Reviewerly/exHarmony huggingface dataset:
Visualization of Virtual House Model on Property Brochure Using Augmented Technology

Brochures are use to help company to promote products or services that they have offer to customers. In the property business, brochures has greatly affects on the level of customers interest in the housing that is offered. Well-made brochures are needed to build interaction with customers. Augmented Reality (AR) is an environment that incorporates 3-dimensional virtual objects into real environments in real-time. Development of this application using Augmented Reality in order to make the housing brochure becomes more real with the existence of 3-dimensional objects. It is aims to show customers a house form that is offered in 3-dimensional. Furthermore, it can increase the interest of customers of the houses that is sold and reduce the cost of making miniature models. Moreove it is runs on android mobile platform and to display augmented objects in 3-dimensional this application use Vuforia SDK.
Representation Learning for Classical Planning from Partially Observed Traces

Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model learning approaches output a domain model in the form of the declarative planning language, such as STRIPS or PDDL, and solve new planning instances by invoking an existing planner. However, planning in such a representation is sensitive to the accuracy of the learned domain model which probably cannot be used to solve real planning problems. In this paper, to represent domain models in a vectorization representation way, we propose a novel framework based on graph neural network (GNN) integrating model-free learning and model-based planning, called LP-GNN. By embedding propositions and actions in a graph, the latent relationship between them is explored to form a domain-specific heuristics. We evaluate our approach on five classical planning domains, comparing with the classical domain-model learner ARMS. The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
Experimental Evaluation of Local Sensitive Hashing Functions for Face Recognition

Since the number of facial images has grown in social networks, such as Facebook and Instagram, face recognition has been recognized as one of the important branches of image processing research area, and large databases of face images have been created. As a result, the response time of the face recognition system is recognized as a challenge. Fortunately, dimension reduction techniques help to reduce the number of computations significantly, which directly effects on system response time. As many facial features do not include important information, which is required for getting a better result from the face recognition systems, these techniques are applicable for facial images, as well. Local Feature Hashing (LFH) is a hash-based algorithm that has been used for face recognition. It has shown notable computational improvements over naive search and can overcome some challenges, including recognition of pose, facial expression, illumination, and partial occlusion parameters. With the aim of improving the time that it takes to run the LFH algorithm, we have tested several versions of Locality-Sensitive Hashing (LSH) algorithm. The results showed that some of these algorithms improve the response time of the LFH algorithm. In comparison with the previously conducted research, the number of input images has been increased in our tests. Besides, the number of extracted key-point vectors have been decreased, and the accuracy has not been decreased. In addition, our algorithm is able to overcome the challenge of the existence of foreign objects, such as glass. Among all different hash versions that for the first time used for face recognition, some of them are not recommended for these systems and several functions can provide minimum response time, rather than previous hash-based algorithms.
Diophantine Sets. Part II

Summary The article is the next in a series aiming to formalize the MDPR-theorem using the Mizar proof assistant [3], [6], [4]. We analyze four equations from the Diophantine standpoint that are crucial in the bounded quantifier theorem, that is used in one of the approaches to solve the problem. Based on our previous work [1], we prove that the value of a given binomial coefficient and factorial can be determined by its arguments in a Diophantine way. Then we prove that two products <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>z</m:mi> <m:mo>=</m:mo> <m:munderover> <m:mo>∏</m:mo> <m:mrow> <m:mi>i</m:mi> <m:mo>=</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mi>x</m:mi> </m:munderover> <m:mrow> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mn>1</m:mn> <m:mo>+</m:mo> <m:mi>i</m:mi> <m:mo>⋅</m:mo> <m:mi>y</m:mi> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> </m:mrow> <m:mi>z</m:mi> <m:mo>=</m:mo> <m:munderover> <m:mo>∏</m:mo> <m:mrow> <m:mi>i</m:mi> <m:mo>=</m:mo> <m:mn>1</m:mn> </m:mrow> <m:mi>x</m:mi> </m:munderover> <m:mrow> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mi>y</m:mi> <m:mo>+</m:mo> <m:mn>1</m:mn> <m:mo>-</m:mo> <m:mi>j</m:mi> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mi> </m:mi> <m:mo>(</m:mo> <m:mn>0.1</m:mn> <m:mo>)</m:mo> </m:mrow> </m:mrow> </m:math> $$z = \prod\limits_{i = 1}^x {\left( {1 + i \cdot y} \right),\,\,\,\,\,\,\,\,} z = \prod\limits_{i = 1}^x {\left( {y + 1 - j} \right),\,\,\,\,\,\,(0.1)} $$ where y &gt; x are Diophantine. The formalization follows [10], Z. Adamowicz, P. Zbierski [2] as well as M. Davis [5].
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection

Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences. They can also be used to generate fake reviews, which can then be used to attack online review systems and influence the buying decisions of online shoppers. To perform such attacks, it is necessary for experts to train a tailored LM for a specific topic. In this work, we show that a low-skilled threat model can be built just by combining publicly available LMs and show that the produced fake reviews can fool both humans and machines. In particular, we use the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96%) to filter out reviews with undesired sentiments. Because none of the words in the review are modified, fluent samples like the training data can be generated from the learned distribution. A subjective evaluation with 80 participants demonstrated that this simple method can produce reviews that are as fluent as those written by people. It also showed that the participants tended to distinguish fake reviews randomly. Three countermeasures, Grover, GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately detect fake review.
Invertible Operators on Banach Spaces

Summary In this article, using the Mizar system [2], [1], we discuss invertible operators on Banach spaces. In the first chapter, we formalized the theorem that denotes any operators that are close enough to an invertible operator are also invertible by using the property of Neumann series. In the second chapter, we formalized the continuity of an isomorphism that maps an invertible operator on Banach spaces to its inverse. These results are used in the proof of the implicit function theorem. We referred to [3], [10], [6], [7] in this formalization.
DOM-Q-NET: Grounded RL on Structured Language

Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states. In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems. It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input. Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page. We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations. Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks.
From relational to technological trust: How do the Internet of Things and Blockchain technology fit in?

The construction industry is complex, fragmented and struggles to cope with an intricate and geographically diverse Supply Chain (SC) where sophisticated flows of materials, information and capital take place in a dynamic environment.In this context, building trust among partners is a necessary requirement to deliver a successful project on time and on budget.This paper investigates the main factors that build inter-firm trust in large construction projects and explores the disruptive effect that the Internet of Things (IoT) and Blockchain (BC) could have in this process.The findings are based on an in-depth analysis of the literature and ten one-to-one telephone and face-toface interviews.The findings contribute to the literature by suggesting that a Blockchain-enabled IoT ecosystem has the potential to generate inter-firm trust at the front-end, during contract development, and throughout the construction phase.Moreover, the data denotes the need for further technological adoption in the construction industry as a means of smoothing the transition from relational trust to technological trust.
FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy

In this paper, a 3D patch-based fully dense and fully convolutional network (FD-FCN) is proposed for fast and accurate segmentation of subcortical structures in T1-weighted magnetic resonance images. Developed from the seminal FCN with an end-to-end learning-based approach and constructed by newly designed dense blocks including a dense fully-connected layer, the proposed FD-FCN is different from other FCN-based methods and leads to an outperformance in the perspective of both efficiency and accuracy. Compared with the U-shaped architecture, FD-FCN discards the upsampling path for model fitness. To alleviate the problem of parameter explosion, the inputs of dense blocks are no longer directly passed to subsequent layers. This architecture of FD-FCN brings a great reduction on both memory and time consumption in training process. Although FD-FCN is slimmed down, in model competence it gains better capability of dense inference than other conventional networks. This benefits from the construction of network architecture and the incorporation of redesigned dense blocks. The multi-scale FD-FCN models both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. In addition, dense blocks are rebuilt to enlarge the receptive fields without significantly increasing parameters, and spectral coordinates are exploited for spatial context of the original input patch. The experiments were performed over the IBSR dataset, and FD-FCN produced an accurate segmentation result of overall Dice overlap value of 89.81% for 11 brain structures in 53 seconds, with at least 3.66% absolute improvement of dice accuracy than state-of-the-art 3D FCN-based methods.
Techniques for Automated Machine Learning

Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
Enhancing Goldreich, Goldwasser and Halevi’s scheme with intersecting lattices

Abstract We present a technique to enhance the security of the Goldreich, Goldwasser and Halevi (GGH) scheme. The security of GGH has practically been broken by lattice reduction techniques. Those attacks are successful due to the structure of the basis used in the secret key. In this work, we aim to present a new technique to alleviate this problem by modifying the public key which hides the structure of the corresponding private key. We intersect the initial lattice with a random one while keeping the initial lattice as our secret key and use the corresponding result of the intersection as the public key. We show sufficient evidence that this technique will make GGH implementations secure against the aforementioned attacks.
A geochemical appraisal of some marble physiques in Ubo area and environs, southwestern Nigeria

Twenty (20) marble samples from Ubo and environs were analyzed both geochemically and statistically with the aim of determining their industrial applicability.The samples were subjected to AAS analysis.Findings revealed a fairly high -very high values of and low values of MgO (1.64wt%-6.95wt%).Other major oxide such as SiO2, FeO/Fe2O3, Na2O, K2O, Al2O3 and MnO2 had values below 2wt%.The multivariate analysis employed revealed a high correlation between CaO/Fe2O3, MgO/Al2O3 and FeO/CaO which is an indication of their fairly similar valencies and ionic values.The high lime content is connected to the shallow marine environment during deposition prior to metamorphism.The marbles are classified as the high calcite group and their industrial use range from the production of Portland cement to their use for ceramics.
Delivering emoji/icon-based universal health education messages through smartphones

Citation: Sudip Bhattacharya, Amarjeet Singh, Roy Rillera Marzo. Delivering emoji/icon-based universal health education messages through smartphones[J]. AIMS Public Health, 2019, 6(3): 242-247. doi: 10.3934/publichealth.2019.3.242
Progressive Perception-Oriented Network for Single Image Super-Resolution

Recently, it has been demonstrated that deep neural networks can significantly improve the performance of single image super-resolution (SISR). Numerous studies have concentrated on raising the quantitative quality of super-resolved (SR) images. However, these methods that target PSNR maximization usually produce blurred images at large upscaling factor. The introduction of generative adversarial networks (GANs) can mitigate this issue and show impressive results with synthetic high-frequency textures. Nevertheless, these GAN-based approaches always have a tendency to add fake textures and even artifacts to make the SR image of visually higher-resolution. In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network. Specifically, the first phase concentrates on minimizing pixel-wise error, and the second stage utilizes the features extracted by the previous stage to pursue results with better structural retention. The final stage employs fine structure features distilled by the second phase to produce more realistic results. In this way, we can maintain the pixel, and structural level information in the perceptual image as much as possible. It is useful to note that the proposed method can build three types of images in a feed-forward process. Also, we explore a new generator that adopts multi-scale hierarchical features fusion. Extensive experiments on benchmark datasets show that our approach is superior to the state-of-the-art methods. Code is available at https://github.com/Zheng222/PPON.
Tag Clouds for Object-Oriented Source Code Visualization

Software visualization helps software engineers to understand and manage the size and complexity of the object-oriented source code. The tag cloud is a simple and popular visualization technique. The main idea of the tag cloud is to represent tags according to their frequency in an alphabetical order where the most important tags are highlighted via a suitable font size. This paper proposes an original approach to visualize software code using a tag cloud. The approach exploits all software identifier names to visualize software code as a tag cloud. Experiments were conducted on several case studies. To validate the approach, it is applied on NanoXML and ArgoUML. The results of this evaluation validate the relevance and the performance of the proposed approach as all tag names and their frequencies were correctly identiﬁed. The proposed tag cloud visualization technique is a helpful addition to the software visualization toolkit. The extracted tag cloud supports software engineers as they filter and browse data.
Steady-state population inversion of multiple <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Ξ</mml:mi></mml:math> -type atoms by the squeezed vacuum in a waveguide

We study the dynamics of multiple $\mathrm{\ensuremath{\Xi}}$-type atoms driven by a squeezed vacuum reservoir in a quasi-one-dimensional waveguide. We show that the atomic system's steady state is a pure state, and a complete population inversion can occur when the dipole moment of the second transition is almost perpendicular to the polarization of the incident squeezed light. We also prove that the steady state of the system is the direct product of that in the single-atom case with modified squeezed vacuum even when dipole-dipole interaction is involved. This steady-state population inversion may be used to study the two-photon laser or collective atomic effect.
SARPS: Sentiment Analysis of Review(S) Posted on Social Network

Today’s environment is witnessing a change in the shopping scenario as most of the urban user of all the age group prefers to buy and sell the product online. The companies allow the buyer to give feedback and reviews related to the product and with this user can easily share their views about any product, brand, services, hotel etc. Before making the purchase the customer nowadays always go-through the reviews which are present on the portal or vortal because it gives them the idea about the quality and service given by the company related to that product or brand. Not always the reviews are genuine but fake reviews are also available to promote and de-promote the product and one cannot easily distinguish which one is real and which one is fake. The positive reviews build a good impact on the user for buying the product whereas the negative reviews always restrict the user from buying a particular product. The proposed work classifies the reviews on the basis of rating ranging from 1 to 5. The review whose rating is greater than 3.5 is considered as the positive reviews and whose rating is less than 3.5 is considered as the negative reviews. The simulation of proposed work has been done in jupyter notebook and the results are encouraging.
Personal Identification with Any Shift: Authentication method for smartwatches having shoulder-surfing resistance

Recently, mobile terminals having small touchscreen such as smartwatches are increasing. PIN or pattern lock are used for personal authentication for smartwatches, but it is possible to leak authentication information by peeking the screen because the information is displayed directly. Therefore, in this paper, we propose Personal Identification with Any Shift (PIAS), which user taps different icon from registered icon on 3 × 3 icons. In addition, we implemented PIAS and evaluated it by confirming the usability and shoulder-surfing resistance. As a result, authentication time and success rate was 13.8 s and 89.4%, and the rate of successful shoulder-surfing attack was 0.0%.
Prototype Software Monitoring Sarana dan Prasarana Perguruan Tinggi

This study aims to facilitate the management system of monitoring infrastructure program of university facilities and infrastructure, through software engineering technology approach as an effort to improve productivity and quality of monitoring process become more efficient and effective. The software in this research is built in a systematic and organized approach to monitoring infrastructure of facilities and infrastructure using appropriate tools and techniques. Through this research, universities are expected to be able to develop the necessary quality measures to support the process of planning and controlling infrastructure infrastructure monitoring. The research was conducted using survey method, development of monitoring management and software. Up to the design stage of this program prototype, research has produced a special picture of the software requirements to be built in the next year. Software development process starting from the analysis phase of system and software requirements, designing data structures up to the architecture stage of the program has produced a list of needs/requirements, the design of program prototype contained in the design of input/output for the monitoring process facilities and infrastructure.
LightGuider: Guiding Interactive Lighting Design using Suggestions, Provenance, and Quality Visualization

LightGuider is a novel guidance-based approach to interactive lighting design, which typically consists of interleaved 3D modeling operations and light transport simulations. Rather than having designers use a trial-and-error approach to match their illumination constraints and aesthetic goals, LightGuider supports the process by simulating potential next modeling steps that can deliver the most significant improvements. LightGuider takes predefined quality criteria and the current focus of the designer into account to visualize suggestions for lighting-design improvements via a specialized provenance tree. This provenance tree integrates snapshot visualizations of how well a design meets the given quality criteria weighted by the designer's preferences. This integration facilitates the analysis of quality improvements over the course of a modeling workflow as well as the comparison of alternative design solutions. We evaluate our approach with three lighting designers to illustrate its usefulness.