Reviewerly/exHarmony huggingface dataset:
A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance

A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with Logistic-normal noise is specified for the latent weight dynamics and the class-preserving property of the logistic-normal is used to reduce the dimension of the latent space and to build a compositional factor model. The projection used in the dimensionality reduction is based on a dynamic clustering process which partitions the large set of predictive densities into a smaller number of subsets. We exploit the state space form of the model to provide an efficient inference procedure based on Particle MCMC. The approach is applied to track the Standard \& Poor 500 index combining 3712 predictive densities, based on 1856 US individual stocks, clustered in relatively small number of model sets. For the period 2007-2009, which included the financial crisis, substantial predictive gains are obtained, in particular, in the tails using Value-at-Risk. Similar predictive gains are obtained for the US Treasury Bill yield using a large set of macroeconomic variables. Evidence obtained on model set incompleteness and dynamic patterns in the financial clusters provide valuable signals for improved modelling and more effective economic and financial decisions.
RETRACTED: Research on Optimization of Coding and Modulation Information Processing Technology in the Era of Big Data

Abstract With the continuous development of China’s science and technology, the economic level is also constantly improving. In today’s big data environment, the computer field has made a comprehensive breakthrough, and computer technology has been widely used in all walks of life. At present, the arrival of the information age has brought new opportunities and challenges to social development. Currently, people have many ways to obtain information. In the era of big data, various kinds of information are increasing, and all kinds of complex information will affect people’s work and life. Therefore, there is an urgent need to optimize and innovate the coding and modulation information processing technology to improve the efficiency of information processing in the context of the big data era, so that people can efficiently obtain useful information from it to meet the needs of people’s development. Actively explore the research of computer information processing technology, change the traditional information processing pattern, improve the accuracy of information needed in the era of big data, and promote the development of modern society and economy. This article mainly analyzes the current situation of the coding and modulation information processing technology under the background of the big data era, and discusses how to optimize and enhance the coding and modulation information processing technology under the background of the big data era. And try to find a new way out for the coding and modulation information processing technology in the current era based on the results of these studies. Experimental research shows that by controlling the maximum number of iterations, code length, code rate and other conditions of the encoding and decoding algorithm of LDPC codes, the coding efficiency can be effectively improved, and the coding and modulation information processing technology can be optimized.
StackEmo: towards enhancing user experience by augmenting stack overflow with emojis

Many novice programmers visit Stack Overflow for purposes that include posing questions and finding answers for issues they come across in the process of programming. Many questions have more than one correct answer on Stack Overflow, which are accompanied by number of comments from the users. Comments help developers in identifying the answer that better fits their purpose. However, it is difficult to navigate through all the comments to select an answer. Adding relevant visual cues to comments could help developers in prioritizing the comments to be read. Comments logged generally include sentiments of users, which, when depicted visually, could motivate users in reading through the comments and also help them in prioritizing the comments. However, the sentiment of comments is not being explicitly depicted on the current Stack Overflow platform. While there exist many tools that augment or annotate Stack Overflow platform for developers, we are not aware of tools that annotate visual representations of sentiments to the posts. In this paper, we propose StackEmo as a Google Chrome plugin to augment comments on Stack Overflow with emojis, based on the sentiment of the comments posted. We evaluated StackEmo through an in-user likert scale based survey with 30 university students to understand user perception towards StackEmo. The results of the survey provided us insights on improving StackEmo, with 83% of the participants willing to recommend the plugin to their peers. The source code and tool are available for download on GitHub at: https://github.com/rishalab/StackEmo, and the demo can be found here on youtube: https://youtu.be/BCFlqvMhTMA.
The Publicity of Education: Challenges of Modern Communication Strategies

The article investigates the publicity phenomenon in modern education as a moral-communicative discourse of a technogenic society. The study confirms that a person's identity falls under the manipulative influence and becomes the target of direct social and communicative coercion in the public environment. The educational environment includes a lot of public informatization. The article proves that educational media practices in public communications form certain public strategies such as the social organization of society, its socio-cultural norms, and values. The nature of these strategies is sometimes contrived, perfunctory and favorable for information confrontation, serves the only material interests and needs. The study proves that the concept of communicative competence of modern youth, which is constantly in a changing, information-saturated public environment, becomes especially important in the modern educational environment. It must have complex communication skills of social and public adaptation, techniques of effective interpersonal communication in which morality would not have a hidden nature, would not focus only on the physiologically-hidden nature of a person and would not invent surrogate identities. Such kind of publicity including the skills of communicative competence in the educational environment is the requirements of a modern technogenic civilization trying to survive. It is stated that in the present time when information and communication can act as effective activators of value-moral principles of educational culture. Such communication skills are necessary for the fast formation of sustainable principles of cross-cultural understanding that would support the values of tolerance and a healthy lifestyle. In such a case, the development of science and educational competences of all participants of the educational process should lead to periodic modification, improvement of the whole system of interpersonal communication, giving them new forms directed to the tolerance, in which the subject of communication finds himself in the "Other", or find "Other" in himself.
Semi-supervised Anomaly Detection based on Improved Adversarial Autoencoder and Ensemble Learning

We propose a novel anomaly detection method based on improved adversarial autoencoder and ensemble learning. Our method improves on the traditional adversarial autoencoder, and adds an encoder to extract additional feature vectors. The first set of feature vectors is extracted from the input image by the first encoder, and the output image is reconstructed by the decoder. Additional encoders use this reconstructed image to extract a second set of feature vectors. Two sets of feature vectors are weighted and fused to form a new set of feature vectors. Taking this new set of feature vectors as training data, AdaBoost algorithm is used to train and integrate several single-layer decision trees, and finally a strong classifier is obtained for anomaly detection. The proposed method has been verified and evaluated on several published image datasets, and has achieved excellent performance compared with the previous methods.
Multi-Wavelength, Multi-Level Inputs for an All-Optical SOA-Based Neuron

We demonstrate the first optical processing of up to 9-bit/symbol multi-level modulated channels on a complete all-optical SOA-based neuron, with an error of 0.08. A higher number of modulation levels and inputs can improve accuracy.
A New Block Cipher Algorithm Using Magic Square of Order Five and Galois Field Arithmetic with Dynamic Size Block

&lt;p class="0abstract"&gt;This paper describes the development of encryption algorithms using the magic square of order 5 and Multi-level keys with the addition of Matrix keys to increase implementation speed and complexity. This work relied mainly on the magic sum and some equations that were added as an improvement on previous work. Multi-level keys were used for three different message sizes, and an additional key matrix with size 5×5 was used to add more complexity. The proposed work was performed using both GF(P) and GF(2&lt;sup&gt;8&lt;/sup&gt;). Results were compared with the MS3, they have been found good, with acceptable speed and high complexity where it was (P)&lt;sup&gt;9&lt;/sup&gt; × (256)&lt;sup&gt;16&lt;/sup&gt; in the first algorithm, (P)&lt;sup&gt;9&lt;/sup&gt; × (256)&lt;sup&gt;16&lt;/sup&gt; × 3 in the second algorithm, and (P)&lt;sup&gt;9&lt;/sup&gt; × (256)&lt;sup&gt;16&lt;/sup&gt; × 3 × (P)&lt;sup&gt;25&lt;/sup&gt; in the third algorithm, the complexity changed according to the chosen value of N randomness, in addition to speed, complexity, NIST calculations have been performed for texts and histogram calculations for different images were calculated and compared as well.&lt;/p&gt;
Sentiment Analysis of Learning from Home During Pandemic Covid-19 in Indonesia

pandemic in Indonesia impacting education sector issuance policy for learning from home.This policy has many responses from the public on social media like Twitter.There are many types of methods using machine learning approach to classify and predict sentiment analysis about this topic on Twitter.A good method with the best performance is required to increase chances of getting correct prediction and classification.To answer these problems and solve these needs, this research aims to find the best performing classification and prediction methods for analysis.This research objective is also expected to provide an overview for practitioners as a reference for researchers in conducting research.This research used a dataset consisting of 71.70% negative sentiment, 11.78% positive sentiment, and 16.52% neutral from 27708 response data on Twitter.Sentiment grouping by 3 annotators has an average annotation agreement level of 80.57%.Logistic regression produced the best performance compared to 6 other methods with performance accuracy 98.03% and f1-score 92.69%.Information extraction that causes negative sentiment mostly comes from the process of self-adaptation to the learning from home policy.Meanwhile, the provision of the Ministry of Education and Culture's quota assistance has a major influence on positive sentiment.Information extraction that affects to this sentiment can be used by the government to improve the application of learning from home policies such as providing internet quotas which have been proven to affect positive sentiment.
Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition

Monitoring of human states from streams of sensor data is an appealing applicative area for Recurrent Neural Network (RNN) models. In such a scenario, Echo State Network (ESN) models from the Reservoir Computing paradigm can represent good candidates due to the efficient training algorithms, which, compared to fully trainable RNNs, definitely ease embedding on edge devices. In this paper, we provide an experimental analysis aimed at assessing the performance of ESNs on tasks of human state and activity recognition, in both shallow and deep setups. Our analysis is conducted in comparison with vanilla RNNs, Long Short-Term Memory, Gated Recurrent Units, and their deep variations. Our empirical results on several datasets clearly indicate that, despite their simplicity, ESNs are able to achieve a level of accuracy that is competitive with those models that require full adaptation of the parameters. From a broader perspective, our analysis also points out that recurrent networks can be a first choice for the class of tasks under consideration, in particular in their deep and gated variants.
Rice Production Forecasting System in East Java Using Double Exponential Smoothing Method

. Rice (Orza savita L.) is one of the most important carbohydrate-producing food plants in the world, besides wheat and corn. The need for rice continues to increase because the increase in the number of consumers is not balanced with sufficient production. According to BPS (2017), rice production in East Java Province in 2017 amounted to 13.06 million tons of Dry Milled Grain (GKG). The aim of this research is to predict / forecast the yield of rice production in the next period in order to become the target of rice production in the next period. The raised problem is how to predict the rice production. From the data that has been obtained from 1993 to 2017 the data shows increasing gradually which is classified into trend data. Thus, the forecasting method that can be used is the double exponential smoothing method. In the calculation process, it is done by finding S ', S' ', a, b &amp; also the next period of forecasting sessions, using 0 &lt;x &lt;1. From the calculation of alpha 0.1 - 0.9 obtained the smallest MAPE of 3.14 in alpha 0.5. The results of the forecast for the next period increased 13299827.49 tons with an accuracy of 96.86%.
Classifying histograms of medical data using information geometry of beta distributions

In this paper, we use tools of information geometry to compare, average and classify histograms. Beta distributions are fitted to the histograms and the corresponding Fisher information geometry is used for comparison. We show that this geometry is negatively curved, which guarantees uniqueness of the notion of mean, and makes it suitable to classify histograms through the popular K-means algorithm. We illustrate the use of these geometric tools in supervised and unsupervised classification procedures of two medical data-sets, cardiac shape deformations for the detection of pulmonary hypertension and brain cortical thickness for the diagnosis of Alzheimer’s disease.
Improved Supersingularity Testing of Elliptic Curves Using Legendre Form

There are two types of elliptic curves, ordinary elliptic curves and supersingular elliptic curves. In 2012, Sutherland proposed an efficient and almost deterministic algorithm for determining whether a given curve is ordinary or supersingular. Sutherland’s algorithm is based on sequences of isogenies started from the input curve, and computation of each isogeny requires square root computations, which is the dominant cost of the algorithm. In this paper, we reduce this dominant cost of Sutherland’s algorithm to approximately a half of the original. In contrast to Sutherland’s algorithm using j-invariants and modular polynomials, our proposed algorithm is based on Legendre form of elliptic curves, which simplifies the expression of each isogeny. Moreover, by carefully selecting the type of isogenies to be computed, we succeeded in gathering square root computations at two consecutive steps of Sutherland’s algorithm into just a single fourth root computation (with experimentally almost the same cost as a single square root computation). The results of our experiments using Magma are supporting our argument; for cases of characteristic p of 768-bit to 1024-bit lengths, our algorithm runs 43.6% to 55.7% faster than Sutherland’s algorithm.
Deixis in the Song Lyrics of Hailee Steinfeld’s “Half Written Story” Album

The research aims to analyze the three types of deixis analysis using Yule's theory and interpret the reference meaning in the Hailee Steinfeld’s Half Written Story album song lyrics. Problems raised in this research are: 1) What are the types of deixis used in the five song’s lyrics of Hailee Steinfeld’s “Half Written Story” Album? 2) What is the most dominant type of Deixis used in the five song’s lyrics Hailee Steinfeld’s “Half Written Story” Album?, and 3) What are the refence meanings of those song lyrics used in Hailee Steinfeld’s“Half Written Story” Album? This study was conducted by using a descriptive qualitative method
Adult age differences in parafoveal preview effects during reading: Evidence from Chinese.

We investigated parafoveal processing by 44 young (18-30 years) and 44 older (65+ years) Chinese readers using eye movement measures. Participants read sentences which included an invisible boundary after a two-character word (N) and before two one-character words (N + 1, N + 2). Before a reader's gaze crossed the boundary, N + 1 and N + 2 were shown normally or masked (i.e., as valid/invalid previews), after which they reverted to normal. Young adults obtained preview benefits (a processing advantage for valid over invalid previews) for both words. However, older adults obtained N + 2 preview benefits only when N + 1 was valid, suggesting their parafoveal processing is more limited. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Automated hierarchical classification of scanned documents using convolutional neural network and regular expression

&lt;p&gt;This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.&lt;/p&gt;
An Experimental Testbed for the Study of Visual Based Navigation Docking of Two Vertical Compound Aircraft

Compound aircraft, merging the advantages of two or more aircraft, is now a promising concept for designing modern aircraft. How to achieve successful docking of two aircraft in flight is an important issue for the applications of vertical compound aircraft. In order to resolve such an issue, this paper proposes a new visual based navigation docking scheme, which is different from rendezvous and docking operations in space applications. The present scheme uses a monocular camera mounted on the chaser aircraft with a certain installation angle to collect the visual features of the target aircraft, then the position and attitude of the landing gear of the target aircraft can be determined. Moreover, a six degrees of freedom docking mechanism mounted on the chaser aircraft is designed to catch hold of the landing gear tires of the target aircraft. In order to avoid undesired collision during the docking process, Z-direction position of the target landing gear is predicted and the proposed Z-direction speed control algorithm is applied to the chaser platform. The robustness of the present scheme has been validated numerically and experimentally by means of a chaser platform and a target platform on the ground testbed. Successful docking of the chaser platform and the target landing gear has been achieved, respectively, when the target platform performs a Z-direction movement and a compound movement.
What type of experience is needed to generate a human-like view-invariant representation of face identity? Evidence from Deep Convolutional Neural Networks

Face recognition depends on the generation of a view-invariant representation of face identity. We have recently discovered a subset of view-invariant facial features that humans use for face identification. But what type of experience is needed in order to generate this face representation? This question is hard to answer in humans as we have no access to the type of experience humans have with faces during development. In previous studies, we discovered that face-trained deep convolutional neural networks (DCNNs) are sensitive to the same subset of facial features humans use for face identification. This sensitivity emerges at high layers of the network, where a view-invariant representation of face identity is generated. These models enable us to ask what type of experience is required to achieve this human-like, view-invariant face representation. To that end, we systematically trained a DCNN with different number of identities and different images per identity. We found that the number of training images that is required for the generation of sensitivity to human-like critical features corresponds with the generation of a view-invariant face representation. Furthermore, we found a tradeoff between the number of identities and the number of images per identity that are required to generate a view-invariant representation such that training with 10 identities requires 300 images per identity, whereas training with 1000 identities requires only 10 images per identity. These findings suggest that sensitivity to human-like view-invariant facial features that define the identity of the face can be achieved with a relatively small training set. These findings may shed light on the initial stages of development of the human face recognition system suggesting that infants who are exposed to a relatively small number of identities during their first year of life can already extract identity-relevant facial information.