Reviewerly/exHarmony huggingface dataset:
Transforming Urban Education in São Paulo: Insights into a Critical-Collaborative School Project

ABSTRACT The world is experiencing a particularly tense moment which asks for a project that goes beyond the limiting situations posed by immediate reality. This condition considers the critical immersion of individuals in their known realities and broad emersion in multiple contexts of understanding, which may broaden their horizons of perception. Vygotsky and recent followers offer us a basis for rethinking our reality in a collaborative and critical search for forms of development beyond who we are. This article brings about projects developed with schools from very poor and wealthy communities of São Paulo, where participants act together to transform their life conditions.
Review on population-based metaheuristic search techniques for optimal power flow

&lt;span&gt;Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. &lt;br /&gt; The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power &lt;br /&gt; system operation.&lt;/span&gt;
A JND-Based Pixel-Domain Algorithm and Hardware Architecture for Perceptual Image Coding

This paper presents a hardware efficient pixel-domain just-noticeable difference (JND) model and its hardware architecture implemented on an FPGA. This JND model architecture is further proposed to be part of a low complexity pixel-domain perceptual image coding architecture, which is based on downsampling and predictive coding. The downsampling is performed adaptively on the input image based on regions-of-interest (ROIs) identified by measuring the downsampling distortions against the visibility thresholds given by the JND model. The coding error at any pixel location can be guaranteed to be within the corresponding JND threshold in order to obtain excellent visual quality. Experimental results show the improved accuracy of the proposed JND model in estimating visual redundancies compared with classic JND models published earlier. Compression experiments demonstrate improved rate-distortion performance and visual quality over JPEG-LS as well as reduced compressed bit rates compared with other standard codecs such as JPEG 2000 at the same peak signal-to-perceptible-noise ratio (PSPNR). FPGA synthesis results targeting a mid-range device show very moderate hardware resource requirements and over 100 Megapixel/s throughput of both the JND model and the perceptual encoder.
Task-Based Neuromodulation Architecture for Lifelong Learning

The conventional supervised deep learning frameworks for image classification focus on finding the minimizer of an objective function. However, for a lifelong learning system, a network has to be able to dynamically learn new tasks from very few examples. In biological neural networks, it is observed that neuromodulation acts as a key factor in continual learning. In this work, we propose a dynamic learning system, ModNet, wherein a modulatory unit regulates the learning depending on the context and the internal state of the system. The modulatory unit, which consists of a group of neurons, exercises the use of neuromodulation and Hebbian plasticity mechanisms over a population of neurons instead of relying on backpropagation of error gradients. An efficient digital architecture with on-chip training and resource sharing is proposed to facilitate the efficacy of task-based processing.
User Group Analytics Survey and Research Opportunities

User data can be acquired from various domains and is characterized by a combination of demographics such as age and occupation, and user actions such as rating a movie or recording one's blood pressure. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a physical activity. User data analytics usually relies on identifying group-level behaviors such as “Asian women who publish regularly in databases”. Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this survey, we discuss different approaches for each component of user group analytics, i.e., discovery, exploration, and visualization. We focus on related work which arises from combining those components. We also discuss challenges and future directions of having an all-in-one system, where all those components are combined. This survey has been presented in the form of two tutorials [1] , [2].
Knowledge of diabetic patients users of the Health Unic System about diabetic retinopathy

Objective: To evaluate the level of knowledge of diabetic patients treated at the Unified Health System (SUS -Sistema Único de Saúde) in the city of Boa Vista / Roraima, about Diabetic Retinopathy (DR).Methods: This is a cross-sectional, descriptive, quantitative study conducted through the application of a semi-structured questionnaire for 150 diabetic individuals, SUS users, from the city of Boa Vista -RR, during the year 2017.Statistical analyzes were performed using the Microsoft Excel and EpiInfo 7® programs, setting the 5% level for the rejection of the null hypothesis.Results: Of the total sample, 76.7% of the individuals did not have any knowledge about DR, 19.3% had some type of knowledge, but did not have the pathology, 2.7% knew, had DR and was receiving treatment, 1,3% knew, had DR and was not receiving treatment.About orientation, 40.6% of the participants never received any information about the risk of vision loss.About the type of Diabetes, 44.7% of the participants did not know what type they had, 42% reported having DM 2 and 13.3% DM1.On glucose control, 59.4% could not maintain it.It was evidenced an association between glucose control and DR knowledge, between the time of DM installation and knowledge about DR, and between having consulted an ophthalmologist and knowing about DR.Conclusion: The level of knowledge about DR is very low, a worrying factor because it is one of the most important complications of diabetes.It is observed that the health system is not being efficient as facilitator of this knowledge.
Performance Criteria for the Identification of Inertial Sensor Error Models

This paper considers performance criteria for the identification of sensor error models and the procedure for their calculation. These criteria are used to investigate the efficiency of the identification problem solution, depending on the initial data, and to carry out a comparative analysis of various suboptimal algorithms. The calculation procedure is based on an algorithm that solves the joint problem of hypothesis recognition and parameter estimation within the Bayesian approach. A performance analysis of the models traditionally used to describe errors of inertial sensors is given to illustrate the application of the procedure for the calculation of performance criteria.
Adaptively Secure Anonymous Identity-based Broadcast Encryption for Data Access Control in Cloud Storage Service

Cloud computing is now a widespread and economical option when data owners need to outsource or share their data.Designing secure and efficient data access control mechanism is one of the most challenging issues in cloud storage service.Anonymous broadcast encryption is a promising solution for its advantages in the respects of computation cost and communication overload.We bring forward an efficient anonymous identity-based broadcast encryption construction combined its application to the data access control mechanism in cloud storage service.The lengths for public parameters, user private key and ciphertext in the proposed scheme are all constant.Compared with the existing schemes, in terms of encrypting and decrypting computation cost, the construction of our scheme is more efficient.Furthermore, the proposed scheme is proved to achieve adaptive security against chosen-ciphertext attack adversaries in the standard model.Therefore, the proposed scheme is feasible for the system of data access control in cloud storage service.
Improving rule-based classification using Harmony Search

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.
MyRestaurant: A Smart Restaurant with a Recommendation System

In this paper, we discuss, in an internet of things environment, our solution regarding context management in the area of food services.We propose a system that automates the different services offered in a restaurant and connects the various equipment of this restaurant, in order to simplify and facilitate the work of employees using this system.We also have integrated a content-based recommendation module, that is based on the use of a similarity measure to keep track of the client order history in order to suggest or recommend to him similar dishes for his next orders.
Educational Serious Games as a Service: Challenges and Solutions

Cloud computing is a distributed computing technology that facilitates access to resources in a dynamic and scalable way which is highly available. One of the service models is SaaS (Software as a Service) which allows executing Web applications flexible and elastically while offering collaborative characteristics. The integration of SaaS in educational environments promotes academic performance and facilitates knowledge management, allowing for storage, exchange, access, and synchronization of information. On the other hand, serious educational games promote among the people involved: collaboration, communication, social abilities, critical thinking, and problems solving. The present work analyzes the challenges that SaaS, with its collaborative and Multi-Tenant characteristics, offer to the execution of serious educational games, this is named JSEaaS –Serious Educational Games as a Service. The analysis was developed based on an exhaustive and systematic bibliographic revision with a qualitative methodological focus, which generated investigation questions, search strategies, keywords and inclusion and exclusion criteria that were applied to identify articles in different sources. This work allowed for the development of a list of the functional characteristics that the serious educational games will acquire when used as software as a service.
Interval-valued intuitionistic fuzzy graphs

The definitions of eight different types of interval-valued intuitionistic fuzzy graphs are introduced and their representations by index matrices are discussed.Examples for operations over these graphs are given.
A Goal-driven Risk Management approach for Distributed Agile Development Projects

Software companies are now using Distributed Agile Development (DAD) in order to create high quality solutions, which aligns with the business priorities of lesser time and cost. Although, DAD is beneficial, there are significant risks involved in such projects. In order to minimize the adverse effect of risks on DAD projects, it is imperative to understand, how the risks impact the project performance goals including ‘Time’, ‘Cost’ and ‘Quality’. In this paper we present a goal driven approach for managing risks in DAD projects. This approach of risk management will enable project managers to identify the most important risks with respect to the goal to be achieved and focus on managing those risks first. The study shows that if ‘Time’ is a considered goal for a DAD project, the most important risks that would need consideration are related to requirement management, architecture changes and coordination issues between stakeholders. Similarly, if ‘Quality’ is the primary performance goal in a DAD project, it would be necessary to first deal with risks related to internal and external communication in the organization, team collaboration and requirement documentation availability. The awareness of top ranking risk factors that impact a particular project goal will assist the projects managers to control the risks in a way that the desired project goals can be achieved.
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding

Jihwan Lee, Ruhi Sarikaya, Young-Bum Kim. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Improving Semantic Similarity with Cross-Lingual Resources: A Study in Bangla—A Low Resourced Language

Semantic similarity is a long-standing problem in natural language processing (NLP). It is a topic of great interest as its understanding can provide a look into how human beings comprehend meaning and make associations between words. However, when this problem is looked at from the viewpoint of machine understanding, particularly for under resourced languages, it poses a different problem altogether. In this paper, semantic similarity is explored in Bangla, a less resourced language. For ameliorating the situation in such languages, the most rudimentary method (path-based) and the latest state-of-the-art method (Word2Vec) for semantic similarity calculation were augmented using cross-lingual resources in English and the results obtained are truly astonishing. In the presented paper, two semantic similarity approaches have been explored in Bangla, namely the path-based and distributional model and their cross-lingual counterparts were synthesized in light of the English WordNet and Corpora. The proposed methods were evaluated on a dataset comprising of 162 Bangla word pairs, which were annotated by five expert raters. The correlation scores obtained between the four metrics and human evaluation scores demonstrate a marked enhancement that the cross-lingual approach brings into the process of semantic similarity calculation for Bangla.
Adaptive information dissemination in the Bitcoin network

Distributed ledgers have received significant attention as a building block for cryptocurrencies and have proven to be also relevant in several other fields. In cryptocurrencies, this abstraction is usually implemented by grouping transactions in blocks that are then linked together to form a blockchain. Nodes need to exchange information to maintain the status of the chain but this process consumes significant network resources. Unfortunately, naively reducing the number of messages exchanged can have a negative impact in performance and correctness, as some transactions might not be included in the chain.
Memway: in-memorywaylaying acceleration for practical rowhammer attacks against binaries

The Rowhammer bug is a novel micro-architectural security threat, enabling powerful privilege-escalation attacks on various mainstream platforms. It works by actively flipping bits in Dynamic Random Access Memory (DRAM) cells with unprivileged instructions. In order to set up Rowhammer against binaries in the Linux page cache, the Waylaying algorithm has previously been proposed. The Waylaying method stealthily relocates binaries onto exploitable physical addresses without exhausting system memory. However, the proof-of-concept Waylaying algorithm can be easily detected during page cache eviction because of its high disk I/O overhead and long running time. This paper proposes the more advanced Memway algorithm, which improves on Waylaying in terms of both I/O overhead and speed. Running time and disk I/O overhead are reduced by 90% by utilizing Linux tmpfs and inmemory swapping to manage eviction files. Furthermore, by combining Memway with the unprivileged posix fadvise API, the binary relocation step is made 100 times faster. Equipped with our Memway+fadvise relocation scheme, we demonstrate practical Rowhammer attacks that take only 15-200 minutes to covertly relocate a victim binary, and less than 3 seconds to flip the target instruction bit.
PENDEKATAN WEBBED LEARNING PADA PEMBELAJARAN TEMATIK TERHADAP PESERTA DIDIK INKLUSIF DI MADRASAH

This paper presents the implementation of the webbed learning model on thematic learning on inclusive learners in Madrasah Ibtidaiyah Negeri 1 Probolinggo. Gathering normal students with inclusive students in the madrasah has many positive effects, such as arising from feeling nurturing, loving, and respecting between normal students and students with special needs. There is no treatment to choose a teacher's love for students' physical and mental differences, all must be equal and fair. Therefore, in order to streamline learning, a webbed learning approach is needed in the design of learning in heterogeneous classes. This research uses qualitative with case study. The results showed that the application of the webbed learning learning model on thematic learning of inclusive students at MIN 1 Probolinggo, used the scientific approach and active learning with the principle of joyful learning, which was carried out through; first, integration through student integration, second, integrated learning on the material, third, determining student activities, fourth, studying media and learning resources, fifth, culminating (sharing) in the form of learning evaluation.
Competing With Bitcoin - Some Policy Considerations for Issuing Digitalized Legal Tenders

The proliferation of peer-to-peer virtual alternatives to traditional banknotes has raised concerns among policymakers about the future of traditional means of making payments and how it might affect monetary policy implementation and its effectiveness. This study provides a brief overview of the existing research in this area. It compares positions taken in the literature by authors on some of the key policy issues relevant for central banks when thinking about the issuance of digitalized legal tenders. We examine the implications of government issued digital alternatives to traditional currencies for monetary policy effectiveness, payments and settlements, and financial market stability. We also discuss recent advances in financial technology to improve the making of payments and settlements, which might help contribute to financial inclusion. At the same time, new technologies represent challenges for regulatory authorities, for instance related to efforts to contain anti-money laundering and prevent financing of terrorism. A number of authors argue that government issued digital currency is necessary to address the flaws in private crypto currencies, and to improve monetary policy effectiveness. Central banks have begun to analyze possible features of digitalized legal tenders, to better understand the policy considerations involved and effects these could have for interest rate transmission and financial markets, but there is no clear consensus on key modalities associated with digitalized legal tenders. Moreover, many central banks do not regard privately issued virtual currencies as a serious threat to traditional currencies. Given the ongoing debate, it is difficult to make firm predictions about the impact of central bank issued digital currencies on monetary policy transmission and financial markets at this point.
TMSA: A Mutual Learning Model for Topic Discovery and Word Embedding

Both topic modeling and word embedding map documents onto a low-dimensional space, with the former clustering words into a global topic space and the latter into a local continuous embedding space. In this study, we propose the TMSA framework to unify these two complementary patterns by the construction of a mutual learning mechanism between word-cooccurrence based topic modeling and autoencoder. In our model, word topics generated with topic modeling are passed into auto-encoder to impose topic sparsity so that auto-encoder can learn topic-relevant word representations. In return, word embedding learned by autoencoder is sent back to topic modeling to improve the quality of topic generations. Empirical studies show the effectiveness of the proposed TMSA model in discovering topics and embedding words.
Network Representation Learning Enhanced Recommendation Algorithm

With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.