stanford Cars). Besides, We have done ablation studies and confirmed the effectiveness of each components in the proposed model.
Improving the detection of underwater metal-containing objects by fusion of ferromagnetic sensors data with vehicle’s navigational data

Abstract Currently, the task of developing the Arctic shelf is relevant, because there are proven reserves of minerals – oil and gas. Undoubtedly, researchers and engineers, after installing the appropriate underwater infrastructure, will face the task of maintenance and repair of communications. For this purpose, autonomous unmanned underwater vehicles (AUV) can be used, with e.g. the task of inspecting pipelines. The article describes the processing algorithm for the signal from passive ferromagnetic sensors mounted on an AUV carrier used to search for metal-containing objects at the sea bottom. A scheme for such a measurement is proposed - the installation of two sensors at opposite ends of the carrier. This allows to measure the gradient of magnetic field between the sensors. The characteristic form of such a signal and the dependence of the signal on the motion parameters of the vehicle and external factors are determined. To eliminate false positives, filters are used based on the readings of the position, speed and orientation sensors of the navigation system. Using data on the motion parameters of the device allows to generate a reference signal, which is used to validate the detection of an object using the cross-correlation method. The use of data on orientation angles makes it possible to compensate for the influence of the orientation of the device in the Earth’s magnetic field.
Blockchain based Application for Exchange of Left over Foreign Currency

Along with the increase in international travel there is a consequential rise in the amount of leftover currency (LFC) or the paper bills/coins that people cannot spend during their international travel. This cash mostly stay in people's pockets and travels back to people's home country. There are ways that LFC can be brought back into the economy such as souvenir shopping at the airports, donations to a charity wending machine or throwing into fountains to wish. However, these means of utilization have various limitations associated with them. Hence, there is a need for a transparent and a more convenient method to spend the LFC. In this work, we implement a system that solves the LFC problem by converting it into a cryptocurrency. The process of conversion requires depositing the LFC at a kiosk. The users can then use an android application to receive the cryptocurrency which they deposit in the foreign country. The amount of cryptocurrency bought by current rate appears in the users' e-wallet. The kiosk backend handles the currency and storage of critical transactions in a blockchain. The proposed system gives a fairly simple solution for a long lasting problem that is only possible because of the blockchain technology.
Statistical Analysis of Noise Estimation in MRI: Modeling, Filtering and Estimation

"Statistical Analysis of Noise Estimation in MRI: Modeling, Filtering and Estimation." Technometrics, 61(3), pp. 426–427
Pose Estimation Technique of Scattered Pistons Based on CAD Model and Global Feature

To estimate the pose of scattered pistons, an estimation technique was proposed based on the CAD model and global features. By automatic selecting the foreground point, the Min-Cut algorithm was improved from an interactive semi-automatic and binary-segmentation algorithm to a fully automatic and multiple-segmentation algorithm. The selection methods of viewpoint and cluster center of clustered viewpoint feature histogram (CVFH) were optimized. The point cloud of pistons was collected by Kinect, and the improved Min-Cut algorithm was used to segment the piston cloud. The optimized CVFH feature and the Camera Roll Histogram (CRH) feature of the pistons cloud were extracted, and the piston poses hypotheses were got by matching the features with the offline template library. The ICP algorithm was used to accurately match the pose. The false results were removed with the hypotheses verification and the six-degree-of-freedom posture of the piston was obtained. The average time of estimating the piston pose is 1.84s, and the average correct recognition rate is 98.61%. The proposed algorithm meets the requirements of grasping the pistons by the manipulator.
Segmentasi Citra Ct Scan Paru-Paru Dengan Menggunakan Metode Active Contour

The lungs are one of the important organs in the human body. Because the lungs are one of the holders of control in the circulatory system in the human body. According to some internal medicine specialists, the lungs are the most vulnerable internal organs to damage, this is due to lung function that comes in direct contact with the outside environment such as inhaled cigarette smoke, vehicle fumes, or is allergic to something. So that the lungs are hollow and eroded. Therefore detection of human lungs is needed. To find out damage to the lungs, a CT scan image process is needed. In CT-Scan images there is information that is used to analyze and know the shape of an object from the lung itself. To obtain this information, it is necessary to do a segmentation process. In this study using the Active Contour method. Active Contour is a segmentation method that is often used because this method uses a closed curve model that can move wide or narrow. The purpose of this study was to segment the lungs on CT-Scan images by using the active contour method to determine the value of the area and perimeter of the lung section that was good and to find the accuracy value of the method used for a CT-Scan image. The results of lung segmentation were carried out a system trial carried out on 15 CT-Scan image data obtained an average accuracy of 80%
Adaptability Performance Evaluation of University Teachers’ informatization Teaching based on Entropy Weight Bayesian Bethod

Using entropy method to calculate ability, behavior, environment, interpersonal, and psychological adaptability, combined with Bayesian method to calculate the post-test probability of the adaptive performance evaluation level of 10 teachers in a university, in order to reflect the performance level of information teaching adaptability of different job titles, positions and categories. The results show that, compared with the cumulative integral method, the entropy right Bayesian method is more objective and closer to the actual teacher informatization teaching adaptability performance grade. Finally, the shortcomings of entropy weight Bayesian method in calculating the posterior probability are summarized, and it is proposed that a-step refinement score is needed to solve the problem in data acquisition.
Analysis on the Causes of Safety Risks About Food of Purchasing Online

Food safety is an extremely important issue of people’s livelihood and people’s hearts, it relates to the real interests of thousands of households. With the rapid progress of network technology and the development of food industry and the innovation of food sales channel, foods sold on the internet quickly appeared in the dinning table of residents, as same time as people enjoy labor-saving, economic, fast and convenient experience in foods of purchasing online, it brings a lot of safety risks on purchasing and eating to consumers. We must face up to these security risks and analyze the reasons of this question, in order to put forward policies and measures to solve the safety risks of food of purchasing online, ensure the safety of online food sales from the source, maintain the people’s security of lives and health and property.
Target Filtering and Tracking

Up to this point, we have presented methods for detection of target properties, specifically, range, velocity, and DOA. Although this information representing instantaneous target state could be the main objective of radar processing, in automotive radar processing, tracking moving targets is of paramount importance. The processing of detected radar targets using filtering and tracking methods for the purpose of capturing target motion dynamics is the goal of this chapter.
Evaluation of Iterative Calibration of Vehicle Cameras Using Reference Information from Traffic Signs

Intrinsic camera parameters can be estimated by camera calibration using multiple images of a priori known reference points on a test field. In particular during driving, vehicle cameras might be exposed to mechanical and thermal effects, leading to a change of these parameters over time and making iterative camera calibration useful to correct for these changes. As on roads no special test fields are available, reference information has to be extracted from road scene images. In this contribution, a method for iterative calibration of a vehicle camera using references on traffic signs is proposed. The references are obtained from the shape of traffic signs detected in road scene images, exploiting that the shape is known a priori. A test with a road scene image sequence acquired during a test drive in a car park equipped with traffic signs shows no decrease in the standard deviation of the parameters iteratively estimated with the proposed method compared to test field calibration.
Modelling Negotiation Strategies

The paper introduces a work in progress on modelling of negotiation. A dialogue model is introduced that includes a reasoning model. Negotiation strategies are considered where both participants having the same communicative goal try to overcome possible obstacles ahead achieving the goal, by presenting various arguments and counterarguments. Analysis of a corpus of authentic human-human spoken dialogues is carried out in order to evaluate the strategies. A dialogue system is under development where the strategies are implemented. Our aim is to create such a dialogue system that interacts with the user in a natural language following norms of human conversation.
Robust Audio Watermarking Algorithm Based on Moving Average and DCT

Noise is often brought to host audio by common signal processing operation, and it usually changes the high-frequency component of an audio signal. So embedding watermark by adjusting low-frequency coefficient can improve the robustness of a watermark scheme. Moving Average sequence is a low-frequency feature of an audio signal. This work proposed a method which embedding watermark into the maximal coefficient in discrete cosine transform domain of a moving average sequence. Subjective and objective tests reveal that the proposed watermarking scheme maintains highly audio quality, and simultaneously, the algorithm is highly robust to common digital signal processing operations, including additive noise, sampling rate change, bit resolution transformation, MP3 compression, and random cropping, especially low-pass filtering.
Improved RPN for Single Targets Detection Based on the Anchor Mask Net

Common target detection is usually based on single frame images, which is vulnerable to affected by the similar targets in the image and not applicable to video images. In this paper, anchor mask is proposed to add the prior knowledge for target detection and an anchor mask net is designed to improve the RPN performance for single target detection. Tested in the VOT2016, the model perform better.
Cascade Region Proposal Networks for Object Detection in the Wild

Although significant progresses have been made in object detection on common benchmarks (i.e., Pascal VOC), object detection in the wild is still challenging due to the serious data inadequacy and imbalance. To address this challenge, we construct a cascade framework which consists of multiple region proposal networks, referred to as C-RPNs. The essence of C-RPNs is adopting multiple stages to mine hard samples and learn better classifiers. Meanwhile, a feature chain and a score chain are proposed to help learning more discriminative representations for proposals. Moreover, a loss function of cascade stages is designed to train cascade classifiers through backpropagation. Our newly proposed object detection method is evaluated on Pascal VOC and a challenging dataset of littoral birds named BSBDV 2017. Our method outperforms baseline by an obvious margin, validating its efficacy for detection in the wild.
Occluded Object Classification with Assistant Unit

This paper presents a new convolutional neural network (CNN) architecture which improve performance of occluded-object classification by adding an assistant unit. The classification architecture is OCC-VGG19. The OCC-VGG19 outputs two parts classification information. First information is occluded state of target, and second information is the objectness information of target. To access the performance of the proposed architecture, we generate a new dataset that referred to as OCC-CIFAR10 based on CIFAR-10. The OCC-CIFAR10 include 40,000 original images and 10,000 generated image that are occluded by noise, and the OCC-CIFAR10 samples are RGB color images with size 32 × 32. The OCC-CIFAR10 is used in both of training and testing step. Experimental results show that the proposed assistant unit enhance network robustness in occluded-objects classification task.
GitViz: An Interactive Visualization System for Analyzing Development Trends in the Open-Source Software Community

This study proposes a visualization that can assist computer scientists and data scientists to make decisions by exploring technology trends. While it is important for them to understand the technology trends in the rapidly changing computer science and data science fields, it takes considerable time and knowledge to acquire good information about these trends. Particularly, data/computer scientists with little experience in the field find it difficult to obtain information on such trends. Therefore, we propose a visualization system that can easily and quickly explore the technology trends in computer and data science. This study aims to identify the key technologies and developers in a specific field, and other technologies deeply related to specific technologies, and explore the changes in popularity of technologies, languages, and libraries over time. This study includes two case studies to obtain information using the proposed visualization. We demonstrate our system with GitHub repositories data.
Binary codes from Paley graphs of prime-power-square order

We examine the binary code from the row span of an adjacency matrix of the Paley graph of order q2, where q is a prime power such that q2≡1(mod8). We show that the code and its dual are respectively [q2,q2−12,q+1]2 and [q2,q2+12,q]2 codes. We obtain spanning sets of minimum-weight vectors for these codes, and in the case of q a prime we show that the code can be used for partial permutation decoding using a certain information set.
AIP: Saving the DRAM Access Energy of CNNs Using Approximate Inner Products

In this work, we propose AIP (Approximate Inner Product), which approximates the inner products of CNNs' fully-connected (FC) layers by using only a small fraction (e.g., one-sixteenth) of parameters. We observe that FC layers possess several characteristics that naturally fit AIP: the dropout training strategy, rectified linear units (ReLUs), and top-n operator. Experimental results show that 48% of DRAM access energy can be reduced at the cost of only 2% of top-5 accuracy loss (for VGG-f).
The Modeling Platform for Video Surveillance Knowledge Base

Video surveillance applications involve video content modeling, video understanding, video retrieval service and data management, which rely on the exploration of video surveillance knowledge base. In this study, we explore a modeling platform for video surveillance knowledge base, which supports the establishment of classification and association relations between concepts, and to form the domain ontology related to video surveillance applications. The platform contains three main tools, with which reasoning and new knowledge is carried out on the basis of the image semantic annotation information.
Cascading Forgetting in Product Development Challenges and Evaluation

Abstract Vast amounts of information and knowledge is produced and stored within product design projects. Especially for reuse and adaptation there exists no suitable method for product designers to handle this information overload. Due to this, the selection of relevant information in a specific development situation is time-consuming and inefficient. To tackle this issue, the novel approach Intentional Forgetting (IF) is applied for product design, which aims to support reuse and adaptation by reducing the vast amount of information to the relevant. Within this contribution an IF-operator called Cascading Forgetting is introduced and evaluated, which was implemented for forgetting related information elements in ontology knowledge bases. For the evaluation the development process of a test-rig for studying friction and wear behaviour of the cam/tappet contact in combustion engines is analysed. Due to the interdisciplinary task of the evaluation and the characteristics of semantic model, challenges are discussed. In conclusion, the focus of the evaluation is to consider how reliable the Cascading Forgetting works and how intuitive ontology-based representations appear to engineers.
Balancing elliptic curve coprocessors from bottom to top

In 2016, Renes et al. were the first to propose complete addition formulas for Elliptic Curve Cryptography (ECC) on Weierstrass curves. With these formulas, the same set of equations can be used for point addition and point doubling, which makes software and hardware implementations less vulnerable to side-channel (SCA) attacks. Further, all inputs are valid, so there is no need for conditional statements handling special cases such as the point at infinity. This paper presents the first ASIC design of the complete addition formulas of Renes et al. Each computation layer in the design is balanced, from the field arithmetic to the point multiplication. The design explores two datapaths: a full-width Montgomery Multiplier ALU (MMALU) with a built-in adder and a serialized version of the MMALU. The interface sizes of the MMALU are optimized through an exploration of the design parameters. The register file size is minimized through an optimal scheduling of the modular operations. The top-level point multiplication is implemented using the Montgomery ladder algorithm, with the additional option of randomizing the execution order of the point operations as a countermeasure against SCA attacks. The implementation results after synthesis are generated using the open source NANGATE45 library.
Towards the Analysis of How Anonymization Affects Usefulness of Health Data in the Context of Machine Learning

The volume and quality of patient data stored and collected have drastically grown in the last years. Such data can be analyzed by machine learning algorithms to improve health and well-being. However, while the distribution of data is benefitial, it should be performed in a way that preserves patient privacy. It would be expected to obtain useful information from the use of machine learning algorithms applied to both anonymized and non-anonymized datasets. However, those algorithms can generate lower quality results (even invalid ones) due to information loss during the anonymization process. We aim to analyze the relationship between anonymization and data utility/information loss, through the use of different algorithms and information loss metrics. With that aim, we plan to 1) analyze how real algorithms used on real data are affected by different anonymization techniques; 2) to use the lessons learned to design useful metrics for measuring the information loss after annonymization; and 3) to validate the proposed metrics by testing them in other environments with different types of data. The expected contributions of the research will be to obtain more information about how anonymization techniques affect the data usefulness, together with additional knowledge about the more suitable machine learning algorithms to be used to anonymized data, and a set of metrics to measure the usefulness of anonymized data would be developed.
Recognition of the Hungarian fingerspelling alphabet using Recurrent Neural Network

The aim of this paper is to introduce a Recurrent Convolutional Neural Network based on depth data to recognize the signs of the Hungarian fingerspelling alphabet. The training dataset contains depth data of 27 static and 15 dynamic signs. A 88.6% classification accuracy was measured for during the test with the recommended model in this paper, which is a special type of recurrent network containing LSTM and convolutional layers.
Multi-Covert Channel Attack in the Cloud

Frequently, it is believed that the adoption of encryption is adequate to ensure the safety of the message. However, encryption only restricts unapproved individuals from decoding the message. Whereas in many circumstances, the mere presence of communication or variations in communication patterns, such as an increase in message rate, is sufficient to raise doubt and initiate alerts. One of the best ways to bypass that is by using covert channels. In this paper, we create innovative protocols that use multiple covert channels working in parallel to exfiltrate data from a remote-controlled machine to our server. The provided protocols guarantee the completeness and unforgeability of the exfiltrated data. We analyze the difference in performance between the created protocols and provide mathematical equations to evaluate them.
Lifted Message Passing for Hybrid Probabilistic Inference

Lifted inference algorithms for first-order logic models, e.g., Markov logic networks (MLNs), have been of significant interest in recent years. Lifted inference methods exploit model symmetries in order to reduce the size of the model and, consequently, the computational cost of inference. In this work, we consider the problem of lifted inference in MLNs with continuous or both discrete and continuous groundings. Existing work on lifting with continuous groundings has mostly been limited to special classes of models, e.g., Gaussian models, for which variable elimination or message-passing updates can be computed exactly. Here, we develop approximate lifted inference schemes based on particle sampling. We demonstrate empirically that our approximate lifting schemes perform comparably to existing state-of-the-art for models for Gaussian MLNs, while having the flexibility to be applied to models with arbitrary potential functions.