Reviewerly/exHarmony huggingface dataset:
Anti-slip Control Research of Brake System with Laser Additive Manufacturing Brake Disc

The material difference of laser additive manufacturing brake discs and traditional brake discs result in inconsistent friction coefficients of contact surfaces of the brake discs and the brake shoes during the braking process. The sliding control strategy needs to be optimized to meet the requirements of new brake discs. The model of EP2002 brake system pneumatic valve unit (PVU) is built up in simulation software of AMESim. And the correctness of the model is verified through the simulation of service brake and emergency brake. The dynamics model of train is built in MATLAB/Simulink environment, and anti-slip control logic is compiled. Considering the friction characteristics of laser additive manufacturing brake discs, anti-slip control logic was developed. To verify the validity of anti-slip logic co-simulation is conducted between AMESim and MATLAB/Simulink. The simulation results of service brake and emergency brake show that the PVU model of EP2002 reacts consistently with real systems, which verifies the correctness of the model. The simulation results of anti-slip control show that a stable performance of anti-slip is achieved even continuous slip occurred, which means that the designed anti-slip control logic meets the control requirements. The research results provide an effective model for the actual design of the train braking system and solving of failures.
Multi-Proxy Constraint Loss for Vehicle Re-Identification

Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.
Determination of Basic Probability Assignment Based on Probability Distribution

In the application of Dempster-Shafer (D-S) evidence theory, the determination of basic probability assignment (BPA) is a key step. How to determine BPA is an open issue. To solve this problem, a new method to determine BPA based on probability distribution is proposed in this paper. First, the approximate distribution of training set is constructed. Next, the distance is proposed according to the approximate distributions of the training set. Then the similarity degree is calculated based on the distance. Finally, the similarity degree is normalized to obtain BPA. The effectiveness of our proposed method is shown by classifying the dataset Iris.
Predicting visual perceivability of scene objects through spatio-temporal modeling of retinal receptive fields

Retinal processing of a visual scene is an essential step of human visual perception. Although foveal vision is linked to the visual attention, perception is by not means limited to this region. Rather, the retinal field of view ranges from 60° nasal to 107° temporal, and from 70° superior to 80° inferior. Whether a scene object is visually perceived depends on both its visual appearance as well as its retinal location. We present a framework to evaluate the visual stimulus of a scene object with regard to different types of retinal receptive fields. Driven by gaze location provided by an eye tracker, the estimated retinal response considers the visual appearance of the object, its eccentricity in the users field of view, and the capabilities at the local retinal region. A desktop experiment shows that, in additional to foveal processing, the estimated retinal response leads to a significant increase in classification accuracy in terms of whether an object is reported as perceived by the user.
Use of Blockchain Partnerships to Enable Transparency in Supply Chain Digitization

Abstract This chapter introduces the use of blockchains to increase supply chain traceability and visibility. It argues for the use of technologies such as radiofrequency identification to realize the full potential of tracking every product and component in the supply chain, from its origin to its manufacture, use, and disposal on a real-time basis. The inherent advantages of blockchains remove the need for a central authority and promote decentralization of record keeping, hence increasing the integrity and security of the data. Blockchains by themselves are a platform that enables the transmission and sharing of data across the supply chain in a secure, fast, and robust manner. The technology works best when used with other data-capturing devices such as radiofrequency identification, which are already in use at different echelons of a supply chain. The chapter concludes by arguing that emergent technologies like the Internet of things work best by incorporating blockchains, because that would prevent the abuse of data by centralized systems and authorities and at the same time reduce the critical points of failure that centralized systems are prone to.
Reversible Watermarking Techniques

Digital content security gained immense attention over past two decades due rapid digitization of industries and government sectors, and providing security to digital content became a vital challenge. Digital watermarking is one prominent solution to protect digital content from tamper detection and content authentication. However, digital watermarking can alter sensitive information present on cover-content during embedding, then the recovery of exact cover-content may not be possible during extraction process. Moreover, certain applications may not allow small distortions in cover-content. Hence, reversible watermarking techniques of digital content can extract cover-content and watermark completely. Additionally, reversible watermarking is gaining popularity by an increasing number of applications in military, law enforcement, healthcare. In this chapter, the authors compare and contrast the different reversible watermarking techniques with quality and embedding capacity parameters. This survey is essential due to the rapid evolution of reversible watermarking techniques.
Examining the influence of emotional arousal and scam preventive messaging on susceptibility to scams

With the increase in scams globally and the elusive methods of perpetrators, law enforcement agencies have turned to public education and awareness programs to decrease the number of scam victims. This has also raised a need to look into the psychology of scams and how they can be prevented. Emotional arousal has been shown to hinder cognitive decision-making processes in scam victims, subsequently influencing them to fall prey to scams. Despite this, messages used in scam prevention campaigns have been framed in ways that appeal to rational cognitive processes. This exploratory study examined two research questions: do (a) the type of messaging used in scam prevention posters (cognitive-focused poster vs. emotion-normalising poster) and (b) emotional arousal (positive arousal vs. negative arousal vs. no arousal) influence one’s susceptibility to scams? Susceptibility to scams was measured through participants’ intention to purchase items during a fake e-commerce scam scenario. Emotional arousal was measured with a combination of heart rate variability (HRV) data obtained through wearable heart rate trackers, and self-report scales. Results of the study showed that participants who viewed emotion-normalising posters demonstrated a lower susceptibility to scams compared to those who viewed the cognitive-focused poster. However, emotional arousal was not found to have any influence on one’s susceptibility to scams. These findings serve to inform crime prevention campaigns by law enforcement. Findings from this exploratory study can also encourage further research into the scam prevention research.
SMOTE-Cov: A New Oversampling Method Based on the Covariance Matrix

Nowadays, many machine learning tasks involve learning from imbalanced datasets, leading to misclassification of the minority class. One of the state-of-the-art approaches to “solve” this problem at the data level is the Synthetic Minority Oversampling Technique (SMOTE), which in turn uses KNN to select and generate new instances. However, those approaches do not take into account the attributes’ dependency relationship. This chapter presents SMOTE-Cov, a modified SMOTE that uses the Covariance Matrix instead of KNN to balance datasets, with continuous attributes and binary class. We implemented two variants SMOTE-CovI, which generates new values within the interval of each attribute, and SMOTE-CovO, which allows some values to be outside the interval of the attributes. SMOTE-Cov was validated by means of an experimental study using C4.5 as a classifier. The results show that our approach displays similar performance to the state-of-the-art approaches. After applying the statistical tests of Friedman and Holm, we did not find any big significant difference.
Real-Time Processing of High-Resolution Video and 3D Model-Based Tracking for Remote Towers

Abstract During the past decade, a new approach to providing air traffic services to airports from a remote location has been established, known as remote or digital tower. High quality video data is a core component in remote tower operations as it inherently contains a huge amount of information on which a controller can base decisions. The total resolution of a typical remote tower setup often exceeds 25 million RGB pixels and is captured at 30 frames per second or more. It is thus a challenge to efficiently process all the data in such a way as to provide relevant real-time enhancements to the controller. In this paper we describe the development of number of improvements and discuss how they can be implemented efficiently on a single workstation by decoupling processes, implementing attention mechanisms and utilizing hardware for parallel computing.
Nonperturbative leakage elimination for a logical qubit encoded in a mechanical oscillator

This work presents a theoretical scheme to suppress decoherence-induced leakage errors of a single-mode harmonic oscillator used to encode a qubit in continuous-variable systems by introducing a nonperturbative leakage elimination operator.
HoloSelecta dataset: 10’035 GTIN-labelled product instances in vending machines for object detection of packaged products in retail environments

To assess the potential of current neural network architectures to reliably identify packaged products within a retail environment, we created an open-source dataset of 295 shelf images of vending machines with 10'035 labelled instances of 109 products. The dataset contains photos of vending machines by the provider Selecta, the largest European operator of vending machines. The vending machines are a mix of machines in public and private office spaces. The vending machines contain food as well as beverage products. The product instances in the vending machine images are labelled with bounding boxes, where a bounding box encapsulates the entire product with as little overlap as possible. The labels corresponding to the bounding box consist of a structured, human-readable labels including brand, product name and size as well as the GTIN of the product. The GTIN is the global standard to identify products in the retail environment and therefore increases the value as a dataset for the retail industry. Contrary to typical object detection datasets that choose labels at a higher level such as a can or bottle for a much wider variety of objects, this dataset chooses a far more detailed label that depends less on the shape but rather on the exact design of the product. The dataset falls into the category of object detection datasets with a large number of objects, which next to the GTIN label, represents a main differentiator of the dataset to other object detection datasets.
Chinese Calligraphy Character Generating via CGAN with a Multi-subnet Parallel and Cascade Generator

Chinese character generation is one of the key technologies in Chinese character intelligent design. Most generation methods cannot solve the situation where the generated Chinese character strokes have artifact or even miss. The main reason is that the generator is not strong enough. Therefore, in this paper, we propose a multi-subnet parallel and cascade generator, which combines the conditional generation adversarial network (CGAN) to realize the generation of calligraphy Chinese characters. This method divides a Chinese character generation task into several parts undertaken by each sub-generator, and the output of each sub-generator will contain certain image information, which will increase with the rise of sub-generator hierarchy. It allows each sub-generator to focus on its own task, generating more realistic characters eventually. Finally, the test set performs better than the CGAN with a single generator.
Utility system for farmers using data analytics and blockchain – KrishiCoin

In the present world there is no transparency between data exchanged and information that is traded between farmers and the government administration. Because of the mediation of government authorities in the middle, data may be altered for their own advantages and development of agriculture industry will be hindered. Building up a decentralized, distributed and a verified secure framework to address these issues. The blockchain is utilized to make exchanges between the administration and farmers which decides the minimum support price. The model goes for making security in the chain and exchanges with the goal that the information isn’t altered. Using blockchain in the field of agriculture is to give security to the information traded between the farmers and government. Due to non-transparency in the exchanges and having government authorities in between, there is an opportunity of misusing the information for personal needs which is a noteworthy drawback to the farmers, along these lines blocking the development of the agriculture business. In order to provide security and privacy to the transactions and data exchange, the blockchain technology is used. Likewise, the sentiment and exploratory analysis causes the general population to get a thought regarding the continuous patterns in blockchain and cryptocurrency, which they can utilize to contribute or find out about the equivalent.
Deposit Location Identification Based on Feature Decomposition of High-Resolution Remote Sensing Images

In modern mineral exploration applications, remote sensing technologies have been widely used and affirmed by engineering and mineral industries due to their unique technical advantages. With the advancement of remote sensing technologies, multiple geological remote sensing-derived prospecting methods have been developed. With the development of more accurate sensors, the detection band has not been segmented, and the spectral resolution of such sensors is constantly being improved. Thus, the accuracy of remote sensing geological prospecting methods has improved, and geological prospecting results have shifted from being qualitative to quantitative in nature. In this article, high-resolution remote sensing images are used to extract the ore controlling factors of deposits. The color, shape, texture and other image shapes produced by high-resolution remote sensing images are fully exploited to comprehensively mine the available data utilizing mathematics, image processing methods and other technologies to systematically identify prospective target areas. Based on an analysis of the metalloorganic geological characteristics detected in the study area, combined with multisource data such as geophysical and geochemical exploration-derived observations, the proposed remote sensing model describing the deposits in the study area is summarized. The research results show that deposit location identification technologies based on high-resolution remote sensing image feature decomposition have the potential to provide a reliable basis for peripheral exploration and deposit positioning in geological and mineral exploration studies.
Parallel Message Encryption Through Playfair Cipher Using CUDA

The well-known multi-letter encryption cipher is Playfair cipher although a wide variety of techniques have been employed for encryption and decryption. The Playfair cipher shows a great advancement over other encryption methods. This cipher technique is used for encrypting and decrypting the pair of characters together. This paper proposes an idea to solve the traditional Playfair cipher through parallel algorithm in order to encrypt the given message. A key is used to encrypt the message. The idea is to reduce the time complexity of sequential Playfair cipher by parallelizing it and using the power of GPUs to the maximum. Instead of making one process do the encryption, a number of threads are used to encrypt letters simultaneously. This is achieved using CUDA.
Secure User Privacy in Population Physique Clustering and Prediction Based on Sport Questionnaires

Population physique is one of the key aspects for measuring and evaluating the healthy degree or living level of the population of a nation. Many population physique evaluation models or tools have been developed in the past decades, among which questionnaire is an essential and promising way to help achieve the above population physique measurement goal. Typically, through designing, distributing and collecting a variety of sport questionnaires associated with people's living conditions or sport preferences, we can quantify, observe and cluster the healthy degree of the whole nation's population objectively and scientifically. However, the above sport questionnaire-based population physique analysis methods are often time-consuming as a considerable amount of sport questionnaires needs to be compared. Moreover, the sport questionnaires filled out by people often contains some sensitive information that is not supposed to be disclosed to other people, which also call for appropriate privacy protection measures. Inspired by the above two challenges, we introduce hash techniques into population physique evaluation process and afterwards, we propose a hash-based population physique clustering and prediction method that is efficient in time cost and effective in terms of privacy protection. At last, we designed experiments based on a dataset to prove the effectiveness of our proposal in this research work.
Waypoint Tracking for Collision Avoidance Using Artificial Potential Field

In this paper, we present a waypoint tracking scheme for collision avoidance using artificial potential field (APF). It is shown that using APF is effective in avoiding collision for path planning but it is not reported yet using APF for waypoint tracking. Since waypoints are given in discrete and even irregularly, smoothly joining the local detour path due to an obstacle and the global path given by waypoints is necessary. Otherwise, severe yawing of the heading angle of the vehicle may happen. In this paper, to resolve the problem, we propose a new path planning method based on waypoints using APF. This proposed method was validated with the empirical kinematics vehicle model using MATLAB/Simulink and waypoints given by a leading vehicle driven by a human driver. The simulation results show that the proposed path planning algorithm based on APF is effective for the waypoint tracking scenario.
Geometry-attentive relational reasoning for robust facial landmark detection

In this paper, we propose a geometry-attentive relational reasoning approach to investigate the problem of robust facial landmark detection, especially when faces were captured in wild conditions. Unlike existing methods which usually cannot explicitly exploit the geometric relationship among different landmarks, our approach aims to reason about the intrinsic geometry-aware relations among landmarks for feature enhancement. To achieve this, we carefully develop an interpretable and plug-and-play module to reinforce the discriminativeness and uniqueness of feature maps, which typically operates on all possible pairs on the immediate inter-landmark heat maps. Among these pairing maps, our model learns to infer the meaningful relational clues in the transformed feature space on condition of holistic facial shape prior. For permutation order invariance, we pool these features as a single aggregated relational feature. To further improve the performance, we simply equip the proposed module inside the backbone hourglass networks. The experimental results on the standard benchmarking datasets indicate the effectiveness of our proposed approach.
A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation

Unsupervised Domain Adaption (DA) is an approach for adapting a data-driven model to new data without labels. Recent work on Remaining Useful Lifetime (RUL) estimation of aero engines yielded promising results for this approach. However, the current evaluation framework for DA is of limited significance when used for RUL estimation. It assumes a use case where a large number of fully degraded systems are available for adaption, which makes unsupervised DA in itself unnecessary. It is shown that the current framework overestimates adaption performance and obscures potential, negative effects of DA on performance. We propose a novel evaluation framework for unsupervised DA, specialized in RUL estimation, that takes the number of available systems and their grade of degradation into account. It enables an informed performance comparison of DA methods. We detail the framework's capabilities on two DA methods and show that unsupervised DA delivers improved RUL estimations under real-life scenarios, as well.
Fully asynchronous stochastic coordinate descent: a tight lower bound on the parallelism achieving linear speedup

We seek tight bounds on the viable parallelism in asynchronous implementations of coordinate descent that achieves linear speedup. We focus on asynchronous coordinate descent (ACD) algorithms on convex functions which consist of the sum of a smooth convex part and a possibly non-smooth separable convex part. We quantify the shortfall in progress compared to the standard sequential stochastic gradient descent. This leads to a simple yet tight analysis of the standard stochastic ACD in a partially asynchronous environment, generalizing and improving the bounds in prior work. We also give a considerably more involved analysis for general asynchronous environments in which the only constraint is that each update can overlap with at most q others. The new lower bound on the maximum degree of parallelism attaining linear speedup is tight and improves the best prior bound almost quadratically.