Reviewerly/exHarmony huggingface dataset:
Federated learning with auxiliary generator

Federated Learning (FL) is a client-based distributed machine learning framework, which aim to train a central- ized model with protecting data privacy. However, the decentralized datasets pose a challenge on the traditional FL, as they are non-independent and identical distributed (non-IID). Non-IID settings can result in client result gradient biases, which may decrease the accuracy of the model. To address this issue, we propose Federated Learning with Auxiliary Generator(FedGen), which keeps the consistent of data distribution between clients leveraging the auxiliary generator, and the gradient become more accurate. To demonstrate the effectiveness of proposed method, extensive experiments are conducted on the benchmark datasets, including the MNIST and LEAF dataset. The experimental results shows that FedGen converges 1.2 times faster than FedAvg, while the accuracy can be increased.
Preliminary Evaluation of Search Space Characterization and Effects of Machine Learning

The bulk of published literature in the field of Machine Learning (ML) documents variations on standard ML algorithms, or describes the application of a ML algorithm to a given problem. The current growing fad of applying ML to every problem is resulting in poor solutions and wasted resources. The optimization of the solution algorithms largely focuses upon industrial usages of ML, but rarely are effects of the underlying problem upon the ML algorithm discussed. That is, there is typically no investigation or definition on the solution space in which the ML algorithm is operating in. As there is no widespread discussion of the different forms solution spaces may take, there exists no standard for knowing when to apply ML algorithms to a given problem. This study is designed to research current terminology and characterization of search spaces and their effects upon ML algorithms by rigorous evaluation of ML performance. The result of this study shall generate an understanding on the value and feasibility of search space characteristics. A guide for search space identification, solution algorithm seeding, and expected results for baseline and seeded implementations of ML is predicted to materialize.
Trust Management for Multi-Agent Systems Using Smart Contracts

Multi-Agent Systems (MAS), a group of agents that work together to solve complex problems, always have concerns around trust management among the agents in a cyber physical system. With the technological advancements in decentralization, automation, and interactions between physical and software agents, the demand for using MAS is increasing though trust management between the agents remain challenging. To alleviate these challenges, the focus of this paper is to explore blockchain based smart contracts for trust management in MAS, especially in the following three aspects: analyzing the interaction mechanism of smart contracts within a MAS environment; providing a potential framework for smart contract based trust management for MAS; and finally, discussing the challenges of deploying and integrating smart contracts within a MAS framework. The outcome of this paper provides a novel approach to solidifying agent to agent trustworthy communication.
Finding maximum sum segments in sequences with uncertainty

Given a sequence of n numbers and two positive integers L and U with L≤U, the maximum sum segment problem is to find a segment of the sequence with length between L and U such that the sum of numbers in this segment is maximized. In this paper, we introduce the concept of uncertainty into the maximum sum segment problem, where each of the n numbers of the sequence is not given as an exact value but as an interval characterizing the possible range of its value. In such a sequence with uncertainty, we are interested in segments that have potentiality to be maximum sum segments. A segment is a potential maximum sum segment if there exists a possible assignment scenario of the uncertain numbers such that the segment has maximum sum under this assignment. We define the maximum sum segment with uncertainty (MSSU) problem, which consists of two sub-problems: (1) reporting all potential maximum sum segments; and (2) counting the total number of those segments. For the case that L=1 and U=n, we propose an O(n+K)-time algorithm for the reporting problem and an O(n)-time algorithm for the counting problem, where K is the number of potential maximum sum segments. For general L and U, we give an O(n(U−L))-time algorithm for either the reporting or the counting problem. Note that we assume the word-RAM model of computation.
A Survey on the Application of SGX in Blockchain Area

As an emerging technology, blockchain is widely used in encrypted digital currencies and has an important impact in various fields such as finance, cloud storage, and Internet of things (IoT), etc. However, it faces various challenges in the process of its development and application: waste of resources, limited privacy protection, poor scalability, etc. Intel Software Guard Extensions (SGX), as a new trusted computing technology, brings solutions to the above challenges in the blockchain field. Based on the hierarchical structure of the blockchain, we are the first to systematically discuss the application status of SGX in the blockchian, including consensus layer, the ledger topology layer, the contract layer, and the application layer. Meanwhile, we summarize the advantages and challenges of SGX in the field of the blockchain, and look forward to the future development direction and the possible research topics.
A Novel Voiceprint Verification Technology Through Deep Neural Network

This paper presents an integrated neural network for voiceprint verification. The system implements two types of deep architecture: ResNet-18 and SincNet to extract the acoustic features. The triplet loss function is used to distinguish same-speaker and different-speaker pairs based on cosine similarity during the training phase. Experiments on three different datasets reveal that integrated system exceeds the baseline of DNN-based i-vector. The system reduces equal error rates (EERs) from the baseline method by 59.7, 54.5 and 58% on the datasets—Voxceleb1, LibriSpeech and AISHELL-1, respectively. In addition, the integrated model decreases the EERs in the single models.
A Kernel Goodness-of-fit Test for Maximum Likelihood Density Estimates of Normal Mixtures

This article contributes a methodological advance so as to help practitioners decide in selecting between parametric and nonparametric estimates for mixtures of normal distributions. In order to facilitate the decision, a goodness-of-fit test based on the integrated square error difference between the classical kernel density and the maximum likelihood estimates is introduced. Its asymptotic distribution under the null is quantified analytically and a hypothesis test is then developed so as to help practitioners choose between the two estimation options. The article concludes with an example which exhibits the operational characteristics of the procedure.
Stacked conditional adversarial networks and multi-patch loss for image depth estimation

Generative Adversarial Nets (GAN) were proposed as generative models estimated by an adversarial process between a generator and a discriminator. This allows the generator to learn a distribution of training data. Conditioning this network on additional input provides a conditional GAN which has been shown to be a good architecture for image translation tasks. We propose two changes to this network, with a focus on image depth estimation task. First, by using a stacked coarse to fine s tack of GANs, and second by using a multi-patch discriminator loss we show improved results in estimating depth from single images.
Quantum state merging with bound entanglement

Quantum state merging is one of the most important protocols in quantum information theory. In this task two parties aim to merge their parts of a pure tripartite state by making use of additional singlets while preserving correlations with a third party. We study a variation of this scenario where the shared state is not necessarily pure, and the merging parties have free access to local operations, classical communication, and PPT entangled states. We provide general conditions for a state to admit perfect merging, and present a family of fully separable states which cannot be perfectly merged if the merging parties have no access to additional singlets. We also show that free PPT entangled states do not give any advantage for merging of pure states, and the conditional entropy plays the same role as in standard quantum state merging quantifying the rate of additional singlets needed to perfectly merge the state.
Designing Shiyam Application: An Android-based Fasting Reminder

Indonesia is a country with Muslim majority. Muslims implement fasting as one of the essential Islamic pillars. Information regarding fasting is substantial for Muslims, especially warnings of imsak, sahur and iftar times. The integration of information related to fasting schedules and provisions in mobile devices with Android is a promising solution for Muslims. Designing the Shiyam application as the fasting reminder is great to perform. This application had been developed using the Waterfall model, emphasizing on the development of systematic and sequential information systems. The implementation of the Shiyam application that focuses on the aspect of fasting can provide detailed fasting-related information and provides warnings at the time of imsak, iftar, and sahur, which can help Muslims in carrying out their worship.
Adaptive interpolation for heterogeneous multidimensional signals fusion

we propose an adaptive parameterized interpolator to solve the problem of heterogeneous multidimensional signals fusion. This interpolator is based on the automatic switching between several interpolating functions at each point of the signal. We propose a set of interpolating functions for the case of arbitrary signal dimension. Besides, we describe a procedure for the optimization of the adaptive interpolator according to the criterion of the minimum energy of post-interpolation residues. We perform the optimization of the adaptive interpolator for a multidimensional signal of reduced resolution. Then we use the found optimal parameter value to interpolate the signal of the required resolution. We perform the computational experiments in natural multidimensional signals to investigate the proposed adaptive interpolator. This interpolator can significantly (up to 26%) increase the efficiency of heterogeneous signals fusion.
Experimental Investigation of Algorithms for Simultaneous Localization and Mapping

This paper describes a mobile robot system designed for simultaneous localization and mapping. The architecture of a robotic mobile system based on the mini-tractor chassis is considered. The existing and modern methods and approaches to solving the SLAM problem are described, as well as the results of experimental studies of the work of methods on a mobile robot. A description of the developed robotic system for solving the navigation problem and constructing a route map is given. The issues addressed in this paper include the design, development and experimental testing of the mobile robot. The advantages, disadvantages of the algorithm, as well as the direction of further research are described in this work.
Adaptive Flower Pollination Algorithm Based on Spatial Dispersal

An adaptive flower pollination algorithm (AFPA) is evolved with respect to the accuracy and stability performance. AFPA is developed by modifying both global and local search operators. The exploration and exploitation abilities of the algorithm are thus improved by integrating a spatial dispersal mechanism adapting with the number of iterations to the global search and the best solution to the local search. Its performance is well corroborated through several benchmark functions with respect to various measuring parameters. AFPA is exhibited the distinguished accuracy and stability performance better than FPA for most benchmark functions. The obtained results show that AFPA has the better global and local search abilities over FPA.
Research on a Blockchain Consensus Algorithm Based on Digital Copyright

Blockchain technology is a decentralized network structure based on the p2p network and cryptography technology as the core. It establishes trust relationships on the network with pure mathematical methods, without relying on intermediate platforms to establish trust relationships. In order to better protect the subjective rights and interests of digital copyright and reduce the occurrence of various infringement incidents, a digital copyright consensus algorithm based on blockchain technology is proposed. With the help of Map function conditions, the internal data transmission rate of the multi-channel model is improved, so as to obtain accurate time complexity values, and complete the design of the digital copyright blockchain consensus algorithm. Experimental results show that, compared with the APBFT algorithm, the maximum value of the DPA permission resistance index in this paper is increased to 83%, while the directional transmission time of data information is shortened to 0.08 s, which greatly reduces the probability of various digital copyright infringement incidents.
Method of conducting video lectures and online broadcasting in the system of preparation of bachelors from cyber security in the conditions of educational digits

The article presents the method of conducting video lectures and online broadcasts in the system of training bachelors in cybersecurity in an educational and digital environment. It is determined that professional training of personnel in various industries in the modern world is an extremely important task of the state. Its solution becomes significant depending on how close these or those specialists are to the sphere of society. The meaning of the concept of "training" is defined, which is defined as a set of special knowledge, skills, qualities, work experience and norms of behavior that provide the opportunity to work successfully in a particular profession; as a process of communicating relevant knowledge and skills. " It is specified that the educational and digital environment can be understood as a set of organizational and pedagogical conditions for learning that contribute to the motivation of higher education students to self-development, self-education and is a key component of professional development of future professionals through information resources and services. It is emphasized that video lecture is the main form of conducting educational classes in the educational-digital environment, intended for mastering theoretical material. Each individual lecture is a component of the course of lectures in the educational and digital environment of the course discipline. Let's outline the general requirements for video lectures for bachelors in cybersecurity in the educational and digital environment. It has been determined that online broadcasting is a convenient way to communicate with bachelors in cybersecurity in real time in an educational and digital environment. At the expense of such broadcast it is possible to arrange for them game streams, to hold online seminars or to answer questions. The presented method of conducting video lectures and online broadcasts for the training of bachelors in cybersecurity is characterized by mandatory professional training for bachelors to obtain the qualification of information security specialist in information and communication systems and provides practice to provide training conditions in the real environment of future professional activities. Prospects for further research are the development of technology for the introduction of video lectures and online broadcasts in the system of training bachelors in cybersecurity and verification of the results.
Image Generation with the Enhanced Latent Code and Sub-pixel Sampling

Synthesizing realistic images from coarse layout is a challenging problem in computer vision. For generating the image with multiple objects, there still exists the problem of the shortage of object details and certain artifacts in existing methods. In this paper, we propose an image generation approach based on the Modified Variational Lower Bound and Sub-pixel Sampling (MVLBSS). Firstly, we modify variational lower bound to form the enhanced latent code with the semantic of object appearance. The change of variational lower bound is implemented by introducing the object category information in the crop estimator and layout encoder to capture the details of object appearance. Secondly, to reduce artifacts, we propose a sub-pixel sampling module with up-sampling and convolution operation. Lastly, our proposed approach is trained in an end-to-end manner to the global optimal image generation. The comparison experimental results with the state-of-the-art methods on both COCO-stuff and Visual Genome datasets show that the proposed method can effectively improve the quality of generation image. Moreover, ablation experiments demonstrate that the improved strategy is effective for enhancing image details and eliminating artifacts.
PATTERN RECOGNITION OF METEOROLOGICAL FIELDS CAUSING HEAVY RAINFALL DISASTER IN HIROSHIMA USING SELF-ORGANIZING MAP

本研究では,自己組織化マップを適用し,九州地方北部と中国地方西部を対象に暖候期の気象場パターンと豪雨頻度の関連性を調べ,広島県で発生した40年間の土石流災害事例がどの気象場パターンに属するか調べた.また,広島県で記録的な災害になった平成30年7月豪雨の気象場の特徴を調べ,過去の類似事例の特徴と比較した.その結果,広島の災害事例は,共通して下層ジェットと湿舌の影響を受け,互いに類似する特徴を持っていた.また,平成30年7月豪雨の事例は,豪雨発生の可能性が高い気象場パターンに分類され,2日間もそのパターンに晒され続けた結果として記録的な災害へと繋がった.その類似事例に2009年7月24日の豪雨事例があり,両者に共通する特徴として,福岡県と広島県に災害をもたらしたこと,豪雨域が広範囲であったことが挙げられる.
Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification

Managing high-bandwidth application traffic through identification of bandwidth-heavy Internet traffic is important for network administration. classification based on statistical flow features was proven as an encouraging method for identifying Internet traffic. Early estimation of statistical flow features from first n packets still plays an essential role in accurate and timely traffic classification. In this work, we investigate the impact of early estimation of statistical flow features for on-line P2P classification in terms of accuracy, Kappa statistic and classification time. Simulations were conducted using available traces from the University of Brescia. Results illustrate the early statistical flow features estimation for gives the most significant accuracy and efficiency to detect P2P traffic.
Tweet-Inspired Intelligent Subselection of Semantically-Related Lyrical Training Data

A current challenge in AI research is enabling AI systems to be inspired by external sources. We present a method for subselecting portions of a training corpus based on relevance to an external inspiring source. Our system takes an external, text-based inspiring source (e.g., tweet), extracts weighted lexical topics contained in the inspiring source, and uses these weighted topics to rank training instances in a corpus of song lyrics according to their relevance to the inspiring source. The system extends on the capabilities of the Empath framework by automatically generating domain-specific categories and mapping functions. The system offers a novel approach toward improved lexical semantic analyses for comparative corpus ranking.
Global Internet Traffic Routing and Privacy

Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.
Communication Specific Emitter Identification Based on Transfer Learning

Abstract In the application of specific communication emitter identification(CSEI), the emitter slight features will change along with different times, places and conditions, which makes training samples and test samples obey different distribution and makes most machine learning algorithms not work well. To solve this problem, this paper introduced transfer learning to CSEI. Compared with traditional machine learning, transfer learning does not relax the assumption that the data is independent and distributed. It can explore useful knowledge from the data which is different from the target domain but similar to the source domain. It can also learn the classifier when the labeled samples in the same domain are insufficient. Extensive experiments show the effectiveness of the new method.
Learning Disentangled Representations with Attentive Joint Variational Autoencoder

Deep generative models for disentangled representation learning in unsupervised paradigm have recently achieved promising better performance. In this paper, we propose a novel Attentive Joint Variational Autoencoder (AJVAE). We generate intermediate continuous latent variables in the encoding process, and explicitly explore the underlying instinct varieties and diversities which are implicitly contain in training samples and fuse them by considering each variable’s dynamic weight obtained by self-attention mechanism. Our method seeks to devise multiple intermediate latent variables, and comprehensively balance variation factors included in the samples to generate a final latent variable. The experimental results on several real-world data sets have demonstrated that the performance of our proposed framework outperforms current state of the art disentangling methods.