Reviewerly/exHarmony huggingface dataset:
Disentangled Feature Networks for Facial Portrait and Caricature Generation

Facial portrait is an artistic form which draws faces by emphasizing discriminative or prominent parts of faces via various kinds of drawing tools. However, the complex interplay between the different facial factors, such as facial parts, background, and drawing styles, and the significant domain gap between natural facial images and their portrait counterparts makes the task challenging. In this paper, a flexible four-stream Disentangled Feature Networks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DFN</i> ) is proposed to learn disentangled feature representation of different facial factors and generate plausible portraits with reasonable exaggerations and richness in style. Four factors are encoded as embedding features, and combined to reconstruct facial portraits. Meanwhile, to make the process fully automatic (without manually specifying either portrait style or exaggerating form), we propose a new Adversarial Portrait Mapping Module ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APMM</i> ) to map noise to the embedding feature space, as proxies for portrait style and exaggerating. Thanks to the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DFN</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APMM</i> , we are able to manipulate the portrait style and facial geometric structures to generate a large number of portraits. Extensive experiments on two public datasets show that our proposed methods can generate a diverse set of artistic portraits.
An Interactive Multi-Objective Ontology Matching Technique

Due to the heterogeneity problem between ontologies caused by the subjectivity of ontology builders, implementing the communication between ontologies in the same domain is hindered. Currently, ontology matching has been regarded as an effective method to solve this problem. As the problem of ontology matching is nonlinear, it is not easy to deal with it. However, matching two ontologies under alignment is a non-trivial task. In this paper, the approximate evaluation functions are proposed to avoid the requirement of external reference alignment that should be provided by the experts beforehand. To improve the quality of the obtained alignment, Interactive Multi-Objective Ontology Matching technique (IMOOM) is proposed. In particular, our proposal is able to screen out three representative solutions for users. Finally, the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) is used to verify the method proposed in this paper, and the experiment results show the effectiveness of our approach.
Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes

Demographic events shape a population's genetic diversity, a process described by the coalescent-with-recombination model that relates demography and genetics by an unobserved sequence of genealogies along the genome. As the space of genealogies over genomes is large and complex, inference under this model is challenging. Formulating the coalescent-with-recombination model as a continuous-time and -space Markov jump process, we develop a particle filter for such processes, and use waypoints that under appropriate conditions allow the problem to be reduced to the discrete-time case. To improve inference, we generalise the Auxiliary Particle Filter for discrete-time models, and use Variational Bayes to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc.
Food and beverage industry supply chains

This chapter promotes an understanding of blockchain’s potential to address various challenges in the food and beverage industry supply chains. We provide details of IBM’s blockchain-based Food Trust, which is a widely used platform for food supply chains. It discusses cases of blockchain deployment in domestic and cross-border food supply chains. It also highlights the limitations to using blockchain systems in food and beverage supply chains in developing countries. It also gives an overview of the open blockchain platform OpenSC, which is being used to track food products by diverse companies such as the Swiss multinational food and beverage company Nestle and Australia’s integrated commercial fishing company Austral Fisheries.
Applications of Reinforcement Learning to Image Enhancement: A Survey

In recent years, we have seen rapid development in Reinforcement Learning. Reinforcement learning, combined with deep learning has proven to be a powerful tool that has been able to make substantial breakthroughs in many complex decision-making systems. Popular examples include Google&#x2019;s AlphaGo and its successor AlphaZero. In this paper, we take a look at some reinforcement learning techniques and major advancements made in the application of reinforcement learning to image enhancement tasks such as denoising, color enhancement and image restoration. We also present comparisons between existing deep learning techniques and reinforcement learning.
NeuralPlan: Neural floorplan radiance fields for accelerated view synthesis

We propose an approach for quickly building a visual representation of a full indoor building. Our goal is to enable intelligent systems which frequently and regularly monitor buildings to assist personnel operating remotely, a need of special importance in these days. Prior work in neural scene representations for view synthesis focuses on single objects and small scenes and does not scale to full buildings in short timeframes. We propose introducing the floorplan and learning a neural floorplan radiance field, mapping floorplan 3D points and view directions to emitted radiance, and rendering via a sinusoidal multi-layer perceptron (MLP) neural renderer. To incorporate local priors and further accelerate the overall learning, we use a hypernetwork which maps a floorplan surface normal to the parameters of the neural renderer, thus defining the scene by a space of local neural rendering functions across the building. This allows shared knowledge, reasoned in function space, of performing the neural rendering from various vantage points in the scene based on similar building structure represented in the floorplan surface normal, and facilitates meta-knowledge pre-training across multiple buildings. The meta-knowledge is used to initialize the parameters of the hypernetwork at test time for the target building. Our approach performs significantly accelerated learning of neural floorplan radiance fields in around 15 min for full buildings on a single commodity GPU, and renders in real-time at 64 Hz, allowing for immersive visual experiences.
A Rate Scalable Construction of Non-Homogeneous Quantum LDPC Codes of CSS Type Based on Balanced Incomplete Block Design

Balanced incomplete block design (BIBD) offers an important way to construct non-homogeneous quantum low-density-parity-check (LDPC) codes of Calderbank-Shor-Steane (CSS) type. However, the rates of the quantum codes based on this method are limited to be 1-2/m approximately, where m, the number of base blocks chosen from the associated BIBD, is even. We overcome this limitation by providing a rate scalable way to construct non-homogeneous quantum LDPC codes of CSS type. With our method, the rates of the resultant quantum codes are 1-2/m', where m'=m/e and e ∈ {1, 2}. Note that here m' can be odd. Thus, we double the rate range of the codes yielded from the existing strategies. Moreover, one can switch codes between the two rates flexibly adapting to various application scenarios.
ERP and Artificial Intelligence based Smart Financial Information System Data Analysis Framework

ERP and artificial intelligence-based modern smart financial information system data analysis framework is designed in this study. The ERP management system is a core modern business management model based on information technology. The proposed work focused on the following innovations. (1) Agents can communicate with each other, work together and collaborate to complete tasks through a certain communication language. This is used to construct the data analytic model. (2) The information that needs to be integrated between PDM and ERP systems includes the product information, process information, and resource information. This is to construct the novel details. The experiment has proven the performance.
Application of blockchain technologies and game approach in the educational process of universities

This article deals with the use of Blockchain technology and game approach in the context of the educational process in high education institutions. Application of new information technologies and the educational sector general digital transformation in conjunction with Blockchain technology and game mechanics allow, on the one hand, to increase information systems reliability and, on the other hand, to increase the efficiency of learning the information by students. Based on the materials described, the model is developed that can be used for own educational software products elaboration.
Time-frequency analysis associated with the Laguerre wavelet transform

We define the localization operators associated with Laguerre wavelet transforms.Next, we prove the boundedness and compactness of these operators, which depend on a symbol and two admissible wavelets on L p α (K), 1 ≤ p ≤ ∞.
A Compound Path Planning Algorithm for Mobile Robots

In this paper, we designed a compound algorithm which combined the advantages of Dijkstra and ant colony optimization to complete the robot path planning. The compound algorithm is one that uses the Dijkstra algorithm for initial path planning under a viewable environment model, and then optimizes the initial path with an improved ACO. Pointing at the problem that the ACO is slow to converge and easy to fall into the local optimal solution, the performance of ACO is improved by constructing a new heuristic function and improving the pheromone update principle. Through the simulation on MATLAB, the designed algorithm shows higher path search efficiency and path optimization rate than the traditional algorithms.
Towards Visual Dialogue for Human-Robot Interaction

The goal of the EU H2020-ICT funded SPRING project is to develop a socially pertinent robot to carry out tasks in a gerontological healthcare unit. In this context, being able to perceive its environment and have coherent and relevant conversations about the surrounding world is critical. In this paper, we describe current progress towards developing the necessary integrated visual and conversational capabilities for a robot to operate in such environments. Concretely, we introduce an architecture for conversing about objects and other entities present in an environment. The work described in this paper has applications that extend well beyond healthcare and can be used on any robot that requires to interact with its visual and spatial environment in order to be able to perform its duties.
Survey on Detection of Credit Card Frauds using Hmm and various Clustering Approaches

This paper focuses on detecting the credit card frauds using hmm and various clustering. This research has been worked in such a way that the state of transaction is to be determined based upon on the past transactions and the amount he used in those transactions. This prediction is done by using HMM (Hidden Markov Model). This model takes behavior pattern of the card holder and then the model is trained so that it can be used for prediction. The incoming transaction will be accepted only when it satisfies the probability. Firstly, it has been required to work on the historical transactions and try to analyze the hidden pattern of the cardholder profile. Secondly, using many clustering algorithms and the characteristic pattern of all the fraud and normal samples are required to be classified into clusters that share the common characteristics. Finally, this model helps us to decides the state of the present transaction based upon the historical transaction using Hmm to predict the probability whether a transaction is genuine or fraud transaction. This experiment helps us to know which clustering algorithm in integration with Hmm helps us to predict the transaction state with a good accuracy score such that a fraud transaction will never be accepted. And so genuine transactions will never be rejected in a bunch of transaction for being a fraud one.
Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles

To tackle problems arising from unexpected camera motions in unmanned aerial vehicles (UAVs), we propose a three-mode ensemble tracker where each mode specializes in distinctive situations. The proposed ensemble tracker is composed of appearance-based tracking mode, homography-based tracking mode, and momentum-based tracking mode. The appearance-based tracking mode tracks a moving object well when the UAV is nearly stopped, whereas the homography-based tracking mode shows good tracking performance under smooth UAV or object motion. The momentum-based tracking mode copes with large or abrupt motion of either the UAV or the object. We evaluate the proposed tracking scheme on a widely-used UAV123 benchmark dataset. The proposed motion-aware ensemble shows a 5.3% improvement in average precision compared to the baseline correlation filter tracker, which effectively employs deep features while achieving a tracking speed of at least 80fps in our experimental settings. In addition, the proposed method outperforms existing real-time correlation filter trackers.
Multimodal semantic analysis with regularized semantic autoencoder

The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query.
Rule Based Approach to Extract Metadata from Scientific PDF Documents

The number of scientific PDF documents is increasing at a very rapid pace. The searching for these documents is becoming a time consuming task, due to the large number of PDF documents. To make the search and storage more efficient, we need a mechanism to extract metadata from these documents and store this metadata according to their semantics. Extracting information from metadata and storing that information is very time consuming task and requires lots of human effort if performed manually due to large numbers of documents and their varying formats. In this paper, we present a rule-based approach to extract metadata information from the research articles. This approach was developed and evaluated on a diverse data-set provided by ESWC (2016) having a number of different formats and features. Evaluation results show that our proposed approach performs 22% better than CERMINE and 9% better than GROBID.
Student’s science literacy about Covid-19

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation A. M. Setiawan, Isnanik Juni Fitriyah; Student's science literacy about Covid-19. AIP Conf. Proc. 2 March 2021; 2330 (1): 060007. https://doi.org/10.1063/5.0043200 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
MSIT: A Modified Lightweight Algorithm for Secure Internet of Things

Internet of Things (IoT) has become an interesting topic of the research community, a way of doing the last years. Traditional encryption algorithms are computationally expensive on account of their complexity and require number of rounds for encryption. Device communication in IoT required algorithms with less complexity for secure communication. In this paper, we address the problem of security in IoT using a modified lightweight encryption and decryption algorithm called Modified Secure IoT (MSIT) for Secure data communication. A block cipher of 64 bits that accepts key of 64-bits and uses +2 function for encryption and decryption. Here, +2 appear as the binary addition which avoid the carry which is reproduced by the most significant bit addition. The proposed work has been evaluated in terms of entropy, correlation, image histogram and differential analysis. We also demonstrates the merits of the proposed formulation using computational cost and total computational time.
WITHDRAWN: Security protocol of sensitive high utility itemset hiding in shared IoT environments

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
Challenges and directions for Blockchain technology applied to Demand Response and Vehicle-to-Grid scenarios

Nowadays, Blockchain is considered a consolidated technology supporting many different applications that integrate well with Artificial Intelligence and the Internet of Things, and supports the creation of Decentralized Autonomous Organizations. Within this wider framework, the energy blockchain applications are now deserving more and more attention, since blockchain architectures, on one hand, provide transparency and solve the information asymmetry problem, on the other, provide disintermediation. In this way, the dream of an energy market closer to end-users becomes a reality, although, still, the regulatory framework is not clear, especially for what concerns the tokenization of assets related to investments in energy projects and energy services. The role of the blockchain technology in the energy sector has several facets: fostering Demand Response and the aggregation of loads and generators, helping the creation of energy communities and the integration of electric vehicles in the power grid, providing a more transparent and traceable relation between service providers and customers. This chapter describes current challenges and gives possible directions for the research in the field. This is done considering the possible evolutions of the regulatory framework and thus the possible implications in terms of business models. Finally, the last section of the chapter describes a possible laboratory infrastructure to test energy blockchain business models under the point of view of service providers such as grid and telecom operators, as well as the customer’s perspective.
Detecting Duplicate Question Pairs Using GloVe Embeddings and Similarity Measures

More than 100 million individuals visit Quora, Stack Overflow, Reddit and many other Web sites which provide answers to the questions posted by users consistently. So it is nothing unexpected that numerous individuals pose similar worded enquiries. Different enquiries with a similar expectation can make searchers invest more energy in finding the best solution to their inquiry and make authors feel they have to answer various renditions of a similar inquiry. Having canonical questions on these Web sites would provide a better experience to searchers and writers and offer more value to both of them. Inspired by this, the main aim of our project is that given a pair of questions identifying if they are similar or not. This could be useful to instantly provide answers to questions that have already been answered.
Innovative, Integrated and Interactive (3I) LMS for Learners and Trainers

3I-LMS is meant to conquer the insurmountable restraints to class/lecture room education. What are insurmountable restraints to physical classroom education and how does 3I-LMS conquer them. Firstly, the lockdown and physical distancing warranted by the spread of Covid-19. In order to overcome the restraints of the pandemic 3I-LMS would be the obvious answer. 3I-LMS, the medium for delivery and reception, is online for both learners and trainers. Secondly, ineffectiveness resulting in frail in pass rates, fast diminishing memory, faulty study techniques are overcome. 3I-LMS makes studying satisfied, gamified, engaged and enjoying. The outcome would be lasting memory, effective study techniques, higher recall rates leading to improved pass rates. Thirdly, inefficiencies such as slow access to relevant lessons, notes, slow answer evaluation, feedback, clarification to routine questions are also resolved by 3I-LMS. 3I-LMS provides for keyword search, relevant subject wise notes, instant answers for routine questions thus contributing to improved efficiencies. Additionally, 3I-LMS has unique and innovative features to assist assignment completion, emanate milestone alerts, monitoring emotions and time utilization. 3I-LMS provides for utmost security. Thus, the solution deploys face recognition and keystroke dynamics to combat impersonation, copying and unauthorized referencing.
<i>Ambiguous Devices</i>: Improvisation, agency, touch and feedthrough in distributed music performance

This article documents the processes behind our distributed musical instrument, Ambiguous Devices . The project is motivated by our mutual desire to explore disruptive forms of networked musical interactions in an attempt to challenge and extend our practices as improvisers and instrument makers. We begin by describing the early design stage of our performance ecosystem, followed by a technical description of how the system functions with examples from our public performances and installations. We then situate our work within a genealogy of human–machine improvisation, while highlighting specific values that continue to motivate our artistic approach. These practical accounts inform our discussion of tactility, proximity, effort, friction and other attributes that have shaped our strategies for designing musical interactions. The positive role of ambiguity is elaborated in relation to distributed agency. Finally, we employ the concept of ‘feedthrough’ as a way of understanding the co-constitutive behaviour of communication networks, assemblages and performers.