Reviewerly/exHarmony huggingface dataset:
What is Natural?

This poster describes a fundamental user research to uncover the challenges and opportunities for a conversational recommender system. The study was conducted in three different environments: home, work and street. During the sessions, the participants (i) recounted their previous experiences of interacting with conversational agents in a critical incident interview, (ii) performed a task to explore restaurant recommendations with Google Assistant, and (iii) discussed their views on conversational recommendations. We organize the challenges uncovered into three levels: environment, conversation, and system. Further, we illustrate the identified opportunities of whispering interaction, collaborative recommendations, driving scenario, and local-like conversations for delivering recommendations.
On the Cryptanalysis of a Bit-Level Image Chaotic Encryption Algorithm

In this paper, the security analysis of a bit-level image chaotic encryption algorithm based on the 1D chaotic map is proposed. The original image chaotic encryption algorithm includes bit-level permutation encryption, diffusion encryption, and linear transform. Deciphering of it can be divided into two stages. First, bit-level permutation encryption, diffusion encryption, and linear transform can be simplified into bit-level equivalent permutation encryption and equivalent diffusion encryption, which is a key breakthrough point of cryptanalysis. Second, the chaotic sequence generated by this algorithm is independent of the plaintext image. Therefore, the equivalent diffusion key and the equivalent permutation key can be obtained by chosen-plaintext attack, respectively. Theoretical analysis and numerical simulation experiment results verify the effectiveness of the analytical method. Finally, some suggestions are proposed to promote the security of the original image chaotic encryption algorithm.
Optical multi-image encryption based on focal length multiplexing and multimode phase retrieval

A novel optical multi-image encryption method based on focal length multiplexing and multimode phase retrieval is proposed in this study. During the encryption process of the proposed method, multiple secret images were encrypted into a single intensity-only image using focal length multiplexing and a coherent diffractive imaging-based encryption system. A specially designed iterative algorithm based on multimode phase retrieval is proposed for the accurate decryption of the original multiple secret images from the intensity pattern. The advantages of the proposed method include a compact optical setup and high decryption quality. The feasibility, security, and robustness of the proposed method were investigated by numerical simulations.
DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.
A new initialization method based on normed statistical spaces in deep networks

Training deep neural networks can be difficult. For classical neural networks, the initialization method by Xavier and Yoshua which is later generalized by He, Zhang, Ren and Sun can facilitate stable training. However, with the recent development of new layer types, we find that the above mentioned initialization methods may fail to lead to successful training. Based on these two methods, we will propose a new initialization by studying the parameter space of a network. Our principal is to put constrains on the growth of parameters in different layers in a consistent way. In order to do so, we introduce a norm to the parameter space and use this norm to measure the growth of parameters. Our new method is suitable for a wide range of layer types, especially for layers with parameter-sharing weight matrices.
Proof Compression and NP Versus PSPACE II

We upgrade [3] to a complete proof of the conjecture NP = PSPACE that is known as one of the fundamental open problems in the mathematical theory of computational complexity; this proof is based on [2]. Since minimal propositional logic is known to be PSPACE complete, while PSPACE to include NP, it suﬃces to show that every valid purely implicational formula ρ has a proof whose weight (= total number of symbols) and time complexity of the provability involved are both polynomial in the weight of ρ. As is [3], we use proof theoretic approach. Recall that in [3] we considered any valid ρ in question that had (by the deﬁnition of validity) a “short” tree-like proof π in the Hudelmaier-style cutfree sequent calculus for minimal logic. The “shortness” means that the height of π and the total weight of diﬀerent formulas occurring in it are both polynomial in the weight of ρ. However, the size (= total number of nodes), and hence also the weight, of π could be exponential in that of ρ. To overcome this trouble we embedded π into Prawitz’s proof system of natural deductions containing single formulas, instead of sequents. As in π, the height and the total weight of diﬀerent formulas of the resulting tree-like natural deduction ∂1 were polynomial, although the size of ∂1 still could be exponential, in the weight of ρ. In our next, crucial move, ∂1 was deterministically compressed into a “small”, although multipremise, dag-like deduction ∂ whose horizontal levels contained only mutually diﬀerent formulas, which made the whole weight polynomial in that of ρ. However, ∂ required a more complicated veriﬁcation of the underlying provability of ρ. In this paper we present a nondeterministic compression of ∂ into a desired standard dag-like deduction ∂0 that deterministically proves ρ in time and space polynomial in the weight of ρ.2 Together with [3] this completes the proof of NP = PSPACE.Natural deductions are essential for our proof. Tree-to-dag horizontal compression of π merging equal sequents, instead of formulas, is (possible but) not suﬃcient, since the total number of diﬀerent sequents in π might be exponential in the weight of ρ – even assuming that all formulas occurring in sequents are subformulas of ρ. On the other hand, we need Hudelmaier’s cutfree sequent calculus in order to control both the height and total weight of diﬀerent formulas of the initial tree-like proof π, since standard Prawitz’s normalization although providing natural deductions with the subformula property does not preserve polynomial heights. It is not clear yet if we can omit references to π even in the proof of the weaker result NP = coNP.
Watermarking Neural Network with Compensation Mechanism

In recent years, the rapid development of neural networks has also brought his intellectual property (IP) protection. Embedding a watermark in a neural network is an effective scheme to protect its IP. In this paper, we propose a new watermark embedding scheme with compensation mechanism that is different from the previous regularization embedding. First, we select the weights of the watermark to be embedded pseudo-randomly. Then, we perform an orthogonal transformation on the selected weights, and embed the watermark by the binarization method in the obtained coefficients, and use the inverse orthogonal transformation on the watermarked coefficients to obtain the watermarked weights. Finally, we propose a model fine-tuning scheme with compensation mechanism, which can eliminate the slight accuracy degradation caused by binarization without destroying the watermark in the model. In our scheme, due to the concealment of watermark embedding location, it can overcome the defects of previous schemes which cannot resist watermark overwriting attack. Moreover, compared with the regularization embedding method, our scheme uses the fine-tuning with compensation mechanism, which requires less embedding cost and is more stable. In addition, it has achieved favorable performance in resisting weight pruning attack, weight fine-tuning and fidelity evaluation.
Game Action Modeling for Fine Grained Analyses of Player Behavior in Multi-player Card Games (Rummy as Case Study)

We present a deep learning framework for game action modeling, which enables fine-grained analyses of player behavior. We develop CNN-based supervised models that effectively learn the critical game play decisions from skilled players, and use these models to assess player characteristics in the system, such as their retention, engagement, deposit buckets, etc. We show that with a carefully constructed input format, that efficiently represents the game state and history as a multi-dimensional image, along with a custom architecture the model learns the strategies of the game accurately. It is further enhanced with look-ahead achieved by self-play simulation to better estimate the game state, and this information is used in a new loss function. Next, we show that analyzing the players with these models as reference has immense benefit in understanding player potential in terms of engagement and revenue. We also use the model to understand the various contexts under which players tend to make mistakes, and use these insights to up-skill players.
The predicted load balancing algorithm based on the dynamic exponential smoothing

Abstract To a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.
Generation of hexapartite entanglement in a four-wave-mixing process with a spatially structured pump: Theoretical study

We have experimentally shown in a recent paper [Wang et al., Phys. Rev. A 95, 051802(R) (2017)] that a four-wave mixing (FWM) process in a Rb vapor with a spatially structured pump (SSP), consisting of a coherent combination of two tilted pump beams, can generate six well-separated bright beams that are quantum correlated. The experimental setup is compact, simple, phase insensitive, and easily scalable to a larger number of quantum correlated beams. For applications to quantum information processing, multipartite entanglement is needed in addition to quantum correlations. Very recently, we have also experimentally demonstrated the generation of the hexapartite entanglement from this SSP-based FWM process [Zhang et al., Phys. Rev. Lett. 124, 090501 (2020)]. The purpose of this theoretical paper is to extensively investigate the hexapartite entanglement properties by using different entanglement witnesses such as the van Loock-Furusawa criterion and the positivity under partial transposition criterion. We find that hexapartite entanglement is indeed present among the six beams. In addition, we also study the supermodes of this system using basis change.
Towards Building a Facial Identification System Using Quantum Machine Learning Techniques

In the modern world, facial identification is an extremely important task in which many applications rely on high performing algorithms to detect faces efficiently. Whilst classical methods of SVM and k-NN commonly used may perform to a good standard, they are often highly complex and take substantial computing power to run effectively. With the rise of quantum computing boasting large speedups without sacrificing large amounts of much needed performance, we aim to explore the benefits that quantum machine learning techniques can bring when specifically targeted towards facial identification applications. In the following work, we explore a quantum scheme which uses fidelity estimations of feature vectors in order to determine the classification result. Here, we are able to achieve exponential speedups by utilizing the principles of quantum computing without sacrificing large proportions of performance in terms of classification accuracy. We also propose limitations of the work and where some future efforts should be placed in order to produce robust quantum algorithms that can perform to the same standard as classical methods whilst utilizing the speedup performance gains.
Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning

Wall defect detection is an important function for autonomous decoration robots. Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. Nonetheless, building large datasets manually is impractical, which is time-consuming and labor-intensive. In this work, we solve this issue to propose the low-shot wall defect detection algorithm using deep reinforcement learning (DRL) for autonomous decoration robots. Our algorithm first utilizes the attention proposal network (APN) to generate attention regions and applies AlexNet to extract the features of attention patches to further reduce computation. Finally, we train our method with deep reinforcement learning to learn the optimal detection policy. The experiments are implemented on a low-shot dataset in which images are collected from real decoration environments, and the experimental results show the proposed method can achieve fast convergence and learn the optimal detection policy for wall defect images.
Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images

Diabetic retinopathy, aged macular degeneration, glaucoma etc. are widely prevalent ocular pathologies which are irreversible at advanced stages. Machine learning based automated detection of these pathologies facilitate t... | Find, read and cite all the research you need on Tech Science Press
Architecture of blockchain

A blockchain technology allows untrusting parties with common interests to cocreate a permanent, unchangeable, and transparent record of exchange and processing without relying on a central authority. It serves as an immutable ledger that allows transactions to take place in a decentralized manner and secured by cryptographic primitives. Its exceptional characteristics include irreversibility, decentralization, persistence, and anonymity. With these advantages, it has found applications in almost all fields requiring data sharing among multiple parties but with secure authentication, anonymity, and permanence. Some of the applications are finance, real estate, smart grid, transportation system, and IoT. In this chapter, we have described the working and structure of blockchain in the real-world. We described the mining process during validation and verification on the blockchain network.
Integration of Blockchain and Remote Database Access Protocol-Based Database

Many companies are relying on software to manage their businesses. Usually, the software, especially those used by smaller companies, is not secure against unauthorized or unethical data manipulation on the database level. This paper recommends and demonstrates the use of blockchain for securing small enterprises against hacking by alerting the management whenever a change is made to the data without using the authorized channels. This is done through blockchain technology’s inherent hash replication and mining algorithm. The paper shows an application where this idea has successfully been implemented for a desktop application built upon a remote database access (RDA) protocol-based relational database management system (RDBMS) such as remote MySQL and remote Oracle database.
On the Problems of Professional Training of Social Sphere Specialists to Work with Vulnerable Categories of the Population

The article presents the results of the work of the “Education for Children” Section of the Council under the Government of the Russian Federation on the Issues of Guardianship in the Social Sphere, which was carried out in the main areas of analysis, development and solution of the problems of professional training for working with vulnerable categories of the population: experience in applying professional standards for specialties in the social sphere, which are of a cross-sectoral nature; issues of professional training of specialists in accordance with the requirements of interdepartmental professional standards; problems of professional training of specialists in the education system to work with vulnerable categories of children; forms of work with the difficulties of children with special educational needs, their parents and teachers, due to the distance learning regime; requirements for the training of specialists in the social sphere for work in new social conditions. Based on the results of the analysis, a model for the formation and development of interprofessional competencies of social workers, based on the development of the best social practices, is discussed as a strategic direction for improving the training system. The presence of interprofessional competencies will allow specialists to act in concert and work in interdisciplinary teams, apply the best social technologies that have proven their effectiveness when working with vulnerable categories of the population. Within the framework of this direction, priority tasks have been identified to improve the system of professional training of such specialists.
Investigating the Influential Factors for Practical Application of Multi-Class Vehicle Detection for Images from Unmanned Aerial Vehicle using Deep Learning Models

Traffic density, which is a critical measure in traffic operations, should be collected precisely at various locations and times to reflect site-specific spatiotemporal characteristics. For detailed analysis, heavy vehicles have to be separated from ordinary vehicles, since heavy vehicles have a significant effect on traffic flow as well as traffic safety. With unmanned aerial vehicles (UAVs), it is easy to acquire video for vehicle detection by collecting images from above the traffic without any disturbances. Despite previous studies on vehicle detection, there is still a lack of research on real-world applications in estimating traffic density. This study investigates the effects of several influential factors: the size of objects, the number of samples, and a combination of datasets, on detecting multi-class vehicles using deep learning models in various UAV images. Three detection models are compared: faster region-based convolutional neural networks (faster R-CNN), region-based fully convolutional network (R-FCN), and single-shot detector (SSD), to suggest guidelines for model selection. The results provided several findings: (i) vehicle detection from UAV images showed sufficient performance with a small number of samples and small objects; (ii) deep learning-based multi-class vehicle detectors can have advantages compared with single-class detectors; (iii) among all the models, SSD showed the best performance because of its algorithmic structure; (iv) simply combining datasets in different environments cannot guarantee performance improvement. Based on these findings, practical guidelines are offered for estimating multi-class traffic density using UAV.
Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games

In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB) as input to be classified into one of five defined classes, normal image, or one of four different kinds of glitches (stretched, low resolution, missing and placeholder textures). Using a supervised approach, we train a ShuffleNetV2 using generated data. This work focuses on detecting texture graphical anomalies achieving arguably good performance with an accuracy of 86.8%, detecting 88% of the glitches with a false positive rate of 8.7%, and with the models being able to generalize and detect glitches even in unseen objects. We apply a confidence measure as well to tackle the issue with false positives as well as an effective way of aggregating images to achieve better detection in production. The main use of this work is the partial automatization of graphical testing in the final stages of video game development.