Reviewerly/exHarmony huggingface dataset:
ARTOUR: Augmented Reality for Tourism - A Case Study in Riobamba, Ecuador

Augmented reality (AR) is a state-of-the-art technology that integrates computer-generated virtual information into the real world and changes the way people interact and experience their surrounding environment. AR technology brings the digital world, whether it is an object in two-dimensions (2D) or three-dimensions (3D), seamlessly into the physical world. It is establishing itself evermore as a fundamental tool in the modern world, not only as a recreational tool but also in the educational, medical and tourism sectors.AR in tourism can help visitors/tourists to explore places and tourist attractions in real terms, in order to make the most of their experience. In this investigation, we propose an interesting way to guide tourists in the city of Riobamba in a modern way, through an application called AR-TOUR, based on the Augmented Reality technology. AR-TOUR is a mobile service platform that will show the main tourist attractions in the city of Riobamba such as churches and parks in 3D, which will increase the interest of tourists in visiting places and the ease of navigating through the city. The ARTOUR application additionally offers options for the selection menu, multilingual functionality, ease of use and the ability to customize the application, which are among the main requirements that are taken into account to attract tourists. The limitations and recommendations of the study are mentioned that will pave the way for future research. The ARTOUR application was developed in Unity 3D using the Vuforia and Flutter augmented reality software for the menu option.
A new approach hybrid recommender system of item bundles for group of users

User recommendation is a big challenge for collaborative filtering, due to the large number of users a company can have today. In addition, the data to recommend products, entertainment programs, among others, have to be very dispersed, there are not many users sent a vote towards a product, generating a high spread to make a recommendation. To solve this problem, we propose a hybrid system, which uses collaborative filtering and content-based filtering techniques where we use the user’s personal information (in this case the occupation or work) to segment them into groups and then use the rating of the items to be able to make predictions. We then make recommendations using aggregation techniques such as average, maximum and minimum, which will be evaluated with metrics such as RMSE and MAE in order to obtain the best recommendation approach. To carry out this hybrid recommendation system, we have used a public database called Movielens. The research aims to leave a baseline for future research so that the proposed system can be improved with different methods.
Decoupling cross-quadrature correlations using passive operations

Quadrature correlations between subsystems of a Gaussian quantum state are fully characterised by its covariance matrix. For example, the covariance matrix determines the amount of entanglement or decoherence of the state. Here, we establish when it is possible to remove correlations between conjugate quadratures using only passive operations. Such correlations are usually undesired and arise due to experimental cross-quadrature contamination. Using the Autonne--Takagi factorisation, we present necessary and sufficient conditions to determine when such removal is possible. Our proof is constructive, and whenever it is possible we obtain an explicit expression for the required passive operation.
Post-quantum Commutative Deniable Encryption Algorithm

There is proposed a new post-quantum commutative encryption algorithm based on the hidden discrete logarithm problem. The introduced cipher is suitable for implementing post-quantum pseudo-probabilistic deniable encryption protocol. The proposed commutative cipher belongs to the class of the algebraic ciphers. Its algebraic support represents a finite noncommutative associative algebra of special type. The used algebra is characterized in existence of a large set of the global right-sided units that are used to define the homomorphism map of the algebra and then to define the hidden discrete logarithm problem using the mutual commutativity of the homomorphism-map operation and the exponentiation operation. The proposed commutative cipher is the first implementation of the post-quantum commutative ciphers based on the hidden discrete logarithm problem defined in a finite algebra that contains no two-sided global unit.
A Computationally Efficient Data-Dependent Projection for Dimensionality Reduction

Principal component analysis (PCA) is a commonly used statistical technique for unsupervised dimensionality reduction, with a drawback of high-computational cost. Random projection (RP) is a matrix-based dimensionality reduction (DR) technique, which projects data by using a projection matrix i.e., constructed with random vectors. Random projection projects the high-dimensional data into low-dimensional feature space with the help of a projection matrix, which is constructed independent of input data. RP uses randomly generated matrices for projection purpose, even though it is computationally more advantageous than PCA, it has been giving unstable results, due to its randomness and data-independence property. Here in this work, we propose a via-medium solution which captures the structure-preserving feature of PCA and the pair-wise distance preserving feature from RP, and also takes less computational cost compared to PCA. Extensive experiments on low and high-dimensional data sets illustrate the efficiency and effectiveness of our proposed method.
Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome

While the study of a single network is well established, technological advances now allow for the collection of multiple networks with relative ease. Increasingly, anywhere from several to thousands of networks can be created from brain imaging, gene coexpression data, or microbiome measurements. And these networks, in turn, are being looked to as potentially powerful features to be used in modeling. However, with networks being non-Euclidean in nature, how best to incorporate them into standard modeling tasks is not obvious. In this paper we propose a Bayesian modeling framework that provides a unified approach to binary classification, anomaly detection, and survival analysis with network inputs. We encode the networks in the kernel of a Gaussian process prior via their pairwise differences, and we discuss several choices of provably positive definite kernel that can be plugged into our models. Although our methods are widely applicable, we are motivated here, in particular, by microbiome research (where network analysis is emerging as the standard approach for capturing the interconnectedness of microbial taxa across both time and space) and its potential for reducing preterm delivery and improving personalization of prenatal care.
Comparing exponential race and signal detection models of encoding stimuli into visual short-term memory.

The exponential race model embodied in the theory of visual attention (TVA) and the power law generalization of the sample size model (SSPL) provide competing accounts of the mechanisms that determine how exposure duration, set size, and attention influence how many items enter visual short-term memory (VSTM). In the exponential race model, items compete for entry into VSTM in a processing race with exponentially distributed processing times. The most recent version of the sample size model assumes that target sensitivity measured by d' increases monotonically as a function of exposure duration and decreases as a power function of set size. Here we compare the two models in a new experiment with letters and Gabor patches and with data from five previously published experiments. This was done by applying TVA to the two-alternative forced-choice method (2AFC), which forms the basis of the experimental work on the sample size model. Both models fitted individual participants' proportions of correct trials quite well, and overall the fits by the two models were almost indistinguishable. This was confirmed by formal pairwise comparison of TVA and SSPL by the Akaike information criterion and the Bayesian information criterion measures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fake Identity Attributes Detection Based on Analysis of Natural and Human Behaviors

Under confrontational environment, individual can impersonate others by wearing masks or other skills to conceal real identity, which brings enormous challenges to physical identity recognition. Moreover, massive fake attributes seriously threaten identity management. Aiming at the existence of fake attributes during multimodal identification, we propose a novel method to detect fake attributes and compute real identity for security of systems. Most previous methods focused on the differences about features between fake and normal attributes, but each method generally targeted one type of fake attribute, and the results decreased a lot with unknown attacks. In this paper, we first explore the essential differences of data distribution caused by natural and human behaviors, then with order-of-consensus-calculation based on the differences, fake attributes are detected by analyzing the rank of consensus identity in recognition results, finally maximum-consensus-calculation is applied to compute real identity for evaluating detection performance. Experimental results on face, fingerprint, and voiceprint demonstrate that the proposed method can detect fake attributes effectively, which has a higher accuracy, and the accuracy of identity recognition is increased obviously by about 13.20% with forgery detection. The additional experiments further confirm the feasibility of the proposed method with increase of fake attributes. Furthermore, the proposed method can deal with different kinds of attacks, even with unknown attacks, and it is also significant to improve the security of identification with complex environment.
Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques

Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules.
PAVSS: Privacy Assessment Vulnerability Scoring System

Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.
Physical-Layer Deep Learning: Challenges and Applications to 5G and Beyond.

The unprecedented requirements of IoT networks have made fine-grained optimization of spectrum resources an urgent necessity. Thus, designing techniques able to extract knowledge from the spectrum in real time and select the optimal spectrum access strategy accordingly has become more important than ever. Moreover, 5G-and-beyond networks will require complex management schemes to deal with problems such as adaptive beam management and rate selection. Although deep learning has been successful in modeling complex phenomena, commercially-available wireless devices are still very far from actually adopting learning-based techniques to optimize their spectrum usage. In this paper, we first discuss the need for real-time deep learning at the physical layer, and then summarize the current state of the art and existing limitations. We conclude the paper by discussing an agenda of research challenges and how deep learning can be applied to address crucial problems in 5G-and-beyond networks.
A Comparative Study on LSB Replacement Steganography

Steganography is an approach of communication where sender and receiver can expect the hidden message. Major goal of steganography is to hide data and should not be susceptible, with less MSE, High PSNR and also with maximum capacity to hold the secret data. In this paper, a comparative analysis is made to evaluate the performance of the LSB replacement algorithm for different number of LSBs. The performance of the LSB replacement steganographic method has been evaluated by computing capacity, mean square error (MSE), peak signal-to-noise ratio (PSNR) and robustness.
An Overview of Abdominal Multi-organ Segmentation

The segmentation of multiple abdominal organs of the human body from images with different modalities is challenging because of the inter-subject variance among abdomens, as well as the complex intra-subject variance among organs. In this paper, the recent methods proposed for abdominal multi-organ segmentation (AMOS) on medical images in the literature are reviewed. The AMOS methods can be categorized into traditional and deep learning-based methods. First, various approaches, techniques, recent advances, and related problems under both segmentation categories are explained. Second, the advantages and disadvantages of these methods are discussed. A summary of some public datasets for AMOS is provided. Finally, AMOS remains an open issue, and the combination of different methods can achieve improved segmentation performance.
Probabilistic Grammar Induction for Long Term Human Activity Parsing

We present a method of representing human activities as Probabilistic Context Free Grammars(PCFGs). Our method will allow these grammars to be learned from any source of data that describe sequences of human actions. We describe how representing human activities as PCFGs will allow them to be used for multiple proposed applications. The method proposed is interpretable such that the representation of an activity can be edited by a human annotator for further increase in performance. We also introduce a method of simulating realistic sequences of human actions, and describe how realistic noise is injected into this data. We propose methods of inducting grammars from this synthetic data and experiments to evaluate both the data and the ability of PCFGs to represent human activities.
Detection of moving human using optimized correlation filters in homogeneous environment

Detection of a moving human is challenging for real-time systems. Misdetection in high alert security areas may lead to heavy losses. This paper presents an optimized approach to avoid this misdetection in sensitive areas. Rotation invariant optimized correlation filters are used for detection of humans. Some pre-processing algorithms such as background subtraction and color space conversion have been linked to the correlation filters to minimize processing time and maximize the accuracy of target detection. The experimental tests of the proposed methodology validate that better accuracy can be achieved if the proposed optimized approach is utilized for moving human detection in real-time systems. In future work, the proposed approach will be extended to detect human activity at night and thermal imagery.
Cloud computing and climate-smart agriculture: An efficient transfer of technology mechanism

The Food and Agriculture Organization of the United Nations (FAO) predicts that the global populationwill rise to 8 billion people by 2025 and 9.6 billion by 2050. To face challenges of food production there is need to develop methods to produce more output with the limited available natural resources. Climate-smart agriculture is an approach for guiding actions required to help stakeholders around the world and identify and develop strategies in order to make agriculture more productive and sustainable. Climate change will increase the vulnerability of agricultural production systems unless scientists and farmers reorient their present approaches toward making them climate-smart or climate-resilient. Use of cloud computing technology in agriculture sector has wider opportunity in the overall development of India. Cloud computing service provider may also offer services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) with affordable cost. Cloud computing has been used for storage of agriculture data by government and private agencies. Use of IOT along with cloud computing can help the farmers a lot to increase the production by providing the correct communication between objects and charging according to the usage of service. Internet of things (IoT) will be beneficial to connect the devices and collect and distribute the information. IOT and cloud computing is a technology which tends to connect various objects in the world to the internet. Use of technology is constantly increasing to improve food production and commercial activities.
Tension Space Analysis for Emergent Narrative

Emergent narratives provide a unique and compelling approach to interactive storytelling through simulation, and have applications in games, narrative generation, and virtual agents. However the inherent complexity of simulation makes understanding the expressive potential of emergent narratives difficult, particularly at the design phase of development. In this paper, we present a novel approach to emergent narrative using the narratological theory of possible worlds and demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis. Lastly, we propose a novel way through which content may be authored for the emergent narrative system using a sketch-based interface.
Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.
Pengujian Perangkat Lunak Sistem Informasi Peminjaman PlayStation dengan Teknik Boundary Value Analysis Menggunakan Metode Black Box Testing

Testing on an application aims to check whether a program has run properly or there are still errors that must be corrected so that the program created will be a program that has good quality. In this study the software that will be reviewed using Black Box Testing is a PlayStation Loan Information Application System where this application aims to facilitate the admin (PlayStation rental) in data collection on the number of borrowers, as well as the return date of PlayStation borrowed by members, where there are 2 forms to be filled in. Member added data form, game added form during the loan. lending data information applications will be tested using Black Box Testing where this test only aims to see the program whether in accordance with the expected function or not without knowing the program code used. Black Box Testing Method consists of several techniques, including Equivalence Partitions, Boundary Value Analysis, Sample Testing, and so on. Among the many testing techniques, in this study the testing technique was chosen using Boundary Value Analysis. Boundary Value Analysis Techniques, are software testing techniques in which tests are designed to include representatives of boundary values in a range.
Validator election in nominated proof-of-stake.

Polkadot is a decentralized blockchain platform to be launched in 2020. It will implement nominated proof-of-stake (NPoS), a proof-of-stake based mechanism where k nodes are selected by the network as validators to participate in the consensus protocol, according to the preferences expressed by token holders who take the role of nominators. This setup leads to an approval-based multi-winner election problem, where each nominator submits a list of trusted candidates, and has a vote strength proportional to their stake. A solution consists of a committee of k validators, together with a fractional distribution of each nominator's vote among them. We consider two objectives, both recently studied in the literature of social choice. The first one is ensuring the property of proportional justified representation (PJR). The second objective, called maximin support, is to maximize the minimum amount of vote support assigned to any elected validator. We argue that the former objective aligns with the notion of decentralization, while the latter aligns with the security level of the consensus protocol. We prove that the maximin support problem is constant-factor approximable, as we present several approximation algorithms for it, and prove a matching hardness result. Furthermore, we present an efficient post-computation which, when paired with an approximation algorithm for maximin support, returns a new solution that a) preserves the approximation guarantee, b) satisfies the PJR property, and c) can be efficiently verified to satisfy PJR by an untrusting third party. Besides being of independent theoretical interest, our results enable the network to run an efficient validator election protocol that simultaneously achieves the PJR property and a constant-factor approximation for maximin support, thus offering strong theoretical guarantees on decentralization and security.