Reviewerly/exHarmony huggingface dataset:
Error probability in polarization sensitive communication systems in terms of moments of the channel’s rotation angle

Abstract Polarization effects of channels such as turbulent free space and optical fibers can deteriorate the quality of polarization-sensitive communication schemes, for example, quantum key distribution. The exact nature of the channel is problematic to determine, and most theoretical results are based on approximations. We introduce a general model with probabilistic polarization angle rotation and analyze the average error it causes in terms of the moments of the probability distribution. For a small rotation angle variance, an inequality between distributions’ errors is considered based on their kurtosis values and potentially higher-order central moments. An upper bound is determined for the error probability, holding for all practically interesting scenarios.
Blockchain-based privacy protection scheme for electric energy metering data

As a new generation of power system, smart grid has efficient and sustainable service capabilities. However, in most application scenarios, people only pay attention to the availability of data and ignore the security of the data. In order to solve the problem of user data information leakage in the smart grid system, we propose a blockchain-based privacy protection scheme for electric energy data. In the data acquisition stage, the threshold ElGamal homomorphic encryption algorithm is used to aggregate the energy measurement data. In the data storage stage, the combination of on chain and off chain storage mode is adopted to generate the certificate information of privacy data and upload it to the blockchain. On the premise of protecting the privacy and security of personal power consumption, the power consumption in the area is collected to realize the safe storage of private data, which provides reference for the energy dispatching of smart grid in the future.
Asymmetric multiple-image cryptosystem based on LUP decomposition in spherical diffraction domain

Abstract In this paper, an asymmetric multiple-image cryptosystem is proposed using LUP decomposition (LUPD) in the spherical diffraction (SpD) domain. To the best of our knowledge, the SpD transform is applied to the optical encryption for the first time. In the proposed cryptosystem, multiple grayscale images are encoded into a sparse and binary amplitude distribution with the cascaded LUPD and SpD transform. The matching private keys are generated by LUPD, which is convenient for administrative permissions by setting different numbers of private keys. Since LUPD and SpD are both non-linear processes, high security can be achieved to resist attacks such as the retrieval attack. In addition, up to 12 plaintext images can be encrypted with high decryption quality using the proposed cryptosystem. On the other side, the sparse and binary ciphertext can be stored or transmitted with 1/8 space compared with general 8-bit-grayscale ciphertext, which is a merit of using LUPD. The feasibility and effectiveness of the proposed asymmetric cryptosystem are verified by numerical simulation results.
All-in-one “HairNet”: A Deep Neural Model for Joint Hair Segmentation and Characterization

The hair appearance is among the most valuable soft biometric traits when performing human recognition at-a-distance. Even in degraded data, the hair's appearance is instinctively used by humans to distinguish between individuals. In this paper we propose a multi-task deep neural model capable of segmenting the hair region, while also inferring the hair color, shape and style, all from in-the-wild images. Our main contributions are two-fold: 1) the design of an all-in-one neural network, based on depthwise separable convolutions to extract the features; and 2) the use convolutional feature masking layer as an attention mechanism that enforces the analysis only within the `hair' regions. In a conceptual perspective, the strength of our model is that the segmentation mask is used by the other tasks to perceive - at feature-map level - only the regions relevant to the attribute characterization task. This paradigm allows the network to analyze features from nonrectangular areas of the input data, which is particularly important, considering the irregularity of hair regions. Our experiments showed that the proposed approach reaches a hair segmentation performance comparable to the state-of-the-art, having as main advantage the fact of performing multiple levels of analysis in a single-shot paradigm.
KPI measurement on the 4G network within the University of Ilorin

Abstract The present work seeks to address the following question which serves a key motivation behind this research: ‘What if there is a guide on the University of Ilorin website or in the weekly published magazine within the school, guiding the student on which MNO is best for internet connectivity based on the location which they are at any point in time?″. This research is key to answering the question. Data was gathered through a walk test using TEMS Investigation 16.3.4 and analyzed using TEMS Discovery Device 10. The test was based on internet access on the 4G Network by users who are subscribed to the various MNOs that are serving within the school premises.
Near-Optimal Finite-Time Feedback Controller Synthesis Using Supervised and Unsupervised Learning

This paper proposes near-optimal controller synthesis for finite-time optimal control problems using ideas borrowed from the machine learning community. Based on a finite number of pre-computed optimal trajectories, a model of the optimal controller is built using both supervised (regression) and unsupervised (classification) learning. Neural networks are trained to approximate the mapping from state to control, resulting in a state-feedback controller. The resulting neural network controller (NNC) is used for online control prediction and generating near-optimal trajectories to steer the system from any initial state to the goal state. Two extensions are introduced compared with previous related works in the literature. First, the use of clustering divides the offline optimal trajectories into different groups and then builds suitable regression models for each group. Second, the use of time labels as an additional input to the regression model highlights the improvement enabled by the use of time-stamped data for finite-time optimal controller synthesis. Multiple simulations, including a 3D quadrotor with a 12-dimensional state-space, are provided to evaluate the performance of the proposed neural network controller. The proposed method generates near-optimal trajectories for nontrivial nonlinear systems and is suitable for real-time implementation.
Social image retrieval based on topic diversity

Abstract Image search re-ranking is one of the most important approaches to enhance the text-based image search results. Extensive efforts have been dedicated to improve the accuracy and diversity of tag-based image retrieval. However, how to make the top-ranked results relevant and diverse is still a challenging problem. In this paper, we propose a novel method to diversify the retrieval results by latent topic analysis. We first employ NMF (Non-negative Matrix Factorization) Lee and Seung (Nature 401(6755):788–791, 1999) to estimate the initial relevance score to the query q . Then, the initial relevance score is fed into an adaptive multi-feature fusion model to learn the final relevance score. Next, the diversification process is conducted. We group all the images by semantic clustering and estimate the topic distribution of each cluster by topic analysis. The clusters are ranked based on the topic distribution vector and the final retrieval image list is obtained by a greedy selection mechanism based on the estimated relevances. Experimental results on the NUS-Wide dataset show the effectiveness of the proposed approach.
Tiger Detection Using Faster R-CNN for Wildlife Conservation

The world population of tigers has been steadily declining over the years. Three of the nine major subspecies of tigers has extinct, and now tigers are declared as an endangered species. The tiger population does not actually need human aid to live, but it is important that they can be monitored and protected from poachers. For this purpose, artificial intelligence methods can be used to remotely monitor tigers in their habitat. This is the aim of this work. In this study, we use Faster R-CNN for tiger detection. Our software is implemented using Tensor Flow in Python and it can be easily integrated to motion sensor cameras, which can be used for remote monitoring of tigers in their habitat. In this way, the captured tiger images can be analyzed by conservation centers. We evaluated the efficiency of our tiger detection approach both quantitatively and qualitatively. We use ATRW tiger detection dataset for quantitative evaluations. In particular, this is the first time faster R-CNN has been applied for tiger detection in ARTW dataset. Results show that the faster R-CNN performs better than other popular deep learning based detectors.
Iterative Min Cut Clustering Based on Graph Cuts

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.
Cinematic-L1 Video Stabilization with a Log-Homography Model

We present a method for stabilizing handheld video that simulates the camera motions cinematographers achieve with equipment like tripods, dollies, and Steadicams. We formulate a constrained convex optimization problem minimizing the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm of the first three derivatives of the stabilized motion. Our approach extends the work of Grundmann et al. [9] by solving with full homographies (rather than affinities) in order to correct perspective, preserving linearity by working in log-homography space. We also construct crop constraints that preserve field-of-view; model the problem as a quadratic (rather than linear) program to allow for an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> term encouraging fidelity to the original trajectory; and add constraints and objectives to reduce distortion. Furthermore, we propose new methods for handling salient objects via both inclusion constraints and centering objectives. Finally, we describe a windowing strategy to approximate the solution in linear time and bounded memory. Our method is computationally efficient, running at 300fps on an iPhone XS, and yields high-quality results, as we demonstrate with a collection of stabilized videos, quantitative and qualitative comparisons to [9] and other methods, and an ablation study.
Symbolic-Numeric Computation of Posterior Mean and Variance for a Class of Discrete-Time Nonlinear Stochastic Systems

This paper proposes a symbolic-numeric Bayesian filtering method for a certain class of discrete-time nonlinear stochastic systems. The prior distribution and the predictive distribution of the output can be non-Gaussian, while the posterior distribution is approximated by a Gaussian distribution. The mean and variance of the posterior distribution are then regarded as functions of the mean and variance at a previous time step, a known input, and an observed output. A set of linear partial differential equations (PDEs) satisfied by these functions is computed by using algorithms for ideals in rings of differential operators offline, and then the set of linear PDEs is numerically solved online to obtain the mean and variance of the current posterior distribution. A numerical example is provided to show the efficiency of the proposed method.
Design of a Neural Controller Using Reinforcement Learning to Control a Rotational Inverted Pendulum

Artificial intelligence (AI) has an increasing influence in the manufacturing and processing industries. An increasingly interesting area of application is the design of controllers for technical systems through reinforcement learning. This allows the design of a controller with reduced effort for human experts, which is normally involved in the design process, for example by eliminating the determination of suitable controller parameters. In this paper a neural controller for a rotational inverted pendulum is developed by using artificial neural networks in combination with reinforcement learning. The rotational inverted pendulum is a classic example of a nonlinear and unstable system and has been little covered in previous publications on this subject. Furthermore, the behaviour of the neural controller is compared with a conventional controller when swinging up and balancing a rotational inverted pendulum.
Polynomial approximation based spectral dual graph convolution for scene parsing and segmentation

Semantic segmentation requires both a large receptive field and accurate spatial information. Although existing methods based on the FCN have greatly improved the accuracy, it still does not show satisfactory results on complex scene parsing and tiny object identification. The convolution operation in FCN suffers from a restricted receptive field, while global modeling is fundamental to dense prediction tasks. In this work, we apply graph convolution into the semantic segmentation task and propose a spectral dual graph convolution module to solve the above problems. Moreover, the semantic segmentation task can be divided into two directions, one of which is to get a large receptive field and consider the global context information; the other is to focus on extracting spatial and contour clues, such as sharply changing curves and tiny objects. From the spectral-domain, it is supposed that low-frequency information is critical to the former task, while high-frequency information is vital to the latter task. Accordingly, high-frequency and low-frequency biased graph convolutions are proposed to process the above information separately. Experiments on Cityscapes, COCO Stuff, PASCAL Context, and PASCAL VOC demonstrate the effectiveness of our methods on semantic segmentation. The proposal achieves comparable performance with advantages in computational and memory overhead.
To Get Lost is to Learn the Way: An Analysis of Multi-Step Social Engineering Attacks on the Web

Web-based social engineering (SE) attacks manipulate users to perform specific actions, such as downloading malware and exposing personal information. Aiming to effectively lure users, some SE attacks, which we call multi-step SE attacks, constitute a sequence of web pages starting from a landing page and require browser interactions at each web page. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks. We propose STRAYSHEEP, a system to automatically crawl a sequence of web pages and detect diverse multi-step SE attacks. We evaluate the effectiveness of STRAYSHEEP's three modules (landing-page-collection, web-crawling, and SE-detection) in terms of the rate of collected landing pages leading to SE attacks, efficiency of web crawling to reach more SE attacks, and accuracy in detecting the attacks. Our experimental results indicate that STRAYSHEEP can lead to 20% more SE attacks than Alexa top sites and search results of trend words, crawl five times more efficiently than a simple crawling module, and detect SE attacks with 95.5% accuracy. We demonstrate that STRAYSHEEP can collect various SE attacks, not limited to a specific attack. We also clarify attackers' techniques for tricking users and browser interactions, redirecting users to attacks.
Representation Learning Through Latent Canonicalizations

We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have focused on learning "disentangled" representations so that as individual factors vary in a new domain, only a portion of the representation need be updated. In this work, we seek the generalization power of disentangled representations, but relax the requirement of explicit latent disentanglement and instead encourage linearity of individual factors of variation by requiring them to be manipulable by learned linear transformations. We dub these transformations latent canonicalizers, as they aim to modify the value of a factor to a pre-determined (but arbitrary) canonical value (e.g., recoloring the image foreground to black). By creating simple simulators with pre-specified factors of variation to roughly approximate datasets such as SVHN and ImageNet, we demonstrate experimentally that our method helps reduce the number of observations needed to generalize to a similar target domain when compared to a number of supervised baselines.
Stemmer and phonotactic rules to improve n-gram tagger-based indonesian phonemicization

A phonemicization or grapheme-to-phoneme conversion (G2P) is a process of converting a word into its pronunciation. It is one of the essential components in speech synthesis, speech recognition, and natural language processing. The deep learning (DL)-based state-of-the-art G2P model generally gives low phoneme error rate (PER) as well as word error rate (WER) for high-resource languages, such as English and European, but not for low-resource languages. Therefore, some conventional machine learning (ML)-based G2P models incorporated with specific linguistic knowledge are preferable for low-resource languages. However, these models are poor for several low-resource languages because of various issues. For instance, an Indonesian G2P model works well for roots but gives a high PER for derivatives. Most errors come from the ambiguities of some roots and derivative words containing four prefixes: 〈ber〉, 〈meng〉, 〈peng〉, and 〈ter〉. In this research, an Indonesian G2P model based on n-gram combined with stemmer and phonotactic rules (NGTSP) is proposed to solve those problems. An investigation based on 5-fold cross-validation, using 50 k Indonesian words, informs that the proposed NGTSP gives a much lower PER of 0.78% than the state-of-the-art Transformer-based G2P model (1.14%). Besides, it also provides a much faster processing time.
A Fuzzy-Based Multi-agent Framework for E-commerce Recommender Systems

In this paper, we focus on the development of e-commerce recommender system using fuzzy-based multi-agent framework. This framework provides a cooperative partnership between the suppliers and the customers through a consultation mechanism. For this, we use fuzzy inference to make it easier for the customers to communicate their orientation regarding goods or services in flavorful essences, like low, medium, high, or other preferences. The fuzzy logic is built by the sampling of preexisting history records, which is translated into principles by a professional. We represent the process of negotiation by creating three agents, namely purchasing, sales, and mediator, respectively. This process is carried out using Jade framework. Finally, we use a test case to show the working of the proposed framework.
A Location-Based Solution for Social Network Service and Android Marketing Using Augmented Reality

This study aims to make it possible for one to share different feelings at the same location through a location-based SNS which is dissimilar to the existing SNS systems. A common platform such as a neighborhood store explorer guides the user by marking the nearby stores based on his/her location but the proposed application can provide some additional information including the reputation or relevant facts through augmented reality (AR) when he/she photograph a specific mark or image with a mobile device for recognition. A location-based Android marketing solution utilizing Vumark AR is proposed in this study where a similar operation method used for the existing RFID and QR code-based systems is adopted to the Vumark-based approach. Vuforia API is used as the main library to store the target marks or images into the database for recognition and present necessary information on a mobile device screen when required.
Dynamic Regret Bound for Moving Target Tracking Based on Online Time-of-Arrival Measurements

The use of online algorithms to track a moving target is gaining attention in the control community for its simpler and faster computation. In this work, we study the dynamic regret of online gradient descent (OGD) for tackling a time-of-arrival (TOA)-based least-squares formulation of the tracking problem. Since the formulation is non-convex, most existing dynamic regret analyses cannot be applied to it directly. To circumvent this difficulty, we proceed in two steps. First, we show that under standard assumptions on the TOA measurement noise, the loss function at each time step will, with high probability, be locally strongly convex at that time step. Moreover, we give an explicit estimate of the size of the strong convexity region. To the best of our knowledge, this result is new and can be of independent interest. Second, we show that under the aforementioned assumptions on the TOA measurement noise and mild assumptions on the target trajectory, the location estimate of the target at each time step will lie in the strong convexity region of the loss function at the next time step with high probability. This allows us to exploit existing analysis for online strongly convex optimization to give the first dynamic regret bound of OGD for the TOA-based target tracking problem. Simulation results are presented to illustrate our theoretical findings.
Model-Based Reinforcement Learning from PILCO to PETS

Model-based reinforcement learning algorithms promise to alleviate the problem of sample inefficiency of their model-free counterparts, allowing for a wider application of reinforcement learning. A popular algorithm called PILCO delivers on this promise by combining Gaussian process regression with policy search. However, PILCO comes at high computational costs and faces limitations in high-dimensional state-action spaces. A—at the time of writing—recent algorithm called PETS relies on neural networks and model predictive control to overcome these limitations, allowing to make use of model-based reinforcement learning in even more complex domains. In this paper, we identify key concepts behind successful model-based reinforcement learning by establishing connections between those—at first glance—very different algorithms. For this, we introduce a common definition of the problem which model-based reinforcement learning algorithms try to solve and then investigate follow up work on PILCO.