Reviewerly/exHarmony huggingface dataset:
Conceptual model to represent body signals in the learning process

Several educators, for decades have been contributing to the education join more theory and practice. However, the learning strategies need to be based on the diversity of styles present in the classroom, having the teacher first understood the student's need through the interpretation of their emotions, expressed from body signals such as heart beats, temperature, brain waves, facial and body expressions. Thus, the objective of this work is to study and propose a model to represent these body signals and relate them to the learning process, using computational resources based on technologies such as artificial intelligence, machine learning, big data and the internet of things by means of 4 steps: selection of technologies; signal capture; identification and analysis of captured data; and analysis based on theories and learning styles. As for the methodology, it presents an approach of a qualitative and quantitative nature and a theoretical basis based on bibliographic research. The research is under development.
Innovation Indicators for Educational Softwares

Educational software can be used to innovate the teaching-learning process, being an enabler to foster students' current essential skills and abilities, such as collaboration, communication, critical analysis and creative problem solving. This article presents result of a research work a set of innovation indicators that can be used in the evaluation of educational software.
Deep Gradient Boosting

Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters. We hypothesize that in certain cases it is desirable to relax its intrinsic generalization properties and introduce an extension of SGD called deep gradient boosting (DGB). The key idea of DGB is that back-propagated gradients inferred using the chain rule can be viewed as pseudo-residual targets of a gradient boosting problem. Thus at each layer of a neural network the weight update is calculated by solving the corresponding boosting problem using a linear base learner. The resulting weight update formula can also be viewed as a normalization procedure of the data that arrives at each layer during the forward pass. When implemented as a separate input normalization layer (INN) the new architecture shows improved performance on image recognition tasks when compared to the same architecture without normalization layers. As opposed to batch normalization (BN), INN has no learnable parameters however it matches its performance on CIFAR10 and ImageNet classification tasks.
Dilated Floor Functions That Commute Sometimes

We explore the dilated floor function fa(x)= ⌊ax⌋ and its commutativity with functions of the same form. A previous paper found all a and b such that fa and fb commute for all real x. In this paper, we determine all x for which the functions commute for a particular choice of a and b. We calculate the proportion of the number line on which the functions commute. We determine bounds for how far away the functions can get from commuting. We solve this fully for integer a, b and partially for real a, b.
The impact of civil society and governance on poverty: Are there differences between the North and East Africa region?

This article attempts to study the impact of civil society and governance on poverty. In this work, we have tried to answer the following questions: what is the effect of civil society and governance on poverty in the region of North and East Africa? In this framework, the basic assumption was the existence of a direct and indirect effect of civil society and the quality of governance in reducing poverty. The study of this hypothesis was formulated in a static model applied to data available on the region of North and East Africa between 1996-2016. The results of our regressions show that civil society, has a positive effect in reducing poverty in East Africa, but negative in North Africa and the political and legal indicators, has a positive effect on poverty in the East African countries and administrative indicators, has a positive effect in North Africa. This result implies that civil society and governance quality factors play an important role in reducing poverty in East Africa as North Africa. The relationship between civil society, governance and poverty varies according to the stage of development. But notes significant differences between the region of North Africa and East. This supports our contention that civil society and governance has more impact on poverty in the East African region, the poorest than in the rich region of North Africa. For example, the relationship between civil society and poverty is positive and significant for East Africa, is negative and significant in North Africa. The relationship between governance (policy and legal indicators) and poverty is positive and significant for East Africa, but in North Africa only administrative indicators have a positive effect on poverty. This supports our claim that governance has more impact on poverty than civil society.
Anisotropic Joint Trilateral Rolling Filter for Image Smoothing

Filtering an image by eliminating noise and irrelevant details while preserving prominent structure edges is an important pre-processing task in many fields, such as image processing and computer vision. In this study, a novel approach called anisotropic joint trilateral rolling filter (AJTRF) is proposed for the smoothing of an image while preserving important structure edges. AJTRF works in an iterative manner and extends the joint bilateral filter to a joint trilateral filter by employing additional weights in eigen space. During filtering, the range weights are recalculated in each iteration, while the spatial Gaussian weights are computed in anisotropic orientations instead of isotropic orientations. Furthermore, the inner products between anisotropic orientations (expressed as eigenvectors) are used as weights to measure orientation similarities of pixels. Structure tensors at each pixel are calculated to determine the structure orientations, which are used as the anisotropic orientations. A range of experiments are conducted. The results demonstrate that the proposed AJTRF has stronger smoothing and structure preserving ability than established methods.
Send in the Drones Developing an Aerial Disaster Relief Response System

Based on the locations of locations of ISO cargo containers and the drone fleet, we develop a shortest path model. The model provides a recommendation for flight plan of drones using Dijkstra’s algorithm. By re-planning the flight path through the short- est path algorithm, it is possible to reduce the flight length of drones and increase the navigation time of drones. We reduce the length of the roads need to be photographed by 60.2% (282.8km) and 46.2%(365.24km) respectively in the western region and eastern region.
Convolutional Neural Networks on Randomized Data

Convolutional Neural Networks (CNNs) are build specifically for computer vision tasks for which it is known that the input data is a hierarchical structure based on locally correlated elements. The question that naturally arises is what happens with the performance of CNNs if one of the basic properties of the data is removed, e.g. what happens if the image pixels are randomly permuted? Intuitively one expects that the convolutional network performs poorly in these circumstances in contrast to a multilayer perceptron (MLPs) whose classification accuracy should not be affected by the pixel randomization. This work shows that by randomizing image pixels the hierarchical structure of the data is destroyed and long range correlations are introduced which standard CNNs are not able to capture. We show that their classification accuracy is heavily dependent on the class similarities as well as the pixel randomization process. We also indicate that dilated convolutions are able to recover some of the pixel correlations and improve the performance.
Packing Chromatic Number of Bismuth Tri-iodide and First type Nanostar Dentrimers

The packing chromatic number  of a graph  is the smallest integer  for which there exists a mapping  such that any two vertices of color  are at distance at least . In this paper we determine the packing chromatic numbers of bismuth tri-iodide and first type nanostar dentrimers.
APLIKASI SECURE-MESSAGE DENGAN ALGORITMA RC6 (RIVEST CODE 6) BERBASIS ANDROID

Many factors must be considered in terms of security when communicating using information technology. SMS is one of the features that are embedded in a mobile phone is no exception in android phone. SMS is still widely used for the exchange of information, many users inadvertently share information that is privacy by sending a message by SMS. Messages that are done with SMS will be received by the provider of the service provider, then after it was forwarded to the recipient therefore sending a message by SMS need to be protected, so that the safety messages are kept strictly confidential. In this study, the software will be built in order to improve the security of messages via SMS. The application will be built to encrypt a message that will be sent and decrypt incoming messages via SMS. Applications built using the RC6 algorithm. RC6 algorithm is an algorithm flow block (block cipher) were included into the type of symmetric key cryptography system. RC6 algorithm implementation is built using Android technology, to protect and maintain the confidentiality of messages. The results of trials that have been done with this application, encrypts messages sent and can decrypt a message that was well received, the message can only be decrypted if entered the correct key corresponding to the message encryption key at the time. This application can only perform encryption and decryption of text messages (letters, numbers, symbols), to encrypt the decrypted form of the image file can not be done with this application. Keyword : SMS, Android, Cryptography, Algorithms RC6.
Understanding key business processes for business process outsourcing transition: industrial case study: discovering business processes by analyzing system's database

Many organizations now outsource the operation of specific business processes to external service providers, which is referred to as BPO (Business Process Outsourcing). At a transition phase in BPO, the providers need a comprehensive and in-depth understanding of the target (outsourced) business domain. There are many types of the business processes. Most of them are often undocumented. A common way to discover business processes is shadowing (i.e., observing system operators' work). If providers observe all types of business processes, it is costly and time-consuming. An important part of the transition phase is to identify and understand the key business processes being outsourced. This can be costly and time-consuming, especially if shadowing is used. This report introduces an industrial case study of transition phase in BPO. In the case, we analyzed 1,960 records stored in the system related to the business processes for one year period system operation. Each record corresponds to one activity execution by a system operator of the business processes. We transformed the records into 152 distinct types of business processes and found 19 activities in all types of the processes. By evaluating the execution frequency of each type, we selected top four types as key business processes which include 10 activities which cover 96.8 % (1,898/1,960) of all activity executions. The key business processes were very helpful in comprehending the main characteristic of the target business domain in the transition phase.
Multi-residuals Network and Region Constraints Based Face-image Denoising

In recent years, the denoising models based on convolutional neural network (CNN) have made great progress. However, CNN based image denoising models tend to generate artifacts and blurry edges. To deal with this problem, this paper proposes a multi-residuals network with cascade strategy to keep image textures, and integrates face region constraints to loss function of model optimization. The weighted loss function characterizes the location and gray probabilities of different face regions, which brings benefits to recover face-image sharpness and naturalness. Experimental results on the Helen and IMM face datasets show that the proposed model can suppress artifacts in smooth regions and recover sharper edges.
LibGuides: Research: Research

Starting research can be difficult. These resources are specifically built and provided to help you become a better researcher and find what you need.
Large-scale Partially Separable Function optimization Using Cooperative Coevolution and Competition Strategies

Optimizing of the large-scale partially separable functions in the real world is a challenging task. In this paper, we devise a novel optimization method based on coevolution and competition strategies. the proposed method is adopted in two stages: 1) the differential grouping (DG) is used to decompose the original problems into several different subcomponents; 2)Competitive swarm optimizer (CSO) is used to optimize the subcomponents individually. Experimental results show that the combining of DG and CSO performs better than state-of-the-art metaheuristic methods on partially separable functions optimization.
A time-distance trade-off for GDD with preprocessing: instantiating the DLW heuristic

For 0 ≤ α ≤ 1/2, we show an algorithm that does the following. Given appropriate preprocessing P(ℒ) consisting of [EQUATION] vectors in some lattice ℒ ⊂ Rn and a target vector t ∈ Rn, the algorithm finds y ∈ ℒ such that [EQUATION] in time poly(n) · Nα, where η(ℒ) is the smoothing parameter of the lattice.The algorithm itself is very simple and was originally studied by Doulgerakis, Laarhoven, and de Weger (to appear in PQCrypto, 2019), who proved its correctness under certain reasonable heuristic assumptions on the preprocessing P(ℒ) and target t. Our primary contribution is a choice of preprocessing that allows us to prove correctness without any heuristic assumptions.Our main motivation for studying this is the recent breakthrough algorithm for IdealSVP due to Hanrot, Pellet-Mary, and Stehle (to appear in Eurocrypt, 2019), which uses the DLW algorithm as a key subprocedure. In particular, our result implies that the HPS IdealSVP algorithm can be made to work with fewer heuristic assumptions.Our only technical tool is the discrete Gaussian distribution over ℒ, and in particular, a lemma showing that the one-dimensional projections of this distribution behave very similarly to the continuous Gaussian. This lemma might be of independent interest.
Efficient Cloud Resource Management using Neuromorphic Modeling and Prediction for Virtual Machine Resource Utilization

With the rapid development of Cloud Computing and Data Centers, Virtual Machine consolidation has become an important issue to achieve economic scale. In order to support such feature, a robust scheme for Virtual Machine resource demands prediction is critical. Previous prediction models such as Auto-Regressive Moving Average model lacks the ability to give an acceptable accuracy for a large prediction window and conventional machine learning based methods suffer from high complexity problem. In this work, a neuromorphic system based on cogent confabulation is built to predict the resource usages from statistics of historical records in comprehensive dimensions. The system exploits the correlations between observations in multiple dimensions and models a probability network to be finely tuned to support the prediction application. The experimental results show the cogent confabulation model based Virtual Machine resource utilization prediction gives a better accuracy than previous work and has an intrinsic advantage in dynamic prediction with a wider prediction window. Using the accurate confabulation-based VM resource prediction, the cloud resource management can improve the energy efficiency (in terms of electricity price) by up to 26.52%.
Roll of Unified Graph Analysis Platforms

A unified graph engine has been playing increasingly critical roles in many applications, especially for those requiring cross-domain analysis and near real-time decision-making on massive data, aiming to offer integrated and efficient end-to-end capabilities in concurrent graph data query, interactive graph analysis, and large scale graph-based (deep) learning. However, there is barely a unified graph system for enterprise use to the best of our knowledge. Simply assembling some frameworks/libraries together can result in significant performance degradation, due to the sparsity of graph data and irregular data access patterns, which adversely impacts its adoption in industry. In this talk, we will exemplify the challenges using our three efforts on making metropolitan districts smart within an integrated engine, which consist of managing a complex synergy of heterogeneous urban data via property graphs, modeling traffic flow patterns from the stored data through heterogenous information network analysis, and predicting traffics interactively using emerging graph neural networks. Although smart city is projected to become one of the most promising scenarios in the AI era, the unified graph engine can address many other domains such knowledge graph analysis and multi-modality medical research. We will address in the talk the progress towards the above three scientific directions and also point out relevant future research opportunities from the industrial perspective.
Pemetaan Lokasi Wisata Di Kota Cirebon Dengan Penerapan Aplikasi Sistem lnformasi Geografis

Tourism in the city of Cirebon is still tinged with a very diverse religious tour and culturation of culture that is still heavily influenced by Persia, Arabia and China. This can be seen from the palace architectural style which is still influenced by Chinese, Persian, and even Arabic cultures. The results of cultural culture with foreign nations, resulting in a variety of tourist attractions in the city of Cirebon. But the location information of the existence of tourist attractions in the city of Cirebon is still not well publicized. This research will develop an application by utilizing the geographical information system of tourist attractions in the city of Cirebon. The Geographic Information System is very appropriate to map the spread of tourist attractions in the city of Cirebon. The software development model in this study is the system life cycle or the System Development Life Cycle. The results of this study are the value of T count&gt; T table (3,230&gt; 1,665). This means that the probability is significant&gt; α (0.002 &lt;0.05). So that geographic information systems mapping tourist attractions in the city of Cirebon can meet expectations by users to find the shortest routes and safer locations.
Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation

A mind-map is a diagram used to represent ideas linked to and arranged around a central concept. It’s easier to visually access the knowledge and ideas by converting a text to a mind-map. However, highlighting the semantic skeleton of an article remains a challenge. The key issue is to detect the relations amongst concepts beyond intra-sentence. In this paper, we propose a multi-grained framework for automatic mind-map generation. That is, a novel neural network is taken to detect the relations at first, which employs multi-hop self-attention and gated recurrence network to reveal the directed semantic relations via sentences. A recursive algorithm is then designed to select the most salient sentences to constitute the hierarchy. The human-like mind-map is automatically constructed with the key phrases in the salient sentences. Promising results have been achieved on the comparison with manual mind-maps. The case studies demonstrate that the generated mind-maps reveal the underlying semantic structures of the articles.
National Security and National Library

The purpose of the study is to understand the mission of national library in the system of national security of Russia. The author formulates definition of national security basing on the analysis of state concepts and doctrines of national security. The national library is conceived as a humanistic resource of national security, which is protected from information wars. The author considers the components of national security: the nation at the level of individual, society and state; national values in the form of demographic stability, cultural heritage, favourable environment; hazards — hostile phenomena detrimental to the nation; types of security — the types of activities necessary to overcome external and internal hazards; resources — social institutions that ensure the integrity and sustainable development of the nation. The paper proposes definition of bibliosphere as a super-system (system of systems), carrying out production, preservation, use and further development of the national book communication. The components of bibliosphere are: professionally specialized social institutions (publishing, printing industry, book trade, librarianship, bibliography); bibliophile social and cultural movement; commercial and non-profit associations; government authorities and censorship, etc. Considering the doctrine of information security, the author notes that bibliosphere is not an element of information sphere and an object of information security. Culture is presented as a key factor in national security. The paper characterizes the dilemma of militarization and humanization of society in the post-industrial era and emphasizes the urgency of formation of moral and creative individual. The author grounds the new understanding of social mission of the national library.
Ontological model for classification of diseases in bovines and buffaloes

This work presents an ontological model for classifying common diseases with prevalence in herds of cattle and buffaloes. The methodology adopted is based on a literature review. The data extracted from the works reviewed were imputed in the proposed model. The ontological model developed is composed of six main classes, being: Anatomy, Amazon Biome, Diseases, Breeds, Breeding System, and Production Type; The relationships between diseases and its class(es) are also mapped in the model. The ontological structure was created using the Protégé tool - open source framework for building intelligent systems. This work is part of a project that aims to build a framework of semantic resources aimed at supporting animal health care as well as to improve the sustainability of livestock production.