Reviewerly/exHarmony huggingface dataset:
Authentication of Printed Document Using Quick Response (QR) Code

Implementation of information systems in the public service has an impact on increasing the use of digital documents. Digital documents replace function of printed documents because they are legally recognised. In reality, printed documents are still considered relevant because it is still considered necessary as legitimate evidence; one example is the permit document. Permit documents are essential because they are needed as administrative requirements. The importance of permit documents has resulted in many falsifications of these documents. Therefore guaranteeing the authenticity of these documents is needed. The use of Quick Response (QR) Code is present as a guarantor of authenticity document because it is considered capable of being an effortless way to validate the authenticity of printed documents. Authentication of QR codes is difficult to determine by using the human senses because they cannot know the original and fake codes. This inability causes QR Code to be vulnerable to counterfeiting. To guarantee authenticity, a unique QR code that is easily validated is needed. This study creates a unique QR Code by utilising AES and SHA-256 that are difficult to produce by other people so that the authenticity of information about document content can be guaranteed and easily validated.
Capabilities of Mixed Reality Applications for Architecture and Construction: A Comparative Review with HoloLens

With the ability of mixed reality (MR) in overlaying digital information in the real world, architecture and construction practitioners have quickly recognized its value in on-site visualization for planning and verification. Although a few contractors have piloted some initial tests, no thorough reviews on its uses have been reported in existing literature. To bridge this gap, a comparative review of the capabilities of MR applications for architecture and construction was performed. Nine MR applications were identified, and their capabilities of on-site uses were evaluated and compared. It was found that although some applications performed better than others, no single application offered all desired features. In addition, the limitations of each application and the requirements of on-site uses were discussed. This paper provides timely and first-hand information to help the vast architecture and construction practitioners and researchers better understand the current capabilities of the MR technology in on-site construction visualization.
Logical-linguistic model for multilingual Open Information Extraction

Open Information Extraction (OIE) is a modern strategy to extract the triplet of facts from Web-document collections. However, most part of the current OIE approaches is based on NLP techniques such as POS tagging and dependency parsing, which tools are accessible not to all languages. In this paper, we suggest the logical-linguistic model, which basic mathematical means are logical-algebraic equations of finite predicates algebra. These equations allow expressing a semantic role of the participant of a triplet of the fact (Subject-Predicate-Object) due to the relations of grammatical characteristics of words in the sentence. We propose the model that extracts the unlimited domain-independent number of facts from sentences of different languages. The use of our model allows extracting the facts from unstructured texts without requiring a pre-specified vocabulary, by identifying relations in phrases and associated arguments in arbitrary sentences of English, Kazakh, and Russian languages. We evaluate our approach on corpora of three languages based on English and Kazakh bilingual news websites. We achieve the precision of facts extraction over 87% for English corpus, over 82% for Russian corpus and 71% for Kazakh corpus.
Bio-Mechanical Distracted Driver Recognition Based on Stacked Autoencoder and Convolutional Neural Network

In this paper, we consider a problem of a biomechanical distraction of a driver; mostly it is related with hands secondary action while driving. Dealing with this type of distraction is crucial because the main causes of car accidents are due to negligent operation of in-vehicle technology, operating mobile phone while driving, and chatting with passenger. The effects of illumination conditions and driver's hands skin color make biomechanical distraction recognition very challenging. Accordingly, we propose a deep recognition model which can handle both effects, and its computational cost is relatively less when compared with the current state of the art. Our model is a sequential integration of two sub module: hand and face localizer and different types of biomechanical distraction recognizer. We conduct several simulations to investigate the effect of hand and face localization, and to measure the recognition performance of the proposed model, and its recognition performance, based on our experiment, our model achieves 98.68% validation accuracy which is better than the current state of the art.
NRQQA

Image stitching technology has been widely used in immersive applications, such as 3D modeling, VR and AR. The quality of stitching results is crucial. At present, the objective quality assessment methods of stitched images are mainly based on the availability of ground truth (i.e., Full-Reference). However, in most cases, ground truth is unavailable. In this paper, a no-reference quality assessment metric specifically designed for stitched images is proposed. We first find out the corresponding parts of source images in the stitched image. Then, the isolated points and the outer points generated by spherical projection are eliminated. After that, we take advantage of the bounding rectangle of stitching seams to locate the position of overlapping regions in the stitched image. Finally, the assessment of overlapping regions is taken as the final scoring result. Extensive experiments have shown that our scores are consistent with human vision. Even for the nuances that cannot be distinguished by human eyes, our proposed metric is also effective.
On regularization properties of artificial datasets for deep learning

The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting noise to high-level features, bears several similarities to existing regularization methods for deep neural networks. One can treat this property of artificial data as a kind of “deep” regularization. It is thus possible to regularize hidden layers of the network by generating the training data in a certain way.
Estimation of higher order statistical parameters for image enhancement on pure and nano-dispersed dodecyloxyBenzoic acid

A new, fast image-enhancement algorithm, for optical microscopy of liquid crystalline systems, is proposed. This is based on higher order statistical parameters in conjunction with a discrete wavelet transform analysis and uses a MATLAB tool. The enhanced images are compared with those produced by currently used global histogram equalisation algorithms – and appear to be superior. The system chosen to evaluate this new algorithm was a two-component system with a low concentration of ZnO nanoparticles dispersed in the liquid-crystalline phase of p-n-dodecyloxy benzoic acid.
“it's a generally exhausting field” A Large-Scale Study of Security Incident Management Workflows and Pain Points

Security incident management is of increasing importance given the growth in cybersecurity threats and regulatory attention. We provide what is, to the best of our knowledge, the first large-scale study of security incident management, consisting of a survey with more than 500 security incident management practitioners grounded by interviews with practitioners in 10 organizations, including companies and educational institutions. We find that some aspects of incident management workftows are consistent across organizations and incident management teams (e.g., collaboration and prioritization), but that the complexity of incident management generally grows with organization size, in that practitioners encounter a wider variety and volume of incidents, collaboration partners and tools. These variations suggest several ways in which tools can better support efficient incident management.
BwShare: Efficient bandwidth guarantee in cloud with transparent share adaptation

Bandwidth guarantee (BwG) is a highly desired feature in cloud data centers for enabling tenants (i.e., users) to achieve predictable performances. However, such a function currently is not commonly available in clouds since it potentially lowers the utilization efficiency of the network fabric (i.e., denoted network efficiency). The reasons lie in two aspects. First, tenants often present a time-varying and/or spatially-varying bandwidth demands. Second, the current cloud network architecture makes it hard to enable tenants to reuse unused bandwidth guarantees efficiently. To tackle these challenges, we propose BwShare, a novel bandwidth guarantee scheme in cloud data centers that can effectively improve the network efficiency at both the guaranteed level and the best-effort level in a synergized manner, while current approaches only adopt one of them. Such a design goal is achieved through two components: an adaptive bandwidth guarantee adaptation module (i.e., BGAdaptor) and a transparent work conservation module (i.e., WCEnabler). BGAdaptor improves the network efficiency at the guaranteed level by adjusting bandwidth guarantees among VMs of the same tenant based on traffic monitoring. WCEnabler works at the best-effort level by enabling VMs to reuse idled bandwidth efficiently and freely without disrupting bandwidth guarantees. The two modules can also facilitate each other’s performance. Extensive experiments show that BwShare can effectively improve the network efficiency while providing the function of bandwidth guarantee in cloud data centers.
A Novel Image-based Path Planning Algorithm for Eye-in-Hand Visual Servoing of a Redundant Manipulator in a Human Centered Environment

This paper presents a novel image-based path-planning and execution framework for vision-based control of a robot in a human centered environment. The proposed method involves applying Rapidly-exploring Random Tree (RRT) exploration to perform Image-Based Visual Servoing (IBVS) while satisfying multiple task constraints by exploiting robot redundancy. The methodology incorporates data-set of robot's workspace images for path-planning and design a controller based on visual servoing framework. This method is generic enough to include constraints like Field-of-View (FoV) limits, joint limits, obstacles, various singularities, occlusions etc. in the planning stage itself using task function approach and thereby avoiding them during the execution. The use of path-planning eliminates many of the inherent limitations of IBVS with eye-in-hand configuration and makes the use of visual servoing practical for dynamic and complex environments. Several experiments have been performed on a UR5 robotic manipulator to demonstrate that it is an effective and robust way to guide a robot in such environments.
Implementation of a Strongly Robust Identity-Based Encryption Scheme over Type-3 Pairings

Identity-based encryption (IBE) is a powerful mechanism for maintaining security. However, systems based on IBE are unpopular when compared with those of the public-key encryption (PKE). In our opinion, one of the reasons is a gap between theory and practice. For example, a generic transformation of weakly/strongly robust IBE from any IBE has been proposed by Abdalla et al., no robust IBE scheme is explicitly given. This means that, theoretically, anyone can construct a weakly/strongly robust IBE scheme by employing this transformation. However, this seems not easily applicable to non-cryptographers. In this paper, we first introduce the Gentry IBE scheme constructed over Type-3 pairings by employing the transformation proposed by Abe et al., and second we explicitly give strongly/weakly robust Gentry IBE schemes by employing the Abdalla et al. transformation. Finally, we show its implementation result and show that we can add strong robustness to the Gentry IBE scheme with a very few additional costs. We employ the mcl library to support a Barreto-Naehrig curve defined over the 462-bit prime. The encryption requires about 9 ms, whereas the decryption requires about 12 ms.
Canonicalizing Organization Names for Recruitment Domain

Online recruitment industry relies on various Knowledge Bases (KB) for enabling search and recommendation systems. These KBs comprise of diverse, non-standard, and large volume of named-entities as they are created from vast unstructured user-generated content (mostly CVs). Such non-standard representation of each entity causes significant vocabulary gap in KB which results in redundancy incompleteness, and ambiguity in the retrieved information. The problem is even more challenging in domains where external sources of context do not exist.
Affordable Voice Services to Bridge the Digital Divide: Presenting the Kasadaka Platform

Despite its global reach, the World Wide Web still fails to serve about 3 billion people, the majority living in the Global South, especially in poor, low-resource regions, where broadband internet is not expected to be rolled out in the foreseeable future. Yet, to bring the advantages of ICTs at the reach of communities in low-resource development regions, lightweight, affordable and context-aware ICT solutions are needed, that fit local needs and context. To this end the Kasadaka platform was developed. This platform supports easy creation of local-content and voice-based information services, targeting currently ‘unconnected’ populations, taking into account contextual and infrastructural requirements, and matching local ecosystems. The Kasadaka platform and its Voice Service Development Kit support the development of decentralized voice-based information services, to serve local populations and communities in their own local languages, in regions where Internet and Web are absent and will continue to be for the foreseeable future.
Phrase-guided attention web article recommendation for next clicks and views

As deep learning models are getting popular, upgrading the retrieval-based content recommendation system to the learning-based system is highly demanded. However, efficiency is a critical issue. For article recommendation, an effective neural network which generates a good representation of the article content could prove useful. Hence, we propose PGA-Recommender, a phrase-guided article recommendation model which mimics the process of human behavior - first browsing, then guided by key phrases, and finally aggregating the gleaned information. As this can be performed independently offline, it is thus compatible with current commercial retrieval-based (keyword-based) article recommender systems. A total of six months of real logs - from Apr 2017 to Sep 2017 - were used for experiments. Results show that PGA-Recommender outperforms different state-of-the-art schemes including session-, collaborative filter-, and content-based recommendation models. Moreover, it suggests a diverse mix of articles while maintaining superior performance in terms of both click and view predictions. The results of A/B tests show that simply using the backward version of PGA-Recommender yields 40% greater click-through rates as compared to the retrieval-based system when deployed to a language of which we have zero knowledge.
Enhancing Privacy and Security in Medical Information with AES and DES

Faster developments in handheld devices, internet connectivity and easy and fast access to information has always been helpful to human being all over globe. These developments in technology had opened gates for everyone to enter this information superhighway, however this information superhighway must not have a data which is private to someone, especially the data in hospitals run by government where thousands of patients visit every day. Hospitals have information such as medical prescription and reports such as pathology, x-ray, electrocardiogram, blood sugar, blood pressure, vision correction to name the few. This information needs to be secured and only the intended recipient should get the report, To solve this issue this paper proposes a secure health center management application where the authenticated user will only have access to the information and communication between the entities will be secured by AES and DES algorithm. In this paper an attempt is made to secure communication between entities along with encrypting medical information. Implementation of the proposed system shows that it protects the privacy of individual and for encryption and decryption of medical reports AES shows better performance over DES.
Expanding One-Handed Touch Input Vocabulary Using Index Finger on and Above Back-of-Device

This paper presents an input method to expand one-handed touch input vocabulary for smartphones using the index finger. To this end, our method combines thumb-based touch input and postures or gestures of the index finger on and above back-of-device. These postures and gestures are detected by three-dimensional directions or movements of the index finger. To enable this detection, users wear a ring-like device on the index finger. Moreover, our method includes a one-handed use detection system. Using this system, our method provides common input vocabulary for two-handed use and expands input vocabulary for one-handed use.
Software for the computational experiment “Synthesis of the topological structure of the cognitive model”

Abstract There is a great number of applications used today to support decision-making in socio-economic sphere. Various approaches and tools are used to implement the applications. The article provides the overview of scientific publications developing software algorithms based on cognitive modeling and fuzzy logic. The authors develop the software tool to conduct simulation experiments to synthesize the topological structure of a cognitive model for socio-economic facilities and objects. The results from the simulation experiment aimed to study the structure of the cognitive model taken as an example, are presented as a digraph “Impact of the Media on Mass Audience”. The software uses the programming language Java, JavaFX.
Low-Cost Image Search System on Off-Line Situation

Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images.
An Augmented Reality based Strategy for Base Station Maintenance

In this paper, a skill training strategy for base station maintenance is proposed thus the engineers of telecomm operators can be well self-trained and solve the problems at the first line without the limitations of time, space, manpower, and other expensive equipment. From this way, the basic knowledge of maintenance skill for mobile base station can be accessed immediately in 24x7 hours thus keep the good service quality of mobile communication. This learning strategy fulfills the concept of learning by practice while reducing the related cost and effort significantly. It can be expected that the proposed system will give great benefit and help the first-line engineers to make up the shortage of manpower while provides high availability and reliability of the base stations via the good effect of self problem-solving. Furthermore, it is also easily to be applied to other skill training fields thus is practical for the future education and training purpose.
Mid-air motion planning of robot using heat flow method with state constraints

We address in this paper the problem of planning motion of free floating robot with state constraints. Due to the conservation of angular and linear momentum during the in-air motion, the problem is two-fold: one needs to design, in addition to the in-air motion, the initial impulse that imparts the robot with the appropriate momentum to perform a desired motion. The existence of state constraints such as joint limit makes the planning problem even more challenging. We propose in this paper a new method to address this problem. The method is a homotopy method which deforms an arbitrary path connecting the desired initial and final states to a feasible trajectory between these states, that is a trajectory that meets the system’s dynamics and constraints. From this trajectory, the method extracts both the controls and initial momentum needed to perform the motion. The deformation of path is leaded by geometric heat flow which is defined by a Riemannian metric that encodes the system dynamics and state constraints. We illustrate the method by designing somersault motions for a diver robot.
Efficient Parallel Stochastic Variance Reduction Algorithms for Large-Scale SVD

In recent years, the stochastic variance reduced gradient (SVRG) method has been used for singular vector decomposition (SVD), called VR-SVD, and VR-SVD only uses simple variance reduction iterations, yet can obtain an exponential convergence rate to the optimal solution. Considering that recent years have witnessed great application prospects in large-scale SVD, this paper proposes two efficient asynchronous parallel stochastic variance reduction algorithms. Moreover we analyze their convergence properties in theory, and both dense and sparse asynchronous parallel variants can converge with the rate of O(1/T). Finally, we extend our algorithms for the SVD problems with multiple singular vectors. Our extensive experimental results show that our algorithms attain ideal parallel speedup ratios and achieve the same PSNR values in a shorter time, and thus they can be widely used in practice.
A Movie Trailer Recommendation System Based on Pre-trained Vector of Relationship and Scenario Content Discovered from Plot Summaries and Social Media

Posting articles on the social platform is the favorite activity of young people. With the potential of digital movie and tv series industry, developing an automatic movie recommendation engine becomes a popular issue. Traditionally movie recommendation is based on structured information like director, players, rough class, etc. Recently, there are more and more researches trying to make a recommendation based on context information like music recommendation based on lyrics with the word vector representation. However, in the long text scenario, the recommendation based on all context vector makes the inference very imprecise.In this paper, we propose effective features types, relationships, and scenarios, to extract important information then improve the recommendation. Furthermore, comparing different pre-training model, we try to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between social media articles and movie plot.