Reviewerly/exHarmony huggingface dataset:
Optimizing Generative Adversarial Networks in Latent Space

Recent GANs have achieved great progress in generating high quality images, however, GANs still suffer a lot from instability and mode collapse. In this work, we propose a new framework, Latent Optimization Generative Adversarial Networks (LO-GANs). We give a definition of mode collapse and derive that generator with capability of reconstructing training images alleviates the mode collapse. Moreover, this capability benefits the stability of GANs. Our method takes advantages of both GANs and non-adversarial methods, as it is capable of both reconstructing training images and generating new high-quality images. Instead of learning a images representation by employing an encoder, we directly optimize a learnable image representation set for image reconstruction. Besides image reconstruction, adversarial regularization is adopted to match the aggregated distribution of the latent image representation with arbitrary prior distribution. Our framework is flexible as it is implemented by adding a latent image representation set and a representation discriminator to vanilla GANs. Experimental results show that our models are capable of reconstructing training images and mode collapse is alleviated significantly. Benefitting from reconstructing constraints and latent image representation optimization, our models are more stable during training than vanilla GANs.
HardStack: Prevent Stack Buffer Overflow Attack with LBR

Stack buffer overflow attack can temper with software control flow by continuously injecting data into stack and overwriting function return address. In this paper, we propose a new method to solve buffer overflow attack with the support of Intel LBR, which we call it HardStack. Usually, the function return address is stored in the stack, and it is easy to be overwritten. In our opinion, HardStack changes this situation. In HardStack, the return address is calculated on hardware information and software information together. In this way, even if the attacker tampers with the data in the stack, he fail to hijack the software control flow without modifying on LBR. Our discussion show that HardStack increases the delay by thousands of times in a certain call, but which has no obvious effect on the running of the software.
Training in Immersive Virtual Reality: A Short Review of Presumptions and the Contextual Interference Effect

The increase of shipped consumer immersive virtual reality (IVR) up to 6 million units in 2019 shows the increasing popularity of this medium. Invests of 8 billion dollars are anticipated in the next five years for the training sector. With the development the question arise, what effects and advantages can be expected using IVR in human training? This paper reviews three important areas, when it comes to the design of immersive virtual reality trainings: 1. cognitive load, 2. spatial imagination and the contextual interference effect.
Knowledge-embodied attention for distantly supervised relation extraction

Knowledge bases (KBs) provide a large amount of structured information for entities and relations, which are successfully leveraged in many natural language processing tasks. However, distantly supervised relation extraction only utilizes KBs to automatically generate datasets, while ignoring the b ackground information in KBs during the relation extraction process. We herein propose a knowledge-embodied attention that leverages knowledge information in KBs to reduce the impact of noisy data for distantly supervised relation extraction. Specifically, we pre-train distributed representations of KBs with the knowledge representation learning (KRL) model, and subsequently incorporate them into relation extraction to learn sentence-level attention weights. The experimental results demonstrate that our approach outperforms all baselines, thus indicating that we can focus our attention on valid data by leveraging background information in KBs.
AN EFFICIENT SPEECH GENERATIVE MODEL BASED ON DETERMINISTIC/STOCHASTIC SEPARATION OF SPECTRAL ENVELOPES

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.
A Searchable Encryption Scheme Based on Elliptic Curves

As in the present we live in the era of the digitalized data, we need to protect our data against malicious users. Cloud computing represents a key technology nowadays, due to its numerous advantages, but it raises some concerns about security. Along the years, many security techniques have been proposed. One important technique is searchable encryption, which allows an authorized user to submit encrypted search queries over encrypted data. The elliptic curves have attracted the researchers' attention, because of many reasons. Elliptic curves cryptography (CC) provides a good security regarding a key size. In ECC, the cryptographic operations run faster on smaller chips or complex software, because of compact implementation provided by a small key size. The basis of ECC is the discrete logarithm problem, which can be extremely difficult in some conditions. In this paper we propose a searchable encryption scheme based on elliptic curves, where in the encryption process we will use a variation of complex quadratic polynomial.
Dance with a Robot

This late-breaking report presents a method for learning sequential and temporal mapping between music and dance via the Sequence-to-Sequence (Seq2Seq) architecture. In this study, the Seq2Seq model comprises two parts: the encoder for processing the music inputs and the decoder for generating the output motion vectors. This model has the ability to accept music features and motion inputs from the user for human-robot interactive learning sessions, which outputs the motion patterns that teach the corrective movements to follow the moves from the expert dancer. Three different types of Seq2Seq models are compared in the results and applied to a simulation platform. This model will be applied in social interaction scenarios with children with autism spectrum disorder (ASD).
Web Ranking of Higher Education Institutions: An SEO Analysis

There are numerous ranking systems that provide web rankings for colleges and universities around the world. The aim of this paper is to present the findings of SEO audit and analysis of 34 HEI websites in Oman in terms of web presence and impact. The study used the SEO audit metrics-domain authority score, linking root domain, ranking keywords, and spam score which are provided by Moz. The findings revealed that only one (1) HEI has achieved a good quality for domain authority while all the others have obtained a low quality only for domain authority. The same HEI has scored high on the linking root domains and ranking keywords. However, 24 % of the HEIs have attained a zero spam score while 76% of the HEIs have attained a low spam score. The study recommends that HEIs should exert more efforts in improving their SEO to increase visibility and impact on the web.
Application of Railway Passenger Flow Statistics Based on Mask R-CNN

Abstract This paper discusses and implements the application of Mask R-CNN in railway passenger flow statistics. Mask R-CNN is a target detector based on candidate region, combining deep learning, neural network and image recognition technology, and has a wide range of application value in the field of intelligent monitoring.The paper focuses on the algorithm named Mask-Flow that Mask R-CNN uses the same color for tracking on the same target, and counts the number of pedestrians. And the passenger flow statistics are compared with Retina-Net in the railway scene of the dense crowd, which proves the accuracy of the method in the paper.
Disease Prediction using Hybrid Optimization Methods based on Tuning Parameters

Swarm Intelligence (SI) is increasing day by day in the various research fields. There are many swarm-based optimizations introduced since the early '60s, Evolutionary Algorithms (EA) is the most updated one. All Evolutionary Algorithms have proved their capability to resolve most of the optimization problems. These algorithms are using for training the neural networks in this paper. The main difficulty for any optimization problem is selecting the correct values of parameters to get possible results. The main idea to get the best convergence rate and best performance is to vary the parameters of the algorithms. This paper provides a comparison of the most used and essential swarm-based optimization algorithms. Here, comparing the optimization algorithms, Particle Swarm Optimization (PSO), and Multi-Verse Optimization (MVO) before and after tuning the parameters with three different datasets.
A Mixed Semantic Features Model for Chinese NER with Characters and Words

Named Entity Recognition (NER) is an essential part of many natural language processing (NLP) tasks. The existing Chinese NER methods are mostly based on word segmentation, or use the character sequences as input. However, using a single granularity representation would suffer from the problems of out-of-vocabulary and word segmentation errors, and the semantic content is relatively simple. In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding. Different from other models, our method combines character and word features at the sequence level, and the attention mechanism computes similarity on the total sequence consisted of characters and words. The character semantic information and the structure of words work together to improve the accuracy of word boundary segmentation and solve the problem of long-phrase combination. We validate our model on MSRA and Weibo corpora, and experiments demonstrate that our model can significantly improve the performance of the Chinese NER task.
Fast Adaptation via Meta Learning in Multi-agent Cooperative Tasks

Multi-agent deep reinforcement learning (MADRL) has been used for disaster rescue and other robotic applications. In most MADRL cases, agents are trained only to deal with specific tasks, so once there are unpredictable changes in target tasks, the earlier trained-models might no longer work, and agents have to get trained again from scratch in limited time, which means traditional MADRL is unsuited to unpredictable post-disaster environments. In order to promote the scalability and flexibility of the multi-agent system, meta-learning, which has achieved few shot learning successfully in supervised deep learning field, could reuse the earlier models as the prior knowledge to guide and speed up the new task learning process. In this work, we propose a framework to apply meta learning methods to multi-agent deep deterministic policy gradient (MADDPG), and achieve efficient adaption to new tasks with less time and fewer samples. Some common experiences in the previous tasks learning will be refined as the meta knowledge. Once the tasks get changed with new scenarios, agents will get retrained with good initial network parameters based on this meta knowledge. Although the new scenarios might be different from the previous scenarios, agents could quickly adjust their policy and get better performance within only few episodes. In two experiments, our method performs better learning efficiency than others, getting higher rewards in the first several episodes of new tasks learning.
Unsupervised Domain Adaptation for Semantic Segmentation with Symmetric Adaptation Consistency

Unsupervised domain adaptation, which leverages label information from other domains to solve tasks on a domain without any labels, can alleviate the problem of the scarcity of labels and expensive labeling costs faced by supervised semantic segmentation. In this paper, we utilize adversarial learning and semi-supervised learning simultaneously to solve the task of unsupervised domain adaptation in semantic segmentation. We propose a new approach that trains two segmentation models with the adversarial learning symmetrically and further introduces the consistency between the outputs of the two models into the semi-supervised learning to improve the accuracy of pseudo labels which significantly affect the final adaptation performance. We achieve state-of-the-art semantic segmentation performance on the GTA5-to-Cityscapes scenario, a widely used benchmark setting in unsupervised domain adaptation.
Anomaly Detection in Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and crucially, it encodes each input signal (either continuous or discrete) from a joint activation and uniform combinations of filters, allowing the correlation across the input signals to be captured. The performance of C-ADDICT, is evaluated on a representative dataset composed of real spacecraft telemetries with an available ground-truth, providing promising results.
Cybersecurity Readiness of E-tail Organisations: A Technical Perspective

Cybersecurity readiness is a challenging issue for online retail businesses which are losing billions of dollars due to cyber-crimes and a lack of readiness to manage these. Therefore, research into cybersecurity readiness in the online retail industry is needed. Technical tools are the foremost measures of defence against these attacks. This study investigates cybersecurity readiness from the technical perspective in some UK online retailers. This research adopted a qualitative case study approach with semi-structured interviews for collecting data. A total of 15 interviews were conducted with an online retail company's staff and management who had responsibility for managing cybersecurity. A thematic analysis method was used to analyse the qualitative data. The research findings show that the company is facing internal and external threats to their information systems and their technical defences are not very effective at present. The company should consider investing more resources in the technical controls to prevent these attacks.
Research and Application of Integrated System Design of Smart Substation Secondary System Based on SCD/CAD File Mapping Technology

The physical part of the secondary system design in the smart substation is completed by the design institute, and the conventional CAD drawings are issued. The part of the virtual circuit design is usually completed by the integrator, which is reflected in the SCD file. The main problem is that the design of the physical circuit of the smart substation and the design of the logical circuit are separated, both in terms of the unit and the embodiment of the result, which affects the construction process of the smart substation and the subsequent operation and maintenance. In this paper, an integrated design scheme of secondary system of smart substation is proposed. Based on the standardized definition of secondary equipment of smart substation, a standard wired expert database is constructed to realize the automatic design of secondary virtual circuit. Based on the standardized port definition and explicit port connection mode of secondary equipment, a design method of automatic fiber loop connection through the connected virtual loop is proposed. By studying the physical circuit drawings and the mapping relationship between logical circuit design documents, the intelligent substation physical mapping between circuits and logic circuit principle and the SVG file which can be used as intelligent substation drawing one with virtual terminal intelligent substation design results reflect tool are put forward, effectively solving the problem of physical circuits and logic circuit split. The method described in this paper has been applied in engineering. Through the application, the feasibility and effectiveness of the scheme have been verified and the design level, construction quality and reliability of intelligent substation have been practically improved.
Learning Network Representation Through Reinforcement Learning

Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn representations from it. In contrast, we propose, reinforcement learning network representations (RLNet), which explores the idea of using reinforcement learning to learn to explore the network and to obtain network representations. Based on reward signals, RLNet learns an actor which uses a policy to determine the network navigation actions. RLNet uses node representations to parameterize its policy, and the representations are learned together with the policy. Through experiments based on multiple datasets, we show that RLNet can obtain promising results in link prediction tasks.
1.5GBIT/S 4.9W Hyperspectral Image Encoders on a Low-Power Parallel Heterogeneous Processing Platform

This work explores the utilization of low-power heterogeneous devices for parallelizing the compute-intensive hyper-spectral and multispectral image compression CCSDS-123 entropy encoders. Multithread processing allows for the near-optimal system's bandwidth to be exploited increasing the system overall performance. The experimental platform consists of a low-power Jetson TX2 GPU equipped with an ARM Cortex-A57 and Denver 2 host processors, reporting more than 1552 Mb/s and, more importantly, 315 Mb/s/W, all running under a global 5 W power budget, which makes it a good candidate for onboard image compression.
Site Generator for Small Open and Private Online Courses

The article analyses approach for developing Small Open and Private Online Courses (SOOC and SPOC). The technology and the program for assembling electronic educational resources from heterogeneous fragments, including video clips, images, texts, html, pdf files are considered. The program creates a static site with a navigation structure ready for use in the learning process. You can work with a static site on a personal computer, a tablet or a smartphone. In addition to the static sites generator a server program that provides user and content management is considered. It additionally supports interactive Python applications with the user interface automated generation based on input and output parameters description.
Improving the Chronological Sorting of Images through Occlusion: A Study on the Notre-Dame Cathedral Fire

We live in a connected society in which no major event - from music concerts to terrorist attempts - happens without being recorded by a smartphone and shared instantly to the world. This flow of generated data is often unstructured and carries no reliable information with respect to time of capture of such media pieces. Consequently, posterior reconstruction, understanding and fact-checking of that event are hindered if the data is not properly organized. In this work, we train a data-driven method to chronologically sort images originated from a real event, the Notre-Dame Cathedral fire, which broke out on April 15th, 2019. Our network leverages visual clues - such as the destruction of the cathedral's structure or the evolution of the fire - to position an image in time. We investigate several occlusion strategies to improve classification accuracy, generalization and explainability of our method. Besides comparing the performance of each strategy, we evaluate their activation maps, i.e., the important regions considered for classification by each method.
A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines

Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many applications. Here we attempt to apply extreme learning machines to a reinforcement learning problem in the same manner as gradient based updates. This new algorithm is called Extreme Q-Learning Machine (EQLM). We compare its performance to a typical Q-Network on the cart-pole task - a benchmark reinforcement learning problem - and show EQLM has similar long-term learning performance to a Q-Network.
An Efficiency Study of Adaptive Median Filtering for Image Denoising, Based on a Hardware Implementation

Generally, in digital image processing applications and real-time signal, some unwanted signals capture the image which is termed as noise. It is desirable to be able to perform some image filtering techniques frequently used to reduce or eliminate noise on an image or signal. The two-dimensional spatial median filter is the most commonly used filter for image denoising. The median of a given sequence can be determined by sorting all values in the sequence and by choosing the middle value in the sorted sequence. Commonly, image filters are following up software approach in systems. But hardware implementation prefers in comparison with software implementation for better processing speed. In this paper, we propose an Adaptive Median Filter hardware (AMFh) implementation. Indeed, the 3 × 3, 5 × 5 and 7 × 7 windows techniques are implemented for removal of Salt-Pepper and Impulse Noises from images and simulated using ModelSim (the Verilog language was utilized) and Matlab softwares. We are conducting a study on the effectiveness of the implemented filter, based on two main parameters, the peak signal-to-noise ratio and windows size. PSNR values for a set of benchmark images are calculated to quantify the impact of the proposed AMFh algorithm on the PSNR performance for treated windows size.