Reviewerly/exHarmony huggingface dataset:
Disentangled Human Action Video Generation via Decoupled Learning

Recently there has been remarkable progress in synthesizing realistic human action videos by directly learning to translate pose heatmaps/stick figures to video frames in an end-to-end fashion. However, such models are not suitable for fashion-related applications that typically require flexible manipulations of visual attributes, such as the color of clothes. In this paper, we propose a disentangled human video generation framework conditioned on both the pose sequence and encoded color attributes. We aim to learn an encoder that captures the manifold structure of latent color space and a generator that fully utilizes the encoded color attributes to produce diversely-colored human action videos. To this end, we design a two-stage decoupled learning approach that uses a pre-trained color-aware encoder to guide the disentangled learning of the generator. Furthermore, a color augmentation approach is applied on raw video clips to better shape the distribution of samples in the latent color space. Comprehensive experimental results demonstrate the efficacy of our proposed methods.
UAV Coverage Using Hexagonal Tessellation

Unmanned aerial vehicles (UAVs) are increasingly being used for coverage applications. Some common examples include inspecting agricultural fields for certain plant diseases, tracking wildfire, photogrammetry, flying over an area to find avalanche victims, and several other search and rescue operations. Although fixed-wing UAVs can survey large areas more quickly and have a better battery lifetime compared to multirotor systems, they fail to provide a close up inspection of a certain area by hovering over it. Quadrotors provide excellent inspection capabilities; however, they have notoriously short battery lifetimes. In this paper, we propose a coverage algorithm through hexagonal tiling of a target region. We present a coverage path using the average distance, d, that can be travelled by a drone on a single charge, and the radius, r, of the viewing cone of a typical downward facing camera mounted on it as parameters. We compare our method with classical zigzag coverage pattern. Our results show that for large enough regions, the Hamiltonian circuit that passes through the centers of the tiling hexagons produces a shorter path.
Online collaborative filtering with local and global consistency

Collaborative Filtering (CF) is one of the most popular technologies used in online recommendation systems. Most of the existing CF studies focus on the offline algorithms, a major drawback of these algorithms is the lack of ability to use the latest user feedbacks to update the learned model in realtime, due to the high cost of the offline training procedure. In this work, we propose Logo, an online CF algorithm. Our proposed method is based on a hierarchical generative model, with which, we derive a set of local and global consistency constraints for the prediction targets, and eventually obtain the design of the learning algorithm. We conduct comprehensive experiments to evaluate the proposed algorithm, the results show that: (1) Under the online setting, our algorithm achieves notably better prediction results than the benchmark algorithms; (2) Under the offline setting, our algorithm attains comparable accurate prediction results with the best performed competitors; (3) In all the experiments, our algorithm performs tens or even hundreds of times faster than the comparison algorithms.
Forensic Detection of Child Exploitation Material Using Deep Learning

A precursor to successful automatic child exploitation material recognition is the ability to automatically identify pornography (largely solved) involving children (largely unsolved). Identifying children’s faces in images previously labelled as pornographic can provide a solution. Automatic child face detection plays an important role in online environments by facilitating Law Enforcing Agencies (LEA) to track online child abuse, bullying, sexual assault, but also can be used to detect cybercriminals who are targeting children to groom up them with a view of molestation later. Previous studies have investigated this problem in an attempt to identify only children faces from a pool of adult faces, which aims to extract information from the basic low- and high-level features i.e., colour, texture, skin tone, shape, facial structures etc. on child and adult faces. Typically, this is a machine learning-based architecture that accomplish a categorization task with the aim of identifying a child face, given a set of child and adult faces using classification technique based on extracted features from the training images. In this paper, we present a deep learning methodology, where machine learns the features straight away from the training images without having any information provided by humans to identify children faces. Compared to the results published in a couple of recent work, our proposed approach yields the highest precision and recall, and overall accuracy in recognition.
Long Tail Multi-label Learning

Multi-label learning is an activity research area that many methods arise recently to solve this problem. However, according to the results of current researches, the class imbalance which appears in the most of labels makes the network unable to be trained. In this paper, we propose a Long Tail Multi-label Classification Processing Algorithm (LTMCP) method to deal with this problem which leads training successfully and greatly reduces the parameters of the model. Firstly, we train model for class balance label that only used one network in view of that the performance of deep learning is better than linear networks. Secondly, we introduce a LTMCP method that trains linear network for each long tail label with unbalanced distribution. The final experimental results clearly show that this LTMCP method improves the accuracy while saving a lot of training time.
Lessons Learned from the NOAA CoastWatch Ocean Satellite Course Developed for Integrating Oceanographic Satellite Data into Operational Use

Satellite data are underutilized in many branches of operational oceanography. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access challenging. The mission of the NOAA CoastWatch Program is to make ocean satellite data easier to access and to apply to operational uses. As part of this mission, the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using common analysis software. These tools leverage the data services provided by ERDDAP, a data distribution system designed to make data access easier via a graphical user interface and via machine-to-machine connections. The course has been offered annually since 2006 and has been attended by over 350 participants. Results of post-course surveys are analyzed to measure course effectiveness. The lessons learned from conducting these courses include using the preferred software of the course participants, providing easy access to datasets that are appropriate (fit for purpose) for operation applications, developing tools that address common tasks of the target audience, and minimizing the financial barriers to attend the course.
An Analysis Framework for Product Prices and Supplies in Darknet Marketplaces

Darknet marketplaces are an interesting research area. Most marketplaces are hosted in the Tor network as an anonymous onion service. In this paper we provide a generic approach to build a framework for the collection and analysis of product prices and supplies in such marketplaces. We focus on the technical details how to implement such a framework and how to collect, organize, and analyze the product data. For our framework implementation we provide an evaluation based on collected data from three large marketplaces and present an approach to enrich the analyzed data with additional information from external sources.
Real-time Human Motion Forecasting using a RGB Camera

This paper propose a real-time human motion forecasting system which visualize the future pose in virtual reality using a RGB camera. Our system consists of three parts: 2D pose estimation from RGB frames using a residual neural network, 2D pose forecasting using a recurrent neural network, and 3D recovery from the predicted 2D pose using a residual linear network. To improve the prediction learning quantity of temporal feature, we propose a special method using lattice optical flow for the joints movement estimation. After fitting the skeleton, a predicted 3d model of target human will be built 0.5s in advance in a 30-fps video.
Guideline for enterprise to a Value Plan through Blockchain and ICO

In 2013 a new fund-raising modality was born. It's called Initial Coin Offering (ICO), it's based on blockchain technology and requires a significant enterprise business model review. ICO is not only a new financing approach; through token issue, it permits to establish a wide range of direct relationships between interested counterparts as entrepreneurs, investors, customers, suppliers and other stakeholders; this set of relationships can generate several types of token-based communities depending on each ICO specific goals. ICO is under regulation in several countries and one of the first is Switzerland through government and FINMA ICO Guidelines, which supplies an institutional and legal framework. Given that ICO is recently born and there is a lack of well-defined standards and phenomenon comprehension, investors are not yet confident to operate in this market. In order to face this matter, a deep knowledge of operative mechanisms and a consolidation of standards for fully potential exploitation are needed. The goal of this Paper is to integrate in an innovative way enterprise Business Model, Business Plan and Communication Plan with the Token Plan in order to exploit the ICO critical success factors. As deliverable the Paper identifies some critical factors and furnishes a set of key recommendations, suggestions and guidelines for a successful ICO.
Kernel homomorphic encryption protocol

In electronic environ and cloud computing, the growth rate of noises or errors and computation is increasing day-by-day. Once the number of noises is exceeded the whole process of communication is failed. To reduce this growth of noises and computation, we proposed a novel kernel homomorphic encryption (Ker-HE) scheme. Ker-HE scheme is used to remove the number of noises (of addition and multiplication) by using kernel and kernel homomorphism. These functions are used to control the exceeding length of ciphertext during decryption and remove all the noises or errors. The applied methods of kernel and kernel homomorphism are used to maintain the size of the ciphertext during decryption and prevent all the noises or errors. Therefore our proposed scheme is more suitable in an aspect of security and computations between communicating parties. The performance of our proposed scheme is 79.12% and 86.64% for multiplication operation during encryption and decryption. While under exponentiation operation it has 100% efficiency as compared to existing schemes. The security properties of our proposed scheme such as integrity, confidentiality and un-forge-ability. The hardness of our scheme is based on the Elliptic Curve Discrete Logarithm Problem (ECDLP). In our proposed ker-HE scheme kernel function and kernel homomorphism are used to recover the failure of injection(one-to-one) function.
A Survey of Knowledge Reasoning based on KG

Abstract Knowledge Reasoning(KR) has become the core issue in the field of Artificial Intelligence(AI) and even Natural Language Processing(NLP). KR based on Knowledge Graph(KG) is based on existing KG’s facts. It uses some inference models and algorithms to infer new unknown knowledge and targets at improving the completeness and accuracy of KG. This article presents a brief overview of KR based on KG, expounds the connotation and research scope of it, judges the two main research directions(Knowledge Graph Completion(KGC) and Question Answering over Knowledge Graph (QA-KG)) of current KR and summarizes the four main technical methods. A series of latest results of current research on KR are also listed in this paper. Finally, we look forward to the future improvement of KR.
Semantic Segmentation of UAV Aerial Videos using Convolutional Neural Networks

Semantic segmentation of complex aerial videos enables a better understanding of scene and context. This enhances the performance of automated video processing techniques like anomaly detection, object detection, event detection and other applications. But, there is a limited study of semantic segmentation in aerial videos due to non-availability of the relevant dataset. To address this, an aerial video dataset is captured using DJI Phantom 3 professional drone and is annotated manually. In addition, the proposed research work investigates the performance of semantic segmentation algorithms for aerial videos implemented using Fully Convolution Networks (FCN) and U-net architectures. In this study, two classes (greenery, road) are considered for semantic segmentation. It is observed that both architectures perform competitively on the aerial videos of Unmanned Aerial Vehicle (UAV) with a pixel accuracy of 89.7% and 87.31% respectively.
Drounght Analysis By Using Standarized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) at Bekasi Regency in 2018

Abstract Drought problem is a routine problem occurring in several regions in Indonesia. Bekasi Regency is one of the regions impacted by drought every year. Such threat of drought occuring due to influence of climate is unavoidable. Nevertheless its impact can be minimized if we know the drought pattern of such a region. Meteorologically, drought monitoring can be conducted with various methods. One of the methods used is by knowing the SPI (Standardized Precipitation Index) . SPI method is the rainfall unit occurring under normal condition in such a time scale. Beside meteorologically monitoring, it can also be monitored based on the spread of vegetation influencing the availability of water supply which can be seen from the NDVI (Normalized Difference Vegetation Index) value. The purpose of this research is to find the relation between the SPI (Standardized Precipitation Index) value and the NDVI (Normalized Difference Vegetation Index) value. The result of this research shows that there is a close relation between a region having such a high SPI value and NDVI value. But in a region having a low NDVI value, several regions have high SPI values.
Online Plan Repair in Multi-robot Coordination with Disturbances

This paper addresses the problem of multi-robot coordination in scenarios where the robots may experience unexpected delays in their movements. Prior work by Čáp, Gregoire, and Frazzołi introduced a control law, called RMTRACK, which enables robots in such scenarios to execute preplanned paths in spite of disturbances in the execution speed of each robot, while guaranteeing that each robot can reach its goal without collisions and without deadlocks. We extend that approach to handle scenarios in which the disturbance probabilities are unknown at the start and non-uniform across the environment. The key idea is to `repair' a plan on-the-fly, by swapping the order in which a pair of robots passes through a mutual collision region (i.e. a coordination space obstacle), when making such a change can be estimated to improve the overall performance of the system. We introduce a technique based on Gaussian Processes to estimate future disturbances, and propose two algorithms for testing, at appropriate times, whether a swap of a given obstacle would be beneficial. Tests in simulation demonstrate that our algorithm achieves significantly smaller average travel time than RMTRACK at only a modest computational expense.
Blockchain Technology: Practical P2P Computing (Tutorial)

Blockchain technology comes with the promise to revolutionize the way current IT systems are organized as well as to revise how trust is perceived in the wider society. In spite of the wide attention that crypto-currencies (such as Bitcoin) have attracted, Blockchain technology is more likely to make an impact beyond ongoing speculations on crypto-currencies. Decentralized identity management, transparent supply-chain systems, and IoT governance and security are only few examples of research challenges for which this technology may hold substantial potential. Blockchain technology has emerged at the intersection of two well established research areas: peer-to-peer (P2P) computing and cryptography. In this tutorial, we provide a general overview of the main components behind this technology, we present the difference between the types of Blockchain available today, and we make a high level discussion on its potentials and limitations as well as possible research challenges.
Ethics of IT Security Team for Cloud Architecture Infrastructure in Banking and Financial Services Industry

Information systems over cloud architecture infrastructure for Banking and Financial Services Corporations are under constant monitoring by IT security team. This monitoring is to keep check for vulnerabilities, security breach incidents and intrusion in cloud environments and network. IT security team are equipped with sophisticated monitoring tools and empowered to take action in the event of security breach or cyber-attack. This gives IT security team privilege access rights to block and investigate attachments, data packets and logs of various users of cloud infrastructure. The investigation may involve or provide access to the private information of the users. This information can be in emails, files & folders, personal documents and messenger history. In such events, IT security teams are supposed to follow ethical standards established by organization and show high integrity to keep privacy of the cloud users and confidentiality of the investigation. In this paper, we have identified the roles & responsibilities and ethical behaviour expected from IT security teams. This paper also enlists various events that demand ethical behaviour from IT security teams in Banking and Financial Services Corporations.
Towards a Computational Model of Artificial Intuition and Decision Making

The ability to perform a detailed decision-making approach based on large quantities of parameters and data is at the core of the majority of sciences. Traditionally, all possible scenarios should be considered, and their outcomes assessed via a logical and systematic manner to obtain accurate and applicable methods for knowledge discovery. However, such approach is typically associated with high computational complexity. Moreover, it is non-trivial for researchers to develop and train models with deep and complex model structures with potentially large number of parameters. However, there are compelling evidence from psychology and cognitive research that intuition plays an important role in the process of intelligence extraction and the decision-making process. More specifically, by using intuitive models, a system is able to take subsets from networks and pass them through a process to determine relationship that can be used to predict future decision without a deep understanding of a scenario and its corresponding parameters. When an artificial agent manifests human intuition properties, then we can describe this as artificial intuition. In this article, we discuss some requirements of artificial intuition and present a model of artificial intuition that utilises semantic networks to improve a decision system.
Machine-learning supported vulnerability detection in source code

The awareness of writing secure code rises with the increasing number of attacks and their resultant damage. But often, software developers are no security experts and vulnerabilities arise unconsciously during the development process. They use static analysis tools for bug detection, which often come with a high false positive rate. The developers, therefore, need a lot of resources to mind about all alarms, if they want to consistently take care of the security of their software project. We want to investigate, if machine learning techniques could point the user to the position of a security weak point in the source code with a higher accuracy than ordinary methods with static analysis. For this purpose, we focus on current machine learning on code approaches for our initial studies to evolve an efficient way for finding security-related software bugs. We will create a configuration interface to discover certain vulnerabilities, categorized in CWEs. We want to create a benchmark tool to compare existing source code representations and machine learning architectures for vulnerability detection and develop a customizable feature model. At the end of this PhD project, we want to have an easy-to-use vulnerability detection tool based on machine learning on code.
Designing Smart for Sustainable Communities

This workshop reflects on the role of the HCI community in addressing sustainable development in cities, regional centres and rural communities. The workshop will bring together academics, researchers and practitioners to share their experiences, expertise and visions for: (1) evolving HCI design approaches to move beyond the individual; (2) re-engaging with institutions in order to repoliticise HCI practices, projects and methods; and (3) counteracting depoliticisation in large parts of the design field. We are specifically interested in ways of 'designing smart' by engaging communities throughout the process of addressing complex challenges, such as social inequality, economic disparity and environmental degradation.
Group-based Adaptive Differential Evolution For Chance Constrained Portfolio Optimization Using Bank Deposit and Bank Loan

Portfolio optimization using bank deposit and loan is formulated as a chance constrained problem in which a non-risk asset called bank deposit is included in the portfolio and the borrowing money called bank loan can be invested in risk assets. The chance constrained problem is transformed into an equivalence problem. Then an Adaptive Differential Evolution using Directed mutation (ADED) is proposed and applied to the equivalence problem. Furthermore, in order to cope with large scale instances, ADED is extended to group-based ADED called ADED4G. ADED4G divides the feasible region of the chance constrained problem into four areas and executes ADED in each of the areas simultaneously. Experimental results show that both ADED and ADED4G are excellent. Besides, the practical use of bank deposit and bank loan improves the efficient frontier.
Vibration signal denoising using partial differential equations of arbitrary order

In this paper, partial differential equations (PDE) denoising characteristics are investigated and a unified vibration denoising model is proposed based on arbitrary order PDE. The numerical solution for PDE filters with arbitrary order is demonstrated. The noise reduction features of the proposed model are analysed on Gaussian white noise, Pearson noise and Weibull noise. The realization of the PDE filter generally include the following steps: (1) Obtain the discretization equation according to the given order using the backward Euler scheme; (2) Compute the grid ratio according to the given order, the cut-off frequency and the sample frequency; (3) acquire the filter matrix. Numerical simulations are conducted and the results indicate that the proposed method exceeds relevant works in noise reduction and time-consuming performances. A shaft centreline orbit experiment has also been conducted to verify the efficacy of the proposed method in field application.