Reviewerly/exHarmony huggingface dataset:
A Kalman Filtering Approach for Systems Subject to Parametric Modeling Uncertainties

The paper presents a Kalman type filtering problem for linear systems with parametric uncertainties. A stochastic model with state dependent noise both in the state and in the output equations is used to represent the system with uncertain parameters. The solution of the filtering problem is a Kalman type filter whose gain is determined by solving the H2 optimization problem resulting from the coupling between the filter and the stochastic system with multiplicative noise. It is proved that this optimal gain results by solving a trace minimization problem with constraints expressed in terms of a system of matrix inequalities. The theoretical developments are illustrated by a case study aiming to estimate the states of the pitch dynamics of a space launch vehicle.
A Word Spotting Method for Arabic Manuscripts Based on Speeded Up Robust Features Technique

The diversity of manuscripts according to their contents, forms, organizations and presentations provides a data-rich structures.The aim is to disseminate this cultural heritage in the images format to the general public via digital libraries.However, handwriting is an obstacle to text recognition algorithms in images, especially cursive writing of Arabic calligraphy.Most current search engines used by digital libraries are based on metadata and structured data manually transcribed in Ascii format.In this article, we propose an original method of pattern recognition for searching the content of Arabic handwritten documents based on the Word Spotting technique.Our method is both effective and simple, it consists in extracting a set of features from the words we segment in the target images and comparing them with the features of the words in the requested images.The principle of the method is to characterize each word with the Speeded Up Robust Features algorithm whose goal is to find all occurrences of query words in the target image even in the case of low-resolution images.We tested our method on hundreds of pages of Arabic manuscripts from the National Library of Rabat and the Digital Library of Leipzig University.The results obtained are encouraging compared to other methods based on the same Word Spotting technique.
CNN-Based Analysis of Crowd Structure using Automatically Annotated Training Data

A CNN-based framework is presented for extracting and classifying from static images of crowd (acquired from surveillance systems) individuals, small groups and large groups. A novel approach to the network training has been investigated. Instead of manually outlined ground-truth data, we use automatic annotations by alternative baseline algorithms (which consider both motion and appearance). The proposed CNN detectors are initially trained over rather limited amounts of data. Nevertheless, the detectors are subsequently updated (fine-tuned) by using new batches of automatically annotated samples. Those test samples are periodically acquired by the baseline algorithms from the future surveillance data. Fine-tuning is performed when noticeable differences appear between results by the CNN-detectors and the results of baseline algorithms (which may indicate changes in visual conditions, scenarios or updates in the baseline algorithms). We preliminarily demonstrate that satisfactory performances of CNN-based detectors can be achieved, even if the baseline algorithms have limited accuracy. Actually, it was noticed that fine-tuned CNN-detectors can be superior to the baseline algorithms used for automatic annotation of training data (even though the baseline algorithms process both static images and video-sequences). Since only static images are used once the detectors are fully trained, the presented solution can simplify complexity of systems automatically evaluating structure and behavior of crowds.
Exploring visualizations for digital reading augmentation to support grammar learning

Reading foreign language texts is a frequently used strategy for language learning. Visual text augmentation methods further support the learning experience, e.g., by annotating vocabulary or grammar. Common approaches are integrated dictionaries or static grammar highlights. This work investigates how we can further support grammar learning with the dynamic visualization and interaction opportunities offered by digital reading devices. In collaboration with teachers and potential learners, we identify difficulties learners experience with English grammar and gather ideas for suitable interactive text augmentations. Based on this, we design four different concepts that augment adjectives and adverbs in English-language texts using typographic cues and interactive information displays. The concepts are evaluated in a within-subject study (N = 16). Results show that participants preferred concepts that presented case-specific support, did not distract too much from the text, and gave details on demand. We conclude with design recommendations for designing text augmentation for language learning.
Evaluation of cluster validation metrics

This chapter discusses various evaluation metrics applied for unsupervised learning applications commonly known as cluster validation indices (CVIs). The work presented is primarily targeted at utilizing the correlation between internal and external CVIs to assess the performance of internal CVIs specifically for biomedical data analysis. It evaluates 6 clustering algorithms on 14 real biological datasets. Additionally, 3 different external CVIs are considered. The evaluation framework incorporates rigorous statistical techniques supported by experimental results and analysis.
A Hybrid Approach for Enhancing Security in Internet of Things (IoT)

The term Internet of things (IoT) is referred as the network of devices like home appliances, physical devices, automobiles that are fixed with software, sensors, electronics and network connectivity which allows the devices to connect and perform data exchange. The communication that occurs between the devices over internet is through IP address. Internet of Things is currently in its initial stage. In the impending years it is going to influence our day to day life. The more it will be included in our lifestyle, more will be the threat of it being misused. There is an immediate need to strongly protect data during data transfers. This paper proposes a hybrid encryption algorithm HAR(Hybrid AES Rail Fence) for secure data communication among IoT devices. The proposed Encryption algorithm ensures confidentiality, so the attackers cannot interpret the cipher text and shows better results in comparison with conventional encryption algorithms.
OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations

In this paper, we propose a new approach to automatic calibration of surveillance cameras. The proposed method is based on observing rigid objects in the scene and automatically estimating landmarks on these objects. The proposed approach can use arbitrary rigid objects, as was verified by experiments with a synthetic dataset, but vehicles were used during our experiments with real-life data. Landmarks on objects automatically detected by a convolutional neural network together with corresponding 3D positions in the object coordinate system are exploited during the camera calibration process. To determine 3D positions of the landmarks, fine-grained classification of the detected vehicles in the image plane is necessary. The proposed calibration method consists of dual optimization-optimization of objects' positions in the world coordinate system and also optimization of the calibration parameters to minimize the re-projection error of the localized landmarks. The experiments show improvement in calibration accuracy over the existing method solving a similar problem furthermore with fewer restrictions on the input data. The calibration error on a real world dataset decreased from 6.88% to 2.85%.
Human Work Activity Recognition for Working Cells in Industrial Production Contexts

Collaboration between robots and humans requires communicative skills on both sides. The robot has to understand the conscious and unconscious activities of human workers. Many state-of-the-art activity recognition algorithms with high performance rates on existing benchmark datasets are available for this task. This paper re-evaluates appropriate architectures in light of human work activity recognition for working cells in industrial production contexts. The specific constraints of such a domain is elaborated and used as prior knowledge. We utilize state-of-the-art algorithms as spatiotemporal feature encoders and search for appropriate classification and fusion strategies. Furthermore, we combine keypoint-based with appearance-based approaches to a multi-stream recognition system. Due to data protection rules and the high effort of data annotation within industrial domains only small datasets are available that reflect production aspects. Therefore, we use transfer learning approaches to reduce the dependency on data volume and variance in the target domain. The resulting recognition system achieves high performance for both singular person action and human-object interaction.
Entangled State Preparation for Non-Binary Quantum Computing

A common model of quantum computing is the gate model with binary basis states. Here, we consider the gate model of quantum computing with a non-binary radix resulting in more than two basis states to represent a quantum digit, or qudit. Quantum entanglement is an important phenomenon that is a critical component of quantum computation and communications algorithms. The generation and use of entanglement among radix-2 qubits is well-known and used often in quantum computing algorithms. Quantum entanglement exists in higher-radix systems as well although little is written regarding the generation of higher-radix entangled states. We provide background describing the feasibility of multiple-valued logic quantum systems and describe a new systematic method for generating maximally entangled states in quantum systems of dimension greater than two. This method is implemented in a synthesis algorithm that is described. Experimental results are included that demonstrate the transformations needed to create specific forms of maximally entangled quantum states.
Deep Learning Framework with Arbitrary Numerical Precision

Deep neural networks (DNNs) have recently shown outstanding performance in solving problems in many domains. However, it is difficult to run such applications on mobile devices due to limited hardware resources. Quantization is one method to reduce the hardware requirements. By default, 32-bit floating-point numbers are used in DNNs, while quantization uses fewer bits, such as 4-bit fixed points, at the cost of precision. Previous research has explored two problems related to this: (1) differences between software emulation and implementation that affect model accuracy and (2) lowered accuracy during normalization. In this paper, we developed a new DNNs framework, PyParch, that allows easy manipulation of quantization and propose a training method for fitting to a hardware-friendly model. We show that our developed tool can solve the two problems mentioned above. Quantized models described in previous methods need 18 bits in order to recover the original accuracy, whereas our method requires only 14 bits.
A Comparative Study of the Use of Coresets for Clustering Large Datasets

Coresets can be described as a compact subset such that models trained on coresets will also provide a good fit with models trained on full data set. By using coresets, we can scale down a big data to a tiny one in order to reduce the computational cost of a machine learning problem. In recent years, data scientists have investigated various methods to create coresets. The two state-of-the-art algorithms have been proposed in 2018 are ProTraS by Ros & Guillaume and Lightweight Coreset by Bachem et al. In this paper, we briefly introduce these two algorithms and make a comparison between them to find out the benefits and drawbacks of each one.
Fully Homomorphic NIZK and NIWI Proofs

In this work, we define and construct fully homomorphic non-interactive zero knowledge (FH-NIZK) and non-interactive witness-indistinguishable (FH-NIWI) proof systems. We focus on the NP complete language L, where, for a boolean circuit C and a bit b, the pair $$(C,b)\in L$$ if there exists an input $$\mathbf {w}$$ such that $$C(\mathbf {w})=b$$. For this language, we call a non-interactive proof system fully homomorphic if, given instances $$(C_i,b_i)\in L$$ along with their proofs $$\varPi _i$$, for $$i\in \{1,\ldots ,k\}$$, and given any circuit $$D:\{0,1\}^k\rightarrow \{0,1\}$$, one can efficiently compute a proof $$\varPi $$ for $$(C^*,b)\in L$$, where $$C^*(\mathbf {w}^{(1)},\ldots ,\mathbf {w}^{(k)})=D(C_1(\mathbf {w}^{(1)}),\ldots ,C_k(\mathbf {w}^{(k)}))$$ and $$D(b_1,\ldots ,b_k)=b$$. The key security property is unlinkability: the resulting proof $$\varPi $$ is indistinguishable from a fresh proof of the same statement. Our first result, under the Decision Linear Assumption (DLIN), is an FH-NIZK proof system for L in the common random string model. Our more surprising second result (under a new decisional assumption on groups with bilinear maps) is an FH-NIWI proof system that requires no setup.
Spatial interpolation methods for spectral-spatial remote sensing image super-resolution algorithm based on gradient descent approach

Abstract The paper presents an investigation of the influence of spatial interpolation methods on the quality of the image obtained as a result of the spectral-spatial remote sensing image super-resolution reconstruction based on the gradient descent approach. As an example of the super-resolution method, we applied our earlier developed super-resolution image reconstruction algorithm. The algorithm provides the minimization of error of the observation model that connects the input low-resolution images with the target high-resolution image. The iterations of the gradient descent method are performed in the high-resolution spectral and spatial coordinates grid. For this reason, the spatial interpolation operator is added in the observation model. It is evident that spatial interpolation affects both the quality of the reconstructed image and the algorithm convergence rate. The objective of our research was to define the most appropriate spatial interpolation method. The paper presents the results of the spectral-spatial super-resolution image reconstruction using the following spatial interpolation methods: bilinear, bicubic, sinc, and nearest neighbour interpolation. We compare these implementations in terms of such image quality indicators as the root mean square error of the estimated high-resolution image, the algorithm convergence rate, and the presence of textural and colour artefacts as well.
Improved Part-aligned Deep Features Learning for Person Re-Identification

Person Re-IDentification (Re-ID) is to recognize a person who has been seen before by different cameras from possibly scenes. Re-ID poses as one of the most difficult computer vision problems owing to the enormous amount of identities involved in a large-scale image pool, with much similar appearance constrained by low resolution image, in a possibly occluded scene, etc. Global features geared for general object recognition and face recognition are far less adequate to re-identify a same person across cameras. As such, more discriminating features are needed to identify people. In particular, part-based feature extraction methods that extract by learning local fine- grained features of different human body parts from detected persons have been proved effective for person Re-ID. To further improve the part-aligned spatial feature approach, this paper proposes an improved part-aligned feature (IPAF) deep learning framework to better characterize a person's complete information with the following threes highlights: part alignment, finer part segmentation, and better learning network backbone. Our proposed solution has been trained and tested on the two most comprehensive Re-ID datasets with comparable performance of reported state-of-the-art solutions: for the dataset of Market1501 (DukeMTMC-reID), our proposed solution both achieves competitive results with mAP of 85.96% (84.70%) and CMC 1 of 94.30% (89.84%), respectively.
Fuzzy Relational Equations/Inequalities

Fuzzy relational equations (FRE) were firstly proposed by Sanchez [1]. Fuzzy relational equations/inequalities (FRE/FRI) have played an important role in fuzzy set theory and fuzzy logic systems, and many researchers have discussed them based on different fuzzy relational compositions [2–9].
Implementation of convolutional-LSTM network based on CPU, GPU and pynq-zl board

Deep learning is among the most commonly investigated approach in computer vision area. Quite recently, considerable attention has been paid to develop an end-to-end deep learning approach for action recognition. According to the developments of these time and resource consuming deep learning models, there is now a growing interest in implementing an accelerator low-power hardware architecture. The main objective of this paper is to implement an optimized convolutional-Long Short Term Memory (LSTM) architecture based a low-cost pynq-zl design tool for human action recognition applications. Firstly, the pre-trained Convolutional Neural Network (CNN) model is applied to extract relevant features from videos. Secondly, the classification of these sequences is done using the LSTM with optimized parameters. Finally, the model testing step is performed on the ARM of the pynq-zl FPGA platform and compared with the performances obtained by the central processing unit and graphics processing tools. The experimental results, performed in UTD-MHAD dataset, prove the efficiency of our proposed approach.
Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

To meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points. Based on the assumption of flat plane, the improved Lucas-Kanade algorithm is carried out to track feature points, and then the custom LARSAE is used to amend vehicle offsets. Meanwhile, Hu moments are used as the feature descriptor to complete image matching, realizing vehicle motion estimation. These two methods are fused by the discrete kalman filter to update and optimize vehicle position. Experimental results show that the fusion algorithm overcomes the shortcomings of poor positioning accuracy of optical flow and the low processing speed of feature matching, and is able to provide more accurate real-time positioning output, having a certain robustness for circumstances such as illumination change and low pavement texture.
A sketch drawing humanoid robot using image-based visual servoing

Abstract This paper presents our sketch drawing artist humanoid robot research. One of the limitations of the existing artist humanoid robot is the lack of feedback on the error that occurs during the drawing process. The contribution of this research is the development of a humanoid robot artist with drawing error correction capability. Based on our previous work with open-loop control pen-and-ink humanoid robot artist, we have implemented a closed-loop visual servoing approach to address this problem. Our experimental results show that this approach is sufficient to correct drawing errors that occur due to mechanical limitation of a robot.
Trajectory planning and control for drone replacement for multidrone cinematography

This work addresses the problem of trajectory planning and control for a team of drones, so that a vehicle that is following a target as part of a multi-vehicle formation flight can be safely replaced with minimum impact on the formation objectives. An optimal trajectory generation method is presented, which relies on formulating a nonlinear optimization problem with constraints imposed by the physical limitations of the vehicle, the formation, and the surrounding environment, including nonlinear constraints to enforce inter-vehicle collision avoidance and clear visibility of the target. An online planning and control strategy is also implemented through a Nonlinear Model Predictive Control problem, based on the dynamic model of the system, a quadratic objective function, and a set of constraints, that again include collision avoidance and clear visibility. The proposed algorithms are validated in software-in-the-loop simulations that include the autopilot software.
A Tool for Implementing Privacy in Nano

We present a work in progress strategy for implementing privacy in Nano at the consensus level, that can be of independent interest. Nano is a cryptocurrency that uses an Open Representative Voting (ORV) as a consensus mechanism, a variant of Delegated Proof of Stake. Each transaction on the network is voted on by representatives and each vote has a weight equal to the percentage of their total delegated balance. Every account can delegate their stake to any other account (including itself) and change it anytime it wants. The fundamental goal of this paper is to construct a tool for the consensus algorithm to function without knowing the individual balances of each account. The tool is composed of three different schemes. The first is a weighted threshold secret sharing scheme based on Shamir's secret sharing scheme, used to generate a secret amongst a set of distributed parties, which will be a private key of an additive homomorphic ElGamal cryptosystem over elliptic curves. The second is a polynomials commitment scheme used to make the previous scheme verifiable, i.e., without the need for a trusted dealer. Finally, the third scheme is used to decrypt an ElGamal ciphertext without reconstructing the private key, which, because of this, can be used multiple times.
Radar Fingerprint Feature Extraction Based on VMD

Extracting effective radar fingerprint features is crucial for accurate radar fingerprint recognition which is an important part of radar reconnaissance . The received radar signals are nonstationary and nonlinear time series. Traditional feature extraction methods such as bispectral estimation and time-frequency analysis have high complexity and are easy to lose useful information. Based on the advantages of variational mode decomposition in signal decomposition, this paper proposes an optimized radar fingerprint feature extraction method. First, the radar signal is decomposed by the variational mode decomposition method. The signals of different radars are decomposed into different subsignals. The multi-domain features of each modal component are then extracted to characterize the radar fingerprint from multiple angles. Experiments on real radar signals show that the proposed method can effectively extract radar fingerprint features based on partial intrapulse data and realize high accuracy radar fingerprint recognition.