Reviewerly/exHarmony huggingface dataset:
Designing Recurrent Neural Networks for Monitoring Embedded Devices

Embedded systems play an important role in various tasks in many areas of our lives. In the case of safety-critical applications, e.g., in the fields of autonomous driving, medical devices or control of unmanned aerial vehicles (UAV), the correct system operation must always be guaranteed. Standard methods for monitoring an embedded application, i.e., detecting erroneous behavior at run-time, require a detailed system understanding during development which increases the design effort significantly.Our approach uses Artificial Neural Networks (ANN), specifically recurrent Long Short-Term Memory (LSTM) architectures, to realize cost efficient monitoring for embedded systems. We propose extensions to existing ANN-based monitoring approaches and investigate suitable ANN architectures, which facilitate fault detection on the open-source UAV flight stack PX4. We demonstrate that our ANN-based approach can detect faults on a raw data stream coming from the monitored system, thus minimizing the need to engineer curated data streams in order to adapt the approach to a different device.
A Method of Equipment Safety Certification Based on Daily Cycle Activity

Smartphones have become an important tool for people's daily lives, which brings higher security requirements in high-risk application areas, for example, mobile payment. Although the combination of physical password, fingerprint and facial recognition have improved the security to a certain extent, there still exists a high risk of being decrepted. This paper attempts an algorithm which is more suitable for studying human partial periodic activity, namely Prophet algorithm. This algorithm has strong robustness for missing data and trend change, and can deal with outliers well. The experimental results on the UniMiB SHAR DATA show that the user simply needs to do 5 cycles of specified actions to realize the prediction of the next time series. The Error analysis of cross validation was applied to 4 different indicators, and the Mean Squared Error of the optimal result “Jumping” behavior was only 8.20%. With these appealing features, The main contribution of this paper is to propose a smart phone user identification system based on behavioral activity cycle, which can be replicated in other behavioral studies. Another outstanding feature of such a system is the capability of fitting models using small data set by exploiting behavioral characteristics derived from periodicity and thus reducing dependence on sensor scanning frequency, therefore the system balances among energy consumption, data quantity and fitting accuracy.
Neural Clinical Event Sequence Prediction Through Personalized Online Adaptive Learning

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.
Optimal quantum radar vs. optimal classical radar with full polarization antennas

We compute the optimal quantum radar detection performance relative to the corresponding optimal classical radar detection performance with full polarization antennas. We use the Belavkin-Zakai equation rather than the Schrödinger equation, because the latter does not have an explicit measurement model. Our calculation is consistent with the well known 6 dB quantum advantage at low photon flux per mode assuming no polarization entanglement of photons and no use of fully polarized antennas.
New Approach to Underwater Image Dehazing using Dark Channel Prior

Abstract Images captured in dense, hazy, foggy atmospheric conditions or in a surrounding filled with impurities, are exposed to deterioration of the captured image which lowers the contrast, color changes, and the object features observed are difficult to recognie by normal human vision and also by some outdoor computer vision systems. This paper analyses the enhancement in the visibility of a single degraded image. The single image is processed to give two or more images of different characteristics and features. The information from these images is used to generate a solitary image with more accurate information of the scene than the original images. To maintain the important features and information of the image for good visibility regions, various parameters are used as filters using dark channel prior technique. The resultant images are then combined and weighed to reduce the unwanted attributes. The resultant image eventually is improved as compared to the resource images.
No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.
On Pillai’s problem with X-coordinates of Pell equations and powers of 2 II

In this paper, we show that if [Formula: see text] is the [Formula: see text]th solution of the Pell equation [Formula: see text] for some non-square [Formula: see text], then given any integer [Formula: see text], the equation [Formula: see text] has at most [Formula: see text] integer solutions [Formula: see text] with [Formula: see text] and [Formula: see text], except for the only pair [Formula: see text]. Moreover, we show that this bound is optimal. Additionally, we propose a conjecture about the number of solutions of Pillai’s problem in linear recurrent sequences.
Intelligent Mining Pool Selection in the Case of Unobservable Block Withholding Attack

In Proof-of-Work (PoW) blockchain systems, miners can select the pool to join for maximizing their revenues, leading to the mining pool selection problem. A pool can infiltrate its mining power into other pools for only obtaining additional revenues without substantially contributing to their mining work. This is called Block WithHolding (BWH) attack and significantly affects the pool selection of miners. We therefore investigate the mining pool selection issue under the BWH attack in this paper. Previous studies rely on an arguable and impractical assumption that miners can observe the attack to calculate their payoffs. This paper however focuses on unobservable BWH attack and applies reinforcement learning (RL) techniques to analyze the intelligent pool selection of miners. We adopt three typical RL models, i.e., Q-Learning (QL), Deep Q Network (DQN) and Advantage Actor-Critic (A2C) to dynamically learn the optimal pool selection policies of an intelligent miner, and use a discrete-event simulator to measure the reward of the miner. Simulation results are also provided to demonstrate the learning performances of the three models.
Automatic Correction of Internal Units in Generative Neural Networks

Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are known as artifacts. While most of the recent work tries to fix artifact generations by perturbing latent code, few investigate internal units of a generator to fix them. In this work, we devise a method that automatically identifies the internal units generating various types of artifact images. We further propose the sequential correction algorithm which adjusts the generation flow by modifying the detected artifact units to improve the quality of generation while preserving the original outline. Our method outperforms the baseline method in terms of FID-score and shows satisfactory results with human evaluation.
Analysis and enhancement of secure three-factor user authentication using Chebyshev Chaotic Map

The most popular solution for a variety of business applications such as e-banking and e-healthcare is the multi-server environment. The user registration of individual servers is not the primary concern in such an environment. Here, the user can get various services from different servers by registering him/her under one server. To get secure services through this environment, the authenticity of users and servers are crucial. In this observation, the smart card based biometric authentication system is well popular and easy to use. This paper delineates that the security flaws can be found in a trusted authentication scheme proposed by Chatterjee et al. (2018). Besides, this work proposes an enhanced authentication scheme namely, asymmetric encryption based secure user authentication (ASESUA) to eliminate the drawbacks of Chatterjee et al.’s scheme. The formal security analysis of ASESUA has been done with the help of random oracle model and verified by the well popular AVISPA tool. The analysis shows that ASESUA performs better concerning security, communication cost, and computation cost than other related existing schemes.
Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Integrated Health Care Delivery system with IoT Enabling Technology

An integrated healthcare system, making use of Internet of things has immense benefits. A crucial factor for an efficient delivery of the health care system is the medical database of the patient on a real time basis. The mortality rate has deep rooted constrains in medical errors due to lack of medical information of the patient and unavailability of real time data. IoT enabled technology can bring about a drastic change in reducing the mortality rate. The value added is much beyond the money it can save people's life, with the outbreak of Covid-19 pandemic the benefits derived from IoT enabled technology has been remarkable. A striking feature of IoT enabled tools in the field of health care sector is thait can cater to a large population simultaneously and remotely. If IoT platform is implemented in healthcare services it can reduce the expenditure of health care services to the national GDP and can reduce the mortality rate. With digital transformation E-health apps are cost effective and less time consuming for individuals to monitor vitals at will. An emerging economy like India can be benefitted immensely from using IoT enabled tools in the field of health care services.
Blockchain Enabled Secure and Efficient Reputation Management for Vehicular Energy Network

Blockchain (BC) based Vehicular Energy Network (VEN) enables secure and distributed trading between the vehicles. Furthermore, reputation management is a critical requirement for building trust in BC based VENs. However, the existing BC based reputation schemes are vulnerable to replay attacks due to insecure reputation verification. Moreover, a BC based VEN also requires a privacy preserving traceability mechanism to prevent false information dissemination and fraudulent transactions. Furthermore, a VEN also necessitates an efficient storage mechanism to reduce the storage overhead incurred by the BC ledger. To address these issues, this paper presents a BC based secure and efficient reputation management scheme for VENs. The proposed scheme provides a secure vehicles' reputation verification mechanism to prevent the replay attacks. Moreover, the proposed scheme uses Elliptic Curve Digital Signature Algorithm based pseudonym mechanism to enable conditional anonymity and vehicles' traceability. Furthermore, the proposed scheme uses InterPlanetary File System to efficiently store the vehicles' reputation information and consequently, reduces the storage overhead. Finally, performance and security analysis is performed to show the effectiveness and practicality of the proposed scheme.
Light Weight Facial Landmark Detection With Weakly Supervised Learning

A robust facial landmark detection framework is proposed in this paper, which can be trained in an end-to-end fashion and has achieved promising detection accuracy in the 3rd Grand Challenge of 106-Point Facial Landmark Localization. Firstly, the upper bound of computational complexity is 100MFLOPs and the model size is 2MB, we design a new model named ShuffleNeXt to be the backbone. Based on ShuffleNetV2, groupwise convolution layer is used to replace the standard depthwise convolution layer. Swish function is also used to replace ReLU function. What is more, we design a single layer coordinate attention module to capture spatial and channel information, which is better than the coordinate attention and squeeze-and-excitation module. In order to prevent the accuracy loss by the coordinates quantization, dual soft argmax is used for mapping the heatmap response to coordinates. Besides, a coarse localization regulation is also proposed to improve the performance. In the end, we introduce weakly supervised learning to increase training samples. We train the model and re-label the large scale CelebA dataset. Original CelebA only has 5 points annotations, so we calculate the NME on these 5 points. We set the threshold of NME to 1% and find 162,731 face images fit the bill. So the number of training set is expanded from 20,384 to 183,115, which is 800% larger than original dataset. The best result 79.38% for AUC is achieved on the validation set.
The (U-Th)/He Chronology and Geochemistry of Ferruginous Nodules and Pisoliths Formed in the Paleochannel Environments at the Garden Well Gold Deposit, Yilgarn Craton of Western Australia: Implications for Landscape Evolution and Geochemical Exploration

Ferruginous nodules and pisoliths that cap deeply weathered profiles and transported cover are characteristic of the Yilgarn Craton, Western Australia. Here we show how ferruginous nodules and pisoliths formed in the paleochannel sediments during Miocene can be used to locate buried Au mineralization. Three types of ferruginous nodules and pisoliths were identified in paleochannel sediments and saprolite, representing different parent materials and environments covering the Garden Well Au deposit: (i) ferruginous nodules formed in saprolite on the flanks of the paleochannel (NSP), (ii) ferruginous pisoliths formed in the Perkolilli Shale in the middle of the paleochannel (PPS) and (iii) ferruginous nodules formed in the Wollubar Sandstone at the bottom of the paleochannel (NWS). The appearance, mineralogy and geochemistry of ferruginous nodules and pisoliths vary according to their origin. The PPS and NWS are goethite-rich whereas NSP is a mixture of goethite and hematite which make them all suitable for (U–Th)/He dating. The average age of goethite in the NSP is 14.8 Ma, in the NWS is 11.2 Ma and in the PPS is 18.6 and 14 Ma. The goethite ages in ferruginous nodules and pisoliths are thought to be younger than the underlying saprolite (Paleocene-Eocene) and were formed in different environmental conditions than the underlying saprolite. Anomalous concentrations of Au, As, Cu, Sb, In, Se, Bi, and S in the cores and cortices of the NWS and the PPS reflect the underlying Au mineralization, and thus these nodules and pisoliths are useful sample media for geochemical exploration in this area. These elements originating in mineralized saprolite have migrated both upwards and laterally into the NWS and the PPS, to form spatially large targets for mineral exploration.
SmoothMV

Multi-view has the potential to offer immersive viewing experiences to users, as an alternative to 360° and Virtual Reality (VR) applications. In multi-view, a limited number of camera views are sent to the client and missing views are synthesised locally. Given the substantial complexity associated to view synthesis, considerable attention has been given to optimise the trade-off between bandwidth gains and computing resources, targeting smooth navigation and viewing quality. A still relatively unexplored field is the optimisation of the way navigation interactivity is achieved, i.e. how the user indicates to the system the selection of new viewpoints. In this article, we introduce SmoothMV, a multi-view system that uses a non-intrusive head tracking approach to enhance navigation and Quality of Experience (QoE) of the viewer. It relies on a novel Hot&Cold matrix concept to translate head positioning data into viewing angle selections. Streaming of selected views is done using MPEG-DASH, where a proposed extension to the standard descriptors enables to achieve consistent and flexible view identification.
E-learning and M-learning Benefits in the Learning Process

This paper elaborates around the perception that students have regarding the learning process based on technological means: e-learning and m-learning combined. The experiment’s sample consisted of 16 students between 9 and 12 years old (37.2% female and 62.5% male). The results showed that most students are in favor of using technology to improve their motivation to learn, which significantly improves their willingness to complete learning activities.
Implementation of data mining with Apriori techniques to determine the pattern of purchasing of agricultural equipment (Case Study: XYZ Store)

Abstract The aim of this research is to take advantage of data mining techniques using the Apriori sales data algorithm for agricultural equipment. This is due to the fact that the high potential agricultural sector in Indonesia has obstacles to the use of technology. Research was conducted in the district of Simalungun in a shop selling agricultural needs. Sales activities in the store continue and generate more and more data. In order to be of use to the resulting data, it is necessary to process these data with a certain algorithm that provides great benefits, in particular by maximizing the sales profits of agricultural products. Apriori Algorithm is one of the methods of data mining, the activities of which include data collection and the use of old data with the aim of finding regularities, patterns or relationships in data collection. The output of the algorithm can help future decision-makers, where one of the advantages is the rearrangement of the product layout, such as the most frequently sold products being assembled so that they are easily visible to consumers and can properly prepare stock items for the store.
A Learning to Tune Framework for LSH

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with a well-defined performance bound. The success of LSH family mainly depends on the design of randomly projected hash functions. However, instead of randomly drawing hash functions from a conventional hashing family such as Gaussian projection for Euclidean space, we argue that whether there could be a set of data sensitive hashing functions with higher capacity to distinguish nearby points and far away points, which could have rigorous performance guarantee like conventional LSH. To this end, we propose a learning to tune framework, called LSH-tuning, which consists of a pruning model and a learning to rank model. The pruning model reduces the total number of hash tables to maximize the separating capacity on the given data distribution and minimize the storage overhead. The learning to rank model ranks hash tables based on their effectiveness on NN retrieval. We also have a theoretic model that guides us to gradually search more hash tables and probe nearby buckets. Extensive experiments with real-world data demonstrate that LSH-tuning is capable of outperforming existing proposals with respect to both efficiency and storage overhead.
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates

Instance segmentation of images is an important tool for automated scene understanding. Neural networks are usually trained to optimize their overall performance in terms of accuracy. Meanwhile, in applications such as automated driving, an overlooked pedestrian seems more harmful than a falsely detected one. In this work, we present a false negative detection method for image sequences based on inconsistencies in time series of tracked instances given the availability of image sequences in online applications. As the number of instances can be greatly increased by this algorithm, we apply a false positive pruning using uncertainty estimates aggregated over instances. To this end, instance-wise metrics are constructed which characterize uncertainty and geometry of a given instance or are predicated on depth estimation. The proposed method serves as a post-processing step applicable to any neural network that can also be trained on single frames only. In our tests, we obtain an improved trade-off between false negative and false positive instances by our fused detection approach in comparison to the use of an ordinary score value provided by the instance segmentation network during inference.
Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement

Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the en-tire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the objective. In this work, we explore new dimensions of unsupervised learning by proposing the Progressive Stage-wise Learning (PSL) framework. For a given unsupervised task, we design multi-level tasks and define different learning stages for the deep network. Early learning stages are forced to focus on low-level tasks while late stages are guided to extract deeper information through harder tasks. We discover that by progressive stage-wise learning, unsupervised feature representation can be effectively enhanced. Our extensive experiments show that PSL consistently improves results for the leading unsupervised learning methods.