Reviewerly/exHarmony huggingface dataset:
Unconditionally Secure Qubit Commitment Scheme Using Quantum Maskers

A commitment scheme allows one to commit to hidden information while keeping its value recoverable when needed. Despite considerable efforts, an unconditionally and perfectly secure bit commitment has been proven impossible both classically and quantum-mechanically. The situation is similar when committing to qubits instead of classical bits as implied in the no-masking theorem [K. Modi et al., Phys. Rev. Lett. {\bf 120}, 230501 (2018)]. In this Letter, we find that circumvention of the no-masking theorem is possible with the aid of classical randomness. Based on this, we construct an unconditionally secure quit-commitment scheme that utilises any kind of universal quantum maskers with optimal randomness consumption, which is distributed by a trusted initializer. This shows that randomness, which is normally considered only to obscure the information, can benefit a quantum secure communication scheme. This result can be generalised to an arbitrary dimensional system.
Learning Chinese Word Embeddings With Words and Subcharacter N-Grams

Co-occurrence information between words is the basis of training word embeddings; besides, Chinese characters are composed of subcharacters, words made up by the same characters or subcharacters usually have similar semantics, but this internal substructure information is usually neglected in popular models. In this paper, we propose a novel method for learning Chinese word embeddings, which takes full use of external co-occurrence context information and internal substructure information. We represent each word as a bag of subcharacter n-grams, and our model learns the vector representation corresponding to the word and its subcharacter n-grams. The final word embeddings are represented as the sum of these two kinds of vector representation, which makes the learned word embeddings can take into account both the internal structure information and external co-occurrence information possible. The experiments show that our method outperforms state-of-the-art performance on benchmarks.
Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions

Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the suitability of different probabilistic state estimation methods, namely, the Extended Kalman Filter (EKF) and the more general Particle Filter (PF) with the addition of the Interacting Multiple Model (IMM) approach. These algorithms are not capable of predicting motion for long term in road traffic conditions, though their robustness and model classification capability are essential for the overall system. The performance is evaluated in road traffic scenarios where the tracked object imitates the motion characteristics of a road vehicle and is observed from a stationary sensor. The measurements are generated according to standard automotive radar models. The analysis conducted along two aspects emphasizes the different performance and scaling properties of the examined state estimation algorithms. The presented evaluation framework serves as a customizable method to test and develop advanced autonomous functions.
Particle Swarm Optimization: Understanding Order-2 Stability Guarantees

This paper’s primary aim is to provide clarity on which guarantees about particle stability can actually be made. The particle swarm optimization algorithm has undergone a considerable amount of theoretical analysis. However, with this abundance of theory has come some terminological inconstancies, and as a result it is easy for a practitioner to be misguided by overloaded terminology. Specifically, the criteria for both order-1 and order-2 stability are well studied, but the exact definition of order-2 stability is not consistent amongst researchers. A consequence of this inconsistency in terminology is that the existing theory may in fact misguide practitioners instead of assisting them. In this paper it is theoretically and empirically demonstrated which practical guarantees can in fact be made about particle stability. Specifically, it is shown that the definition of order-2 stability which accurately reflects PSO behavior is that of convergence in second order moment to a constant, and not to zero.
Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling's, Alicante, Essex, and Stegmann's) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions.
Multi-scale Dense Network for Single-image Super-resolution

Recently, deep neural networks have led to tremendous advances in image super-resolution. As a well-known one-to-many inverse problem, the deep learning based methods tackle this issue via large receptive field. By that, the deep network could infer each output pixel from sufficient context information. However, most existing studies use larger kernel size or design a very deep network model to attain sufficient receptive field. The computational cost dramatically increments along with the training difficulty. Concerning this problem, the goal of this paper is to design an effective and trainable convolutional neural network. We proposed a multi-scale dense network (MSDN) which is composed of deep concatenation and basic blocks, namely multi-scale dense block (MSDB). The proposed MSDB use different dilated convolutions to gather multi-scale information; meanwhile concatenating the different dilated convolution results magnify the receptive field of a single layer. To facilitate the training difficulty, there are the dense skip connections in the proposed MSDB. Moreover, the deep concatenation and global skip connection are also adopted for improving training furthermore. Consequently, we achieve a large receptive field network without deeper structure. The experiments indicate that the quality of the proposed MSDN yields the state-of-the-art result.
A New Binary Adaptive Elitist Differential Evolution Based Automatic<i> k</i>-Medoids Clustering for Probability Density Functions

This paper proposes an evolutionary computing based automatic partitioned clustering of probability density function, the so-called binary adaptive elitist differential evolution for clustering of probability density functions (baeDE-CDFs). Herein, the k -medoids based representative probability density functions (PDFs) are preferred to the k -means one for their capability of avoiding outlier effectively. Moreover, addressing clustering problem in favor of an evolutionary optimization one permits determining number of clusters “on the run”. Notably, the application of adaptive elitist differential evolution (aeDE) algorithm with binary chromosome representation not only decreases the computational burden remarkably, but also increases the quality of solution significantly. Multiple numerical examples are designed and examined to verify the proposed algorithm’s performance, and the numerical results are evaluated using numerous criteria to give a comprehensive conclusion. After some comparisons with other algorithms in the literature, it is worth noticing that the proposed algorithm reveals an outstanding performance in both quality of solution and computational time in a statistically significant way.
Comment: Strengthening Empirical Evaluation of Causal Inference Methods

This is a contribution to the discussion of the paper by Dorie et al. (Statist. Sci. 34 (2019) 43–68), which reports the lessons learned from 2016 Atlantic Causal Inference Conference Competition. My comments strongly support the authors' focus on empirical evaluation, using examples and experience from machine learning research, particularly focusing on the problem of algorithmic complexity. I argue that even broader and deeper empirical evaluation should be undertaken by the researchers who study causal inference. Finally, I highlight a few key conclusions that suggest where future research should focus.
Design of a board game with augmented reality

This paper presents the design process of a board game with augmented reality. The board games have remained attractive in this time of complete digitalization. The experience of hands on play has been the key feature of the board game. By introducing AR in the board games this key feature is kept together with digitalization of some elements of the gameplay. Augmented reality games provide players with a rich and potentially unlimited range of interaction possibilities. Implementation of AR creates added value to the game. We want to enhance the game experience by combining the best features from classical board games and video games. The benefits of playing board games are numerous contrary to the video games. By implementing AR, the game will draw interest of the video games players too. This paper presents an example of the design of what is known as AR Board Game, a class of table-top games that combines handheld devices (such as smart phones) with physical game pieces to create a merged physical/virtual game on the table-top. Along with that, the paper describes the process of designing a board game that incorporates AR, along with technical implementation. The design process is performed using the well-known principles (fig.1). The goal of the research is to identify the benefits for the players of introducing digital technology in the analogue world of the board games. Comparison of the user experience in the case of using just the board game and the board game with AR is conducted. For the sample under consideration were accounted board game players but also video games players. Results of the conducted comparison are presented and discussed. At the end the whole experience of designing such a product is presented along with ideas for further work.
A Novel Motion-Intelligence-Based Control Algorithm for Object Tracking by Controlling PAN-Tilt Automatically

As computer vision develops, pan-tilt platform visual systems are able to track moving target over static camera systems. In this paper, a novel motion-intelligence-based control algorithm for object tracking by controlling pan-tilt platform has been proposed. The algorithm includes the motion control model based on angular speed and the intelligent control algorithm based on reinforcement learning (RL). The motion control model converts deviation between the center point of the tracked target and the center point of the frame to angular speed of pan-tilt platform. It can keep position of the tracked object in the center of the frame automatically. The intelligent control algorithm based on reinforcement learning can reduce the error between the ideal value and the actual value when the pan-tilt platform moves. The two blocks work together to make the pan-tilt platform track a dynamic object more stably and the experiment result shows that both the tracking accuracy and robustness are improved.
Towards Analyzing Semantic Robustness of Deep Neural Networks

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.
Improved method for image security based on chaotic-shuffle and chaotic-diffusion algorithms

In this paper, we propose to enhance the security performance of the color image encryption algorithm which depends on multi-chaotic systems. The current cryptosystem utilized a pixel-chaotic-shuffle system to encode images, in which the time of shuffling is autonomous to the plain-image. Thus, it neglects to the picked plaintext and known-plaintext attacks. Also, the statistical features of the cryptosystem are not up to the standard. Along these lines, the security changes are encircled to make the above attacks infeasible and upgrade the statistical features also. It is accomplished by altering the pixel-chaotic-shuffle component and including another pixel-chaotic-diffusion system to it. The keys for diffusion of pixels are extracted from the same chaotic arrangements created in the past stage. The renovation investigations and studies are performed to exhibit that the refreshed version of cryptosystem has better statistical features and invulnerable to the picked plaintext and known plaintext attacks than the current algorithm.
Playing Game 2048 with Deep Convolutional Neural Networks Trained by Supervised Learning

Game 2048 is a stochastic single-player game and development of strong computer players for Game 2048 has been based on N-tuple networks trained by reinforcement learning. Some computer players were developed with (convolutional) neural networks, but their performance was poor. In this study, we develop computer players for Game 2048 based on deep convolutional neural networks (DCNNs). We increment the number of convolution layers from two to nine, while keeping the number of weights almost the same. We train the DCNNs by applying supervised learning with a large number of play records from existing strong computer players. The best average score achieved is 93, 830 with five convolution layers, and the best maximum score achieved is 401, 912 with seven convolution layers. These results are better than existing neural-network-based players, while our DCNNs have less weights.
The LQG Control Algorithms for Nonlinear Dynamic Systems

In this paper I will discuss some theoretical and practical aspects of the linear–quadratic–Gaussian (LQG) control algorithms implementation. The LQG control problem is one important optimal control problem and it can be used for systems where additive Gaussian noise appears on the system (output and state variables) or where the variable states are not complete measurable. The LQG control is a combination between one state estimator algorithm and one linear–quadratic regulator (LQR) algorithm. If the system to be controlled can be characterized by a linear system model, then the linear Kalman filter can be used. If the system is nonlinear, the Extended Kalman Filter (EKF) can be applied or other substitute methods such as the Unscented Kalman filter (UKF) algorithm.
Investigating the Effect of Orientation and Visual Style on Touchscreen Slider Performance

Sliders are one of the most fundamental components used in touchscreen user interfaces (UIs). When entering data using a slider, errors occur due e.g. to visual perception, resulting in inputs not matching what is intended by the user. However, it is unclear if the errors occur uniformly across the full range of the slider or if there are systematic offsets. We conducted a study to assess the errors occurring when entering values with horizontal and vertical sliders as well as two common visual styles. Our results reveal significant effects of slider orientation and style on the precision of the entered values. Furthermore, we identify systematic offsets that depend on the visual style and the target value. As the errors are partially systematic, they can be compensated to improve users' precision. Our findings provide UI designers with data to optimize user experiences in the wide variety of application areas where slider based touchscreen input is used.
Gaining Ground: Search Engine Optimization and Its Implementation on an Indie Book Press

This paper focuses on the historical and current day impact that search engine optimization (SEO) tools have in the context of increasing website traffic and, inevitably, online sales. It takes a special focus on the book publishing industry from the perspective of a small-scale publishing company (Company A). The paper accomplishes three things: to develop a working knowledge of SEOs and their impact on website sales and traffic, to highlight the differing usages of SEO in competitors to Company A, and to arrive at a well-informed implementation of SEO strategy for Company A. Our findings conclude that, although there are many, 3 factors are large determinants of a successful SEO campaign. These factors are: title tags, metatag descriptions, and keyword usage. As data and research are conveyed, this paper concludes with how Company A has implemented SEO into their website.
Exploring Object Relation in Mean Teacher for Cross-Domain Detection

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.
Segmentation is All You Need

Region proposal mechanisms are essential for existing deep learning approaches to object detection in images. Although they can generally achieve a good detection performance under normal circumstances, their recall in a scene with extreme cases is unacceptably low. This is mainly because bounding box annotations contain much environment noise information, and non-maximum suppression (NMS) is required to select target boxes. Therefore, in this paper, we propose the first anchor-free and NMS-free object detection model called weakly supervised multimodal annotation segmentation (WSMA-Seg), which utilizes segmentation models to achieve an accurate and robust object detection without NMS. In WSMA-Seg, multimodal annotations are proposed to achieve an instance-aware segmentation using weakly supervised bounding boxes; we also develop a run-data-based following algorithm to trace contours of objects. In addition, we propose a multi-scale pooling segmentation (MSP-Seg) as the underlying segmentation model of WSMA-Seg to achieve a more accurate segmentation and to enhance the detection accuracy of WSMA-Seg. Experimental results on multiple datasets show that the proposed WSMA-Seg approach outperforms the state-of-the-art detectors.
Design of a Morphological Generator for an English to Indian Languages in a Declension Rule-Based Machine Translation System

Morphology is a branch of linguistics that deals with the internal structure of words in a natural language. Any word in a natural language is comprised of one or more morphemes. A morpheme is a smallest linguistic unit that forms a word. A morphological analyzer is a tool that analysis a given input word and outputs its internal structure along with its different morphemes. Conversely, a morphological generator creates the possible word(s) given the morphemes. This paper presents a design of a morphological generator for an English to Malayalam and English to Hindi rule-based machine translation system using declension rules. Declensions also termed as inflections are the different variations or inflected forms of a particular word in a language. Morphological generator is an essential part in the machine translation process that creates inflected words from the root word according to the morphological rules of a language. Machine translation is the branch of computational linguistics that automatically translates human language to another. The language to be translated is labeled as source language (SL) and the language into which translation is done is termed as target language (TL). The declension rule-based machine translation is accomplished by using grammar rules according to the word inflections of the target language. The proposed morphological generator module is elucidated with its framework and each of its modules and their working are expatiated in detail. The input and the output to/from the module are also illustrated using examples.
Assessment of the level and structure of motivation of undergraduate students as indicators of the quality of education

В. Г. Антонов, И. А. Румянцева, Т