Reviewerly/exHarmony huggingface dataset:
MODELLING WIDE-ANGLE LENS CAMERAS FOR METROLOGY AND MAPPING APPLICATIONS

Abstract. Wide-angle lenses typically offer fields of view greater than 70°, which are utilized in a variety of imaging, mapping, and navigation applications. Wide-angle lenses are commonly modelled using the central perspective model, compensating for lens distortions through a series of additional parameters. The more extreme the distortions, the further the reality of the lens matches the collinearity equations that define the central perspective model. Fisheye lenses are modelled differently because their fields of view are so wide (typically 180°) that the collinearity model is not applicable. This work studied the effects of modelling wide-angle lenses using both the conventional central perspective model and the fisheye model to determine which model best fits the observations and models the distortions more precisely and accurately. These results were produced by generating observations in a dedicated indoor calibration facility at the University of Calgary: an 11 m × 11 m × 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different models and different cameras. The results of the self-calibrating bundle adjustments were then utilized in a check adjustment on independent sets of check images to validate their accuracy. Two cameras, a Ladybug5 and a GoPro Hero5, were tested. The GoPro was also calibrated using a checkerboard target pattern, and the results were compared to those of the 3D calibration target-field. The results of the bundle adjustments determined that the fisheye model describes the distortions more precisely in both wide-angle camera systems.
Increasing Community Knowledge Through the Literacy Movement

Literacy is an important human skill that depends on the ability and awareness to continue learning. Literacy is very closely related to reading activities and information is interpreted as information or news about something. Information literacy in principle aims to develop skills in understanding the information needed or information literacy both through print media and online media. Community service activities will be carried out activities to increase community knowledge through literacy culture. The main problem experienced by the community in general is the low ability of people to read and get good and correct sources of information and avoid hoax information. The steps for the implementation of community service activities carried out in this community service are training activities, provision of useful books for the community, and assistance. The material presented in the training includes (a) the delivery of basic literacy skills in improving reading, writing, numeracy, science, communication information technology (ICT), financial, and cultural &amp; citizenship skills; (b) information literacy through the introduction of electronic sources (technology), critical thinking, problem solving, communication and synthesis; (c) strategies for selecting correct information and stating the source accurately and avoiding hoax information or false information that contains defamation and blasphemy; and (d) motivation for students to have a sense of caring for the surrounding environment. The results of community service can be concluded that the increase in community knowledge through literacy culture is done through the delivery of training materials, the provision of books that are beneficial to the community, and mentoring activities. As a recommendations for further community service activities is focused on fostering students dropping out of school, dropping out of elementary school, dropping out of middle school, and dropping out of high school through literacy culture based on the identification of problems previously carried out.
ARMA model for short-term forecasting of solar potential: application to a horizontal surface of Dakar site

This paper presents a model for short-term forecasting of solar potential on a horizontal surface. This study is carried out in to the context of valuing of energy production from photovoltaic solar sources in the Sahelian zone. In this study, Autoregressive Moving Average (ARMA) process is applied to predict global solar potential upon 24 hours ahead. The ARMA (p, q) is based on finding optimum parameters p and q to better fit considered variable (sunshine). Data used for the model calibrating are measured at the station of Ecole Supérieure Polytechnique of Dakar. Records are hourly and range from October 2016 to September 2017. The choice of this model is justified by its robustness and its applicability on several scales through the world. Simulation is done using the RStudio software. The Akaike information criterion shows that ARMA (29, 0) gives the best representation of the data. We then applied a white noise test to validate the process. It confirms that the noise is of white type with zero mean, variance of 1.252 and P-value of about 26% for a significant level of 5%.Verification of the model is doneby analyzing some statistical performance criteria such the RMSE =0.629 (root mean squared error), the R² = 0.963 (Coefficient of determination), the MAE=0.528 (Mean Absolut Error) and the MBE=0.012 (Mean BiasError). Statistics criteria show that the ARMA (29,0) is reliable; then, can help to improve planning of photovoltaic solar power plants production in the Sahelian zone.
Use of Integrated Mobile Application With Realistic Mathematics Education: A Study to Develop Elementary Students’ Creative Thinking Ability

One of the abilities students must possess is Creative Thinking Ability (CTA) in order to succeed in their lives. Learning must facilitate students to develop CTA; one of them is by developing a mobile application based on Realistic Mathematics Education (RME). The purpose of this study is to determine the level of validity/feasibility, practicality, and effectiveness of mobile application based RME. This mobile application development used the ADDIE model development procedure. The subjects in this study were grade 4 elementary school students. The validation data collection techniques through validation sheets, practicality data through CTA tests. The results of the study show that the developed of mobile application integrated with realistic mathematics meets valid/proper requirements based on expert and practitioner assessments that are practical and easy if applied in mathematics learning and that is effective in improving students’ creative thinking ability.
A Semisupervised Infinite Latent Dirichlet Allocation Model for Target Discrimination in SAR Images With Complex Scenes

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods may suffer from lack of labeled training chips, whose acquirement is costly, time-consuming, and sometimes impossible. Moreover, traditional discrimination features only provide rough and partial description about chip and perform badly in SAR images with complex scenes. In order to solve these problems, we propose a novel semisupervised target discrimination method for SAR image by combining the feature learning with classifier learning into a uniform Bayesian framework based on a modified latent Dirichlet allocation (LDA) model, semisupervised infinite latent Dirichlet allocation (SSILDA). In our method, the semisupervised idea is used to deal with the difficulty of obtaining lots of labeled chips. A new variable is introduced into the LDA model for semisupervised learning, making it possible to obtain the semantic information of chips, while implementing the target discrimination in a semantic level. Moreover, the Dirichlet process (DP) is introduced into the LDA model to automatically determine the number of topics, and the parameters are inferred via Gibbs sampling. We have analyzed the performance of the proposed method comprehensively and specifically by using some measured data and carried out comparisons with the existing methods. The results validate the effectiveness of the proposed method for SAR target discrimination.
Investigating the Reliability of Click Models

Click models aim to extract accurate relevance feedback from the noisy and biased user clicks. Previous work focuses on reducing the systematic bias between click and relevance but few studies have examined the reliability and precision of click models' relevance estimation. So in this study, we propose to investigate the reliability of relevance estimation derived by click models. Instead of getting a point estimate of relevance, a variational Bayesian method is used to infer the posterior distribution of relevance parameters. Based on the posterior distribution, we define measures for the reliability of pointwise and pairwise relevance estimation. With experiments on both real and synthetic query logs, we show that: 1) the proposed method effectively captures the uncertainty in relevance estimation; 2) the reliability of click models' relevance estimation is affected by the size of training data, the average ranking position of documents, and the ranking strategy of search engines.
Medical Image Compression Based on Combining Region Growing and Wavelet Transform

Medical data grows very fast and hence medical institutions need to store high volume of data about their patients.Medical images are one of the most important data types about patients.As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images.Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency.Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes.Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing).In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced.A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values.Then, wavelets methods applied on foreground regions including important regions.These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one.To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.
Digital mapping of soil attributes using machine learning

Mapping the chemical attributes of the soil on a large scale can result in gains when planning the use and occupation of the land. There are different techniques available for this purpose, whose performance should be tested for different types of landscapes. The aim of this study was to spatialize chemical attributes of the soil, comparing eight methods of prediction. Forty morphometric attributes, generated from a digital elevation model, were used as independent variables, in addition to geophysical data, images from the Landsat 8 satellite and the NDVI. All possible combinations between the satellite bands were calculated, generating 28 new variables. Combinations between the Th, U and K bands obtained from the geophysical data were also calculated, generating a further three variables. The final variables to be calculated were the distances between the four points of the edges of the basin (d1, d2, d3 and d4). The dependent variables for the model were Al, Ca, Fe, K, Mg, Na, Si, Ti, Cr, Cu, Mn, Ni, P, Pb, V, Zn, Zr, S and Cl. A total of 200 soil samples were used, which were collected from 100 points at two depths (0-10 and 10-30 cm); the total elements were determined using an X-ray fluorescence analyzer. The Random Forest algorithm proved to be superior to the others in predicting the chemical attributes of the soil at both depths, and is suitable for predicting soil attributes in the study region. Spatial variables are essential, and should be considered when modelling chemical elements in the soil. Using the methods under test, it is possible to predict elements with R² values ranging from 0.32 to 0.62.
Sleight is right: Cyber control as a new battleground for African elections

Abstract Sleight of hand in manipulating the computation of results has become the new might for deciding who wins presidential elections. It appears that whoever controls the computation exercises a right to take advantage and win, and whoever loses or relinquishes control of the computation loses the election. As incumbents do not want to be identified with direct interference or rigging, hacking has become an alternative means. This raises a serious challenge for election management bodies (EMBs) and a new frontier for international observation. As electronic data management has become a key battleground, international observers cannot restrict their monitoring to the manual process alone. However, individual states may have data sensitivity concerns about granting electronic monitoring access to partisan international observers. Institutionalizing internationally agreed protocols that would allow real-time monitoring of EMBs’ computer systems by international observers or forensic audits of any stage of the electoral process to investigate interference, manipulation, hacking, and counter claims, is now a necessity. At the same time, the extent to which international monitors can be trusted to be non-partisan is of equal importance and could reduce forum shopping over time.
Completion norms for 3085 English sentence contexts

Abstract In everyday language processing, sentence context affects how readers and listeners process upcoming words. In experimental situations, it can be useful to identify words that are predicted to greater or lesser degrees by the preceding context. Here we report completion norms for 3085 English sentences, collected online using a written cloze procedure in which participants were asked to provide their best guess for the word completing a sentence. Sentences varied between eight and ten words in length. At least 100 unique participants contributed to each sentence. All responses were reviewed by human raters to mitigate the influence of mis-spellings and typographical errors. The responses provide a range of predictability values for 13,438 unique target words, 6790 of which appear in more than one sentence context. We also provide entropy values based on the relative predictability of multiple responses. A searchable set of norms is available at http://sentencenorms.net . Finally, we provide the code used to collate and organize the responses to facilitate additional analyses and future research projects.
MMF: Attribute Interpretable Collaborative Filtering

Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix Factorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of attribute ratings generated by the inner product of the user latent vectors and the attribute latent vectors. MMF provides more fine grained analyses than matrix factorization in the following ways: attribute ratings with weights allow the understanding of how much each attribute contributes to the recommendation and hence provide interpretability; the common attributes can act as a link between existing and new items, which solves the item cold-start problem when no rating exists on an item. We evaluate the interpretability of MMF comprehensively, and conduct extensive experiments on real datasets to show that MMF outperforms state-of-the-art baselines in terms of accuracy.
MaxPass: Credit-based multipath transmission for load balancing in data centers

Various applications require the data center networks to carry their traffic efficiently. The data center networks usually have a hierarchical topology and exhibit distinct traffic patterns, which is different from the traditional Internet. These features have driven the data center networks to reduce the flow completion time (FCT) and to achieve high throughput. One of the possible solutions is balancing network loads across multiple paths by leveraging transport mechanisms like equal-cost multipath (ECMP) routing. ECMP allows flows to exploit multiple paths by hashing the metadata of the flows. However, due to the random nature of hash functions, ECMP often distributes the traffic unevenly, which makes it hard to utilize the links' full capacity. Thus, we propose an adaptive load balancing mechanism for multiple paths in data centers, dubbed MaxPass, to complement ECMP. A sender adaptively selects and dynamically changes multiple paths depending on the current network status like congestion. To monitor the network status, the corresponding receiver transmits a probe packet periodically to the sender; its loss indicates a traffic congestion. We carry out the quantitative analysis on the ns-2 simulator to show that MaxPass can improve the FCT and the throughput.
Authentication System Design Based on Dynamic Hand Gesture

Due to biometric immutability, an authentication system that depends on irrevocable biometric data (faces and fingerprints) is vulnerable to vicious attacks. Gestures, as short actions that contain static and dynamic behavioral information, are gradually replacing traditional biometrics. Compared to body gestures, hand gestures are more flexible and do not require the user’s entire body to appear in front of the camera. However, most existing feature extraction algorithms rely on the key point of a hand in motion or the image analysis of a static hand gesture, thereby making the authentication less real-time and less effective in the real-word. To alleviate these problems, we propose a user authentication system based on dynamic hand gestures jointly models the silhouette and skeletal properties of moving hands for user authentication. Our system obtains an average 0.105% false acceptance rate (FAR) and an average 3.40% false rejection rate (FRR) on the public Dynamic Hand Gesture 14/28 dataset.
Challenges and Emerging Solutions for Public Blockchains

With the interest and attention that the Blockchain and the Distributed Ledger Technologies (DLTs) have recently demanded, the technology is advancing at a very high rate. With investors and applications in a wide variety of fields, a lot of funding and efforts are being driven into bringing the technology to everyday use. The community and companies are coming up with new ways to collaborate, which makes the blockchain ecosystem evolve at full tilt. Consequently, this paper’s aim is to review the academic and grey literature and to provide readers with information about the evolution, benefits and challenges of the Public Distributed Ledger Technologies and to discuss the latest solutions, which are being developed for bringing decentralization closer to the mainstream. The paper reviews the Directed Acyclic Graph (DAG) structured distributed ledgers with focus on the Hedera Hashgraph, a novelty DLT bringing a unique consensus algorithm with new use-cases enabled by new cryptoeconomic mechanisms, as well as vital services, such as Solidity smart contracts and distributed file storage. Then, we are going to explore second-layer network protocols, a major topic for solving scalability issues and for decentralizing cryptocurrency exchanges. The article tries also to identify the particularities of these technologies and how they bring specific answers to the blockchain trilemma, consisting in three themes - scalability, interoperability and sustainability.
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion to generate random transformations of images with missing pixels to expand existing labelled datasets. In our work, GAN’s been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on GAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showed between DCGAN cascaded with SSD and SSD itself.
The Improvised GZIP, A Technique for Real Time Lossless Data Compression

Whenever it comes to data processing, the user always faces two major constraints.One is storage capacity and second is bandwidth.These two resources must be efficiently utilized by compressing the data.Enormous algorithms are used to compress data.As far as, compression in storage is concern, GZIP is used on large scale for lossless data compression.However, it is not desirable to carry out lossless data compression for real time data.In this paper, an improvisation is proposed in the existing GZIP algorithm for compressing real time data by a contemporary concept of introducing Adaptive Huffman algorithm by replacing the traditional Huffman encoder (static).Simulations have proved that improvised GZIP has approximate 18% better compression ratio and space saving than traditional GZIP for real time data.This research paper extends the usability of GZIP algorithm to carry out lossless compression for real time data.
Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multiple-choice question answering problem. In the first stage, each question-answer pair is fed into an information retrieval engine to find relevant candidate contexts that serve as the underlying knowledge for the inference models. In the second stage, deep learning architectures are used to predict if a candidate answer can be inferred from the context extracted in the first stage. We deploy multiple deep learning architectures pre-trained on different datasets in order to capture semantic features and to enlarge the scope of the questions we can answer correctly. As it will be described, each dataset used for training the inference models has particular characteristics that can be exploited. In the end, all these solvers are combined in an ensemble model to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy. Moreover, the model can answer both simple, factoid questions and more complex questions that require reasoning or inference.