Reviewerly/exHarmony huggingface dataset:
Scrambling and Descrambling of Document Image for Data Security in Cloud Computing

This paper presents the issue of security for the document image, which may store in the cloud environment. To provide the protect to the document image is gained by using the Arnold Transform scrambling and descrambling method. In today’s scenario, we are witnesses of the rapid development of the technology. The security of data is becoming the most critical factor. The Document image is treats as the digital property of any individual person, organization or any Government sector. The digitized documents are stored from any place and allow the access also from any place on the globe, by using Cloud computing technologies. Hence, there has been an issue of security, which plays the important role. The security issue still exists in cloud computing. This security issue is motivated to address in this paper. We have considered the Arnold Transformation method for scrambling the document image. The image may send using the medium of internet technology or store in the cloud. By using this work, the security applied to the document image and this ensures un-authorized persons should not be able to read or exploit the document image information. To retrieve the original image descrambling process is applied. The proposed method exhibited encouraging results.
Autonomously learning beliefs is facilitated by a neural dynamic network driving an intentional agent

Intentionality is the capacity of mental states to be about the world, both in its “action” (world-to-mind) and its “perception” (mind-to-world) direction of fit. An intentional agent must be able to perceive, act, memorize, and plan. These psychological modes may be driven by desires and be informed by beliefs. We have previously proposed a neural process account of intentionality, in which intentional states are stabilized by interactions within populations of neurons that represent perceptual features and movement parameters. Instabilities in such neural dynamics activated the conditions of satisfaction of intentional states and induced sequences of intentional behavior. Here we explore the idea that the process organization of such intentional neural systems enables autonomous learning. We show how beliefs may be learned from single experiences, may be activated in new situations, and be used to guide behavior. Beliefs may also be dis-activated when their predictions do not match experience, leading to the learning of a new belief. We demonstrate the idea in a simple scenario in which a simulated agent autonomously explores an environment, directs action at objects and learns simple contingencies in this environment to form beliefs. The beliefs can be used to realize fixed desires of the agent.
Semantic 3D gaze mapping for estimating focused objects

Eye-trackers are expected to be used in portable daily-use devices. However, it must register object information and define a unified coordinate system in advance for human--computer interaction and quantitative analysis. Therefore, we propose a semantic 3D gaze mapping to collect gaze information from multiple people on the unified map and detect focused objects automatically. The semantic 3D map can be reconstructed using keyframe-based semantic segmentation and structure-from-motion, and the 3D point-of-gaze can also be computed on the map. We confirmed that the fixation time of the focused object can be calculated through an experiment without prior information.
Video Structure Meaning

For over a century, motion pictures have entertained us, occasionally educated us, and even served a few specialized fields of study. Now, however, with the precipitous drop in prices and increase in image quality, motion pictures are as widespread as paperback books and postcards once were. Yet, theories and practices of analysis for particular genres and analytical stances, definitions, concepts, and tools that span platforms have been wanting. Therefore, we developed a suite of tools to enable close structural analysis of the time-varying signal set of a movie. We take an information-theoretic approach (message is a signal set) generated (coded) under various antecedents (sent over some channel) decoded under some other set of antecedents. Cultural, technical, and personal antecedents might favor certain message-making systems over others. The same holds true at the recipient end—yet, the signal set remains the signal set.
Understanding Dialogue Acts by Bayesian Inference and Reinforcement Learning

evel (Austin 1962). DAs constitute the most fundamental part of communication, and the comprehension of DAs is essential to human-agent interaction. The purpose of this study is to enable an agent to behave properly in response to DAs without their explicit representation on one hand and to estimate the DAs explicitly on the other hand. The former is realized by reinforcement learning and the latter by Bayesian inference. The simulation results demonstrated that the agent not only responded to DAs successfully but also inferred the DAs correctly.
Siamese Deep Dictionary Learning

Researchers have explored the importance of Siamese networks in deep learning. With recent developments in deep learning and the effectiveness of deep dictionary learning, this research proposes the architecture of Siamese Deep Dictionary Learning. We first propose the architecture followed by solving the optimization problem. The experimental effectiveness is demonstrated on five different image databases pertaining to two classification problems: face verification and kinship verification. The experiments show that the proposed Siamese Deep Dictionary Learning yields comparable results compared to state-of-the-art algorithms on all five databases.
Enhanced Style Transfer in Real-Time with Histogram-Matched Instance Normalization

Since the neural networks are utilized to extract information from an image, Gatys et al. found that they could separate the content and style of images and reconstruct them to another image which called Style Transfer. Moreover, there are many feed-forward neural networks have been suggested to speeding up the original method to make Style Transfer become practical application. However, this takes a price: these feed-forward networks are unchangeable because of their fixed parameters which mean we cannot transfer arbitrary styles but only single one in real-time. Some coordinated approaches have been offered to relieve this dilemma. Such as a style-swap layer and an adaptive normalization layer (AdaIN) and soon. Its worth mentioning that we observed that the AdaIN layer only aligns the means and variance of the content feature maps with those of the style feature maps. Our method is aimed at presenting an operational approach that enables arbitrary style transfer in real-time, reserving more statistical information by histogram matching, providing more reliable texture clarity and more humane user control. We achieve performance more cheerful than existing approaches without adding calculation, complexity. And the speed comparable to the fastest Style Transfer method. Our method provides more flexible user control and trustworthy quality and stability.
Retranslating Thucydides as a scientific historian

Abstract The nineteenth century was a period of dramatic change in Europe for the idea of history. While from antiquity through to the eighteenth century, historiography had broadly been considered an artistic and rhetorical activity, this view gradually lost ground in the nineteenth century to an understanding of history as a science. This case study aims to explore how these shifts in attitudes towards the proper aims and methods of history writing might have shaped the interpretation and translation into English of Thucydides’ History of the Peloponnesian War , a work first written in classical Greek in the fifth century BCE. The analysis is carried out by means of a corpus-based methodology which, I argue, can better enable researchers to engage with each (re)translator’s overall presentation of the source through the production and interrogation of concordances listing every instance of a given search item as it occurs within digitised versions of the target texts. This is demonstrated through an investigation of the use of the term ‘fact(s)’ which reveals a striking divergence in interpretation between the six translations, with Crawley’s (1874) History in particular appearing to lend a significantly more objective and empirical tone to Thucydides in English.
Detecting RSU Misbehavior in Vehicular Edge Computing

Mobile Edge Computing can be used to realize the low latency requirements of vehicular networks. However, by compromising the road side units (RSUs), an adversary can introduce an extra delay leading to various problems such as the wastage of edge computing resources and disruption of navigational and safety functions. The compromised RSU can for instance deliberately corrupt the PHY layer payload of the packets to be transmitted to the vehicles or drop the packets received from the vehicles. With such simple strategies, the adversary would increase latency and through that effect, create serious disruptions. Such attacks can affect many critical delay sensitive applications such as collision avoidance. To detect the presence of such an adversary, we propose a trust based detection system in this paper. Each vehicle transmits a feedback packet about every RSU it has interacted with to a central trusted server. Using the feedback obtained from multiple vehicles, at regular intervals, an aggregated trust value for each RSU in the network is obtained and is compared with a threshold to classify the RSU as authentic or malicious. We also present a mechanism to detect the presence of malicious vehicles reporting false feedback in the network. Simulation results demonstrate the effectiveness of the proposed detection mechanism and the impact of the choice of adversary parameters on the detection system.
Notification Timing of Agent with Vection and Character for Semi-Automatic Wheelchair Operation

Automatic driving systems not only for cars, but also for wheelchairs are being developed. Improving the operability and safety of electric wheelchairs is an important issue. For example, if a driving system changes the speed of the vehicle without a driver's operation, it makes the driver uneasy. We developed a system to address this uneasiness. Our system, called the MIZUSAKI system, notifies drivers of a change in the speed gain, which is controlled by the system, before the change. This system uses 4 in-screen effects, which are intended to be seen in the peripheral vision and do not inhibit drivers' attention to driving. In developing this system, we have always considered the notification timing to be a key factor. We tested this system to find the best notification timing and found that if the anticipatory timing is 3 seconds before the speed gain changes or later.
Ecology of the cognitive-educational space of the digital age

The article is devoted to the ecology of cognitive-educational space of the digital age. The author points out that the need to obtain information from the outside world is one of the biological needs of man. The information environment is becoming the main habitat of modern man, spreading to all areas, including the educational one. The author wonders how digital educational technologies influence the formation of critical thinking, mental performance, the ability to be at ease with a lot of information and choose the right one from it? The author comes to the conclusion emphasizing that providing new opportunities for mastering knowledge, information society technologies often have a negative impact on the formation of consciousness and thinking of the younger generation and at the same time the impact of digital technology can lead to a change in the «cognitive portrait» of mankind, stimulating the development of emotional intelligence.
Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability Pc. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.
Development of a hand pose recognition system on an embedded computer using Artificial Intelligence

The recognition of hand gestures is a very interesting research topic due to the growing demand in recent years in robotics, virtual reality, autonomous driving systems, human-machine interfaces and in other new technologies. Despite several approaches for a robust recognition system, gesture recognition based on visual perception has many advantages over devices such as sensors, or electronic gloves. This paper describes the implementation of a visual-based recognition system on a embedded computer for 10 hand poses recognition. Hand detection is achieved using a tracking algorithm and classification by a light convolutional neural network. Results show an accuracy of 94.50%, a low power consumption and a near real-time response. Thereby, the proposed system could be applied in a large range of applications, from robotics to entertainment.
The Development and Evaluation of Web-based Multiplayer Games with Imperfect Information using WebSocket

Web-based games have been popular. These days Internet technology allows players to play web-based games in multiplayer. An example of multiplayer games with imperfect information is Big Two. In this game, each player plays without knowing the opponent's confidential information. Web-based multiplayer games with imperfect information require real-time communication for handling rapid information changes and the condition of the current state at any time. This research proposes a new framework for web-based multiplayer games with imperfect information. We utilized an open-source WebSocket, namely Socket.IO, and implemented this framework in Big Two as a case study. The evaluation results show that the Big Two prototype has good quality, and this game able to run smoothly.
Behaviors of Reservoir Computing Models for Textual Documents Classification

Reservoir Computing is a paradigm of recurrent neural network (RNN) models, attractive because of its ease of training and new neuromorphic optoelectronic implementations. Applied with success to time series prediction and speech recognition, few works have so far studied the behavior of these networks on natural language processing (NLP) tasks. Therefore, we decided to explore the ability of Echo State Network-based Reservoir Computing (ESN) models with additional embedding layers to classify text documents of the Reuters C50 data set based on authorship. We explored various learned representations such as word and character embedding and deep feature extractors. Our experiments demonstrate that ESN models can achieve state-of-the-art results on this task and are competitive with common models such as Support Vector Machines (SVM). Moreover, we show that these models compute documents as data streams and could then be able to handle other tasks such as event detection and text segmentation. The best performance is obtained by an ESN with a large reservoir of 1,500 neurons based on word vectors. We think that these results demonstrate the possibility of processing massive quantities of textual data in the future using Reservoir Computing-based systems.
Cosine-Based Embedding for Completing Schematic Knowledge

Schematic knowledge, as a critical ingredient of knowledge graphs, defines logical axioms based on concepts to support for eliminating heterogeneity, integration, and reasoning over knowledge graphs (KGs). Although some well-known KGs contain large scale schematic knowledge, they are far from complete, especially schematic knowledge stating that two concepts have subclassOf relations (also called subclassOf axioms) and schematic knowledge stating that two concepts are logically disjoint (also called disjointWith axioms). One of the most important characters of these axioms is their logical properties such as transitivity and symmetry. Current KG embedding models focus on encoding factual knowledge (i.e., triples) in a KG and cannot directly be applied to further schematic knowledge (i.e., axioms) completion. The main reason is that they ignore these logical properties. To solve this issue, we propose a novel model named CosE for schematic knowledge. More precisely, CosE projects each concept into two semantic spaces. One is an angle-based semantic space that is utilized to preserve transitivity or symmetry of an axiom. The other is a translation-based semantic space utilized to measure the confidence score of an axiom. Moreover, two score functions tailored for subclassOf and disjointWith are designed to learn the representation of concepts with these two relations sufficiently. We conduct extensive experiments on link prediction on benchmark datasets like YAGO and FMA ontologies. The results indicate that CosE outperforms state-of-the-art methods and successfully preserve the transitivity and symmetry of axioms.
RsyGAN: Generative Adversarial Network for Recommender Systems

Many recommender systems rely on the information of user-item interactions to generate recommendations. In real applications, the interaction matrix is usually very sparse, as a result, the model cannot be optimised stably with different initial parameters and the recommendation performance is unsatisfactory. Many works attempted to solve this problem, however, the parameters in their models may not be trained effectively due to the sparse nature of the dataset which results in a lower quality local optimum. In this paper, we propose a generative network for making user recommendations and a discriminative network to guide the training process. An adversarial training strategy is also applied to train the model. Under the guidance of a discriminative network, the generative network converges to an optimal solution and achieves better recommendation performance on a sparse dataset. We also show that the proposed method significantly improves the precision of the recommendation performance on several datasets.
Deep Q-Learning for Illumination and Rotation Invariant Face Detection

The domain of automatic face detection is a challenging problem that has made great progress in the last two decades. Much of this progress is due to the continuous advancements in deep learning and computer vision. Nonetheless, contemporary state-of-the-art face detection models rely on exhaustive search or are unable to deal with changes in the data distribution. In this work, we propose a novel Deep Reinforcement Learning (DRL) approach for face detection on data with nonuniform conditions. More specifically, we address illumination and rotation invariance in face detection. Firstly, we train a Stacked Convolutional Autoencoder (SCAE) in a greedy layer-wise unsupervised fashion for illumination invariant feature extraction. We then train a deep Q-network on the illumination invariant features produced by the SCAE model, to learn an action-value policy that allows an agent to place a bounding box around a face. The proposed approach achieves state-of-the-art recognition rates on images with varying degrees of illumination and images that contain faces with some degree of rotation.
An Adversarial Perturbation Approach Against CNN-based Soft Biometrics Detection

The use of biometric-based authentication systems spread over daily life consumer electronics. Over the years, researchers' interest shifted from hard (such as fingerprints, voice and keystroke dynamics) to soft biometrics (such as age, ethnicity and gender), mainly by using the latter to improve the authentication systems effectiveness. While newer approaches are constantly being proposed by domain experts, in the last years Deep Learning has raised in many computer vision tasks, also becoming the current state-of-art for several biometric approaches. However, since the automatic processing of data rich in sensitive information could expose users to privacy threats associated to their unfair use (i.e. gender or ethnicity), in the last years researchers started to focus on the development of defensive strategies in the view of a more secure and private AI. The aim of this work is to exploit Adversarial Perturbation, namely approaches able to mislead state-of-the-art CNNs by injecting a suitable small perturbation over the input image, to protect subjects against unwanted soft biometrics-based identification by automatic means. In particular, since ethnicity is one of the most critical soft biometrics, as a case of study we will focus on the generation of adversarial stickers that, once printed, can hide subjects ethnicity in a real-world scenario.
Text Attention and Focal Negative Loss for Scene Text Detection

This paper proposes a novel attention mechanism and a fancy loss function for scene text detectors. Specifically, the attention mechanism can effectively identify the text regions by learning an attention mask automatically. The fine-grained attention mask is directly incorporated into the convolutional feature maps of a neural network to produce graininess-aware feature maps, which essentially obstruct the background inference and especially emphasize the text regions. Therefore, our graininess-aware feature maps concentrate on text regions, in especial those of exceedingly small size. Additionally, to address the extreme text-background class imbalance during training, we also propose a newfangled loss function, named Focal Negative Loss (FNL). The proposed loss function is able to down-weight the loss assigned to easy negative samples. Consequently, the proposed FNL can make training focused on hard negative samples. To evaluate the effectiveness of our text attention module and FNL, we integrate them into the efficient and accurate scene text detector (EAST). The comprehensive experimental results demonstrate that our text attention module and FNL can increase the performance of EAST by F-score of 3.98% on ICDAR2015 dataset and 1.87% on MSRA-TD500 dataset.
Nested Variance Estimating VAE/GAN for Face Generation

We study the task of conditional image generation and provide a general framework for combining autoencoders (AEs) with generative adversarial networks (GANs). Our framework provides a principled way to avoid well-known problems such as mode collapse and training instability. We use two AEs, a big parent-AE and a small child-AE, to play different roles. Our parent-AE is trained to minimize only one single objective: the reconstruction loss, which makes its training process stable and efficient. It is then fixed and used for several purposes, including initializing the generator and providing powerful features for a simple discriminator of GAN. It also plays the role of reducing the initial harder task of image generation to a simpler one: sampling from its latent distribution, which is given to child-AE to accomplish. Child-AE can be trained very efficiently due to its small size, and we only need to modify it if necessary for different applications, which makes our framework very flexible. Our experiments show that our model is capable of generating high quality novel images with controllable attributes.