Reviewerly/exHarmony huggingface dataset:
Generation of Handwritten Numbers Using Generative Adversarial Networks

Abstract As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.
Robot Communication: Network Traffic Classification Based on Deep Neural Network

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%
Examining The Animal-Human Bond Through The Lens Of Telepresence

While the field of Animal-Computer Interaction is an emerging one, the main focus has still been on anthropocentric approaches to dog technology, assuming that technology that works for humans would also work for dogs. ACI research often examines ways that giving animals technology can improve some aspect of a human's life without considering the complexities of how dogs navigate the world. Using a dog's connection to its owner as a starting point, I plan to design telepresence-based technology that exposes the potentially problematic sides of pet tech, while helping owners to appreciate the complexities of their dogs.
An Approximation Method for Large Graph Similarity

Similarity calculation of large-scale graphs is essential in big data classification, sorting, and other work. However, when there are diverse attributes and the vertices are not ordered, the time and space complexity of similarity computation is often too high. This paper presents a unified representation comprehensive tensor (CT) of large-scale graphs with different specifications and attributes to save space. Besides, before approximation, the concept of a completely satisfied comprehensive tensor (CSCT) set is utilized to ensure the attribute consistency. Then, a spatial mapping (SM) method is proposed to approximate the similarity between two large-scale graphs. In this way, the computational memory is reduced to O(e+n), where e represents the edge number, and n represents the number of vertices. Moreover, the computational efficiency is improved a lot to O(e1+e2), in which e1 and e2 respectively represent the edge numbers of the two graphs for similarity calculation.
A Metric Space Perspective on Self-Supervised Policy Adaptation

One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what was tasked to do in its training environment.While an agent could learn from reward signals to ignore them, the complexity of the real-world can make rewards hard to acquire, or, at best, extremely sparse.A recent class of self-supervised methods have shown promise that reward-free adaptation under challenging distractions is possible.However, previous work focused on a short one-episode adaptation setting.In this letter, we consider a longterm adaptation setup that is more akin to the specifics of the realworld and propose a metric space perspective on self-supervised adaptation.We empirically describe the processes that take place in the embedding space during this adaptation process, reveal some of its undesirable effects on performance and show how they can be eliminated.Moreover, we theoretically study how actor-based and actor-free agents can further generalise to the target environment by manipulating the Lipschitz constant of the actor and critic functions.
Oblivious stable sorting protocol and oblivious binary search protocol for secure multi-party computation

Multi-party computation (MPC) sorting and searching protocols are frequently used in different databases with varied applications, as in cooperative intrusion detection systems, private computation of set intersection and oblivious RAM. Ivan Damgard et al. have proposed two techniques i.e., bit-decomposition protocol and bit-wise less than protocol for MPC. These two protocols are used as building blocks and have proposed two oblivious MPC protocols. The proposed protocols are based on data-dependent algorithms such as insertion sort and binary search. The proposed multi-party sorting protocol takes the shares of the elements as input and outputs the shares of the elements in sorted order. The proposed protocol exhibits O ( 1 ) constant round complexity and O ( n log n ) communication complexity. The proposed multi-party binary search protocol takes two inputs. One is the shares of the elements in sorted order and the other one is the shares of the element to be searched. If the position of the search element exists, the protocol returns the corresponding shares, otherwise it returns shares of zero. The proposed multi-party binary search protocol exhibits O ( 1 ) round complexity and O ( n log n ) communication complexity. The proposed multi-party sorting protocol works better than the existing quicksort protocol when the input is in almost sorted order. The proposed multi-party searching protocol gives almost the same results, when compared to the general binary search algorithm.
Performance analysis of efficient video transmission using EvalSVC, EvalVid-NT, EvalVid

Video transmission in bandwidth constrained networks such as adhoc networks is a challenging task. In bandwidth constrained networks, video losses its original quality. To improve video quality during transmission over adhoc networks, different frameworks such as EvalSVC, EvalVid, EvalVid-NT are developed. These frameworks are integrated with Network Simulator. The video is transmitted over these frameworks and the video quality is evaluated by the PSNR Value.
Safe Navigation of Humanoid Robot in Cluttered Terrain Using Ant Lion Optimizer Tuned RA Approach

Automation in the industry using robots is attracting many researchers because it increases the efficiency and quality of outputs. This article proposes the ant lion optimizer (ALO) tuned regression analysis (RA) approach to guide the humanoid robot in cluttered terrain. This approach optimizes the travel length and the computational time by taking an optimum turning angle to avoid the collision with the obstacle in the robot’s path. The sensory statistics, i.e., the position of start, goal and obstacles, are fed into ALO as input, which provides an initial turning angle. The output of ALO is inducted to RA along with the sensory data, which provides an optimum turning angle. The optimum turning angle is used as a controlled parameter for the humanoid robot to guide the robot to the target. The simulation in a 3D simulator is carried out, which is further validated using real-time experiments on humanoid robot NAO. The relation between the simulation and experiment shows acceptable behavior with deviation under 5%. Further, the proposed technique is validated using the comparison with the existing technique.
Multi-Stream CNN-LSTM Network with Partition Strategy for Human Action Recognition

The wide application of human action recognition in the field of computer vision makes it a hot research topic in the past decades. In recent years, the prevalence of deep sensors and the proposal of real-time skeleton estimation algorithm based on deep images make human action recognition based on skeleton sequence attract increasing attention of researchers. Most of the existing work is aimed at extracting the spatial information of different joint nodes in a frame, but they do not fully consider the combination of temporal and spatial features. At the same time, the different joints were regarded as equally significant in most previous work, which is obviously not in line with the physiological characteristics and kinematics of human body. Therefore, in this paper, a human joint partition strategy is proposed to divide 25 human joints. In addition, a cnn-lstm framework is designed, which can simultaneously model the spatio-temporal characteristics of human skeleton sequence data, and extract the spatial domain information of different joints in a frame and the temporal domain information embedded in consecutive frames.
Measuring inter- and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage

Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predictive of the next fixation in time. However, this assumption may not be sufficient in general, as recent research has shown that scan patterns may also exhibit more long-range temporal correlations. Thus, we here evaluate the active information storage (AIS) as a novel information-theoretic approach to quantifying scan path predictability in a dynamic task. In contrast to the GTE, the AIS provides means to statistically test and account for temporal correlations in scan path data beyond the previous last fixation. We compare AIS to GTE in a driving simulator experiment, in which participants drove in a highway scenario, where trials were defined based on an experimental manipulation that encouraged the driver to start an overtaking maneuver. Two levels of difficulty were realized by varying the time left to complete the task. We found that individual observers indeed showed temporal correlations beyond a single past fixation and that the length of the correlation varied between observers. No effect of task difficulty was observed on scan path predictability for either AIS or GTE, but we found a significant increase in predictability during overtaking. Importantly, for participants for which the first-order Markov chain assumption did not hold, this was only shown using AIS but not GTE. We conclude that accounting for longer time horizons in scan paths in a personalized fashion is beneficial for interpreting gaze pattern in dynamic tasks.
Search Processes in the Exploration of Complex Data under Different Display Conditions

The study investigated user experience, display complexity, display type (tables versus graphs), and task difficulty as variables affecting the user's ability to navigate through complex visual data.A total of 64 participants, 39 undergraduate students (novice users) and 25 graduate students (intermediate-level users) participated in the study.The experimental design was 2 × 2 × 2 × 3 mixed design using two between-subject variables (display complexity, user experience) and two within-subject variables (display format, question difficulty).The results indicated that response time was superior for graphs (relative to tables), especially when the questions were difficult.The intermediate users seemed to adopt more extensive search strategies than novices, as revealed by an analysis of the number of changes they made to the display prior to answering questions.It was concluded that designers of data displays should consider the (a) type of display, (b) difficulty of the task, and (c) expertise level of the user to obtain optimal levels of performance.
Spatial analysis of flood-prone areas and flood discharge

Abstract The Wanggu watershed is a natural ecosystem that is very sensitive to changes in land use. Increased changes in land use and geomorphological conditions in the Wanggu watershed such as slope levels, altitude, soil texture, natural drainage density, rainfall factors and the influence of infrastructure development (road construction, housing) will affect the hydrological response of the watershed and become the cause of increased flood vulnerability in the region. The purpose of this study is to analyze the factors causing flood vulnerability and its impact on the city of Kendari and the Wanggu watershed and to determine the level and distribution of flood vulnerability in the Wanggu watershed area by utilizing geographic information system technology (GIS). This study uses Scoring, weighting, and Overlay methods on several parameters causing floodings such as rainfall, land use, slope, soil texture and height, drainage density, and infrastructure. The results showed that the causes of flooding in Wanggu watershed are rainfall, land-use change does not match the ability class because it is dominated by 40.1% flat slope, medium to rough soil texture 82.3%, altitude 29.0%, moderate drainage density 89.3% and run-off discharge 82.53 m 3 s -1 . The Wanggu watershed has four flood vulnerability classes, namely safe 27.7%, rather vulnerable 16.0%, vulnerable class 32.6%, and very vulnerable class 23.6%. The flood hazard class that dominates the Wanggu River Basin area is the Prone Class.
Mathematical analysis and experimental testing of floating building platform prototypes made from expanded polystyrene system (Styrofoam) and lightweight concrete

Abstract Floating building is one of the solutions to anticipate residential areas against Tidal floods which have been occurring on the North Coast of Central Java. This floating building can adjust its buoyancy to the effect of rising sea level and land subsidence. This building can be applied in sea tidal flood areas and retention ponds in polder systems. The main materials used as the floating building platform are styrofoam and lightweight concrete. This study aims to determine the stability, capacity and buoyancy of the prototype platform using styrofoam and lightweight concrete. This study uses mathematical analysis methods and prototype testing with laboratory experiments. Based on the analysis result for the styrofoam material platform with dimensions of 2 × 2 × 0.62 m, a buoyancy force of 2.73 tons was obtained with the Safety Factor (SF) of 1.25. A lightweight concrete weight of 0.97 tons, the load that can be supported by the prototype is 1.2 tons. The prototype of the styrofoam material platform with a lightweight concrete cover has a stable condition with a value of m&gt;0, namely the center of object weight (B0) is +0.74 meters and the metacentric value is +0.9 meters.
Distribution and monogamy of quantum coherence for open three-qubit systems under local noisy channels

Abstract We study the distribution and monogamous relations of quantum coherence for three-qubit systems under the influence of local amplitude-damping, phase-damping and random unitary channels, respectively. We find that under the influence of the considered channels, the W state has both global and bipartite coherence, and its monogamy is always equal to zero. For the Greenberger–Horne–Zeilinger) state, however, there is only global coherence in the time evolution and the monogamy is always positive. We also present a partially entangled state, <?CDATA $|\Psi\rangle$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>|</mml:mo> </mml:mrow> <mml:mi mathvariant="normal">Ψ</mml:mi> <mml:mo fence="false" stretchy="false">⟩</mml:mo> </mml:math> , which has global, bipartite, and monomeric coherences and negative monogamy during the whole time evolution.
Text Classifications Learned from Language Model Hidden Layers

Advancements in machine learning methods have yielded powerful natural language generation models. However, in general, these models have drawn concern for being both uninterpretable and uncontrollable. Model interpretability and control have become important topics of interest among researchers. We explore a variety of machine learning methods to classify the hidden states of language models. This classification enables model interpretation at a deep semantic level and is a necessary part of recently proposed model control methods. We show further that the use of language model hidden layers as text representations in classification tasks may be more reliable in some applications than more standard text representations.
Cryptanalysis of an Anonymous and Traceable Group Data Sharing in Cloud Computing

In cloud environments, group data sharing has become a hot topic in recent years. How to share data securely and efficiently in cloud environments is an urgent problem to be solved. Recently, an anonymous and traceable group data sharing scheme was proposed by Shen et al. to address this issue. They constructed their scheme using a group signature scheme as the building block. In this comment, we discuss the security of their group signature scheme and point out that it does not achieve the anonymity which they claimed and give a corresponding attack.
A differentiation between Image Mining and Computer Vision in the application area of Big Data

The technological progress in image acquisition, image processing, and improvements in the field of big data in the last decades opens a wide range of possible applications for image processing using analytical methods. The most popular concepts in this field of tension are computer vision and image mining. However, the issue is that there is no clear distinction between these two concepts in the current scientific discussion. To address this need, a structured literature review was carried out. Thereby, the key characteristics as well as the process of image mining could be identified and compared to computer vision. Thus, this research in progress study was a first step towards differentiating these two concepts. In conclusion, a hypothesis could be derived, which will be considered now in our ongoing research.
Improved Word Representations Via Summed Target and Context Embeddings

Neural embedding models are often described as having an `embedding layer', or a set of network activations that can be extracted from the model in order to obtain word or sentence representations. In this paper, we show via a modification of the well-known word2vec algorithm that relevant semantic information is contained throughout the entirety of the network, not just in the commonly-extracted hidden layer. This extra information can be extracted by summing embeddings from both the input and output weight matrices of a skip-gram model. Word embeddings generated via this method exhibit strong semantic structure, and are able to outperform traditionally extracted word2vec embeddings in a number of analogy tasks.
AI Legal Counsel to train and regulate legally constrained Autonomous systems

In this paper I motivate and present the idea of a legal counsel for autonomous algorithmic agents (or A-bots). In its simple incarnation the counsel is a legal oracle which is able to tell the A-bot the legality of any action at any moment in time. I enumerate a number of capabilities that the oracle requires to work in a regulated environment with a rich set of rules. A point of difference is that the oracle can be used for training and online control of A-bots and is relatively independent to the learning methodology chosen. It is an example of a hybrid system known as a shield, which uses methods from both symbolic and statistical AI.
A Recurrent Method for Structural-Parametric Identification of Fuzzy Neural Networks

The paper proposes a structural-parametric identification method to models in form of fuzzy rule based neural networks. The proposed method is a system identification method which includes a new method for structure and parameter initialization. Algorithm solves the problem of initial structure and parameter identification and adjustments of membership functions. Numerical experiments based on datasets are presented to illustrate the advantages of the proposed method and shows effectiveness and accuracy of the proposed approach.
A Brief Analysis of GAN Variants on Image Classification and Generation

Abstract Recently, the Generative Adversarial Network (GAN) has attracted much attention in a large range of applications, such as image classification, plausible image generation and image completion. Lots of GAN variants have been proposed. This paper focused on two popular GAN variants, including GAN and Auxiliary Classifier Generative Adversarial Network (ACGAN) and made a comparison between them. The experiment on CIFAR-10 and anima dataset shows that ACGAN can perform better than original one with better accuracy. Furthermore, we find ACGAN has a worse inception score (IS), which indicates that ACGAN is not capable of generating realistic pictures.
A New Hybrid Method Based on ACO and PSO with Fuzzy Dynamic Parameter Adaptation for Modular Neural Networks Optimization

Bio-inspired algorithms are metaheuristics that simulate insects or animal behavior for solving optimization problems. Ant colony Optimization (ACO) and Particle Swarm Optimization (PSO) are two of the main bio-inspired methods used to solve different kind of optimization problems. On the other hand, there are hybrid optimization methods consisting of the combination of two or more metaheuristics to solve a common set of problems. In this paper a new hy-brid method combining ACO and PSO is presented. This method is tested with the optimization of modular neural networks (MNNs) architectures. The MNN learns to classify images of human faces from the ORL face database. Another interesting contribution of this work is the implementation of fuzzy inference sys-tems to dynamically adjust the values of some parameters for the metaheuristics used in this paper. Therefore, in this paper, the main idea is to design a hybrid method to optimize the modular neural networks architectures. The achieved re-sults using the proposed hybrid method outperform the results with the individual optimization methods. The main contribution is the proposed new hybrid meta-heuristic that can solve complex problems more efficiently than ACO or PSO individually.