Reviewerly/exHarmony huggingface dataset:
Consensus Algorithms Blockchain: A comparative study

A blockchain is a Distributed Ledger Technology that has been defined as a â€œdistributed, shared, encrypted database that serves as an irreversible and incorruptible repository of information. Blockchain can be defined as a peer-to-peer distributed ledger that is cryptographically secure, append-only, immutable and updatable only via consensus or agreement among peers. In blockchain platforms, each transaction in the public ledger is verified by consensus of the majority of the system participants in a transparent and secure way. The consensus algorithm refers to the process of attaining an unified agreement on the state of the network in a decentralized way and to facilitate the verification and validation of information being added to the blockchain. This paper aims at providing a comparison between most of the recent consensus algorithms regarding the scalability of the algorithm; the type of blockchain, node identity, the performance of the algorithm (in terms of throughput &amp; latency) and Adversial Tolerance and to deliver a solid basis for discussions about current statistics. In this research, we also presented a new category of the Blockchain consensus algorithms, which consist of three groups as follows; the proof based on Hardware, the proof based on stake, and the proof based on voting.
MARKETING STRATEGY FOR THE DETERMINATION OF STAPLE CONSUMER PRODUCTS USING FP-GROWTH AND APRIORI ALGORITHM

The demand for staple products that vary among customers makes it necessary for the store to determine how the marketing strategy should be. Data mining are known as KDD (Knowledge Discovery in Database) is to digging up valuable knowledge from the data. Research purpose is to identify the right marketing strategy to sales the goods. The marketing strategy is took by analyze how much consumers demand for basic needs. The algorithms used in this research are FP (Frequent Pattern)-Growth and A-priori Algorithm. Finding combinations patterns between itemset using the Association Rule. FP-Growth algorithm is an algorithm that been used to determining a set of data in a data set that often appears on the frequency of the itemset. the KDD stages study are data cleansing, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge presentation. the Testing used Rapidminer software with a minimum confidence value of 0.6 and a minimum support of 0.45. FP-Growth algorithm obtained 5 rule conclusions while Apriori Algorithm obtained 3 rule conclusions. The FP-Growth algorithm make a better decision rules than a priori algorithms in determining of marketing strategies, because it produces more decisions on how the goods sold.
Image classification of malaria using hybrid algorithms: convolutional neural network and method to find appropriate K for K-nearest neighbor

&lt;span&gt;This paper presents image classification algorithms to improve the learning rate and to comparison the classification efficiency. Using convolutional neural network (CNN) for feature extraction and method to find appropriate k for k-nearest neighbor (KNN). Medical datasets were used in the experiments to classify &lt;span&gt;Plasmodium Vivax and Plasmodium Falciparum. Results of the study indicated that for Plasmodium Vivax in ring form, the appropriate k was 1 and the learning rate (LR) was 83.33%, Trophozoite (k=5, LR=91.67%),&lt;/span&gt;&lt;/span&gt;&lt;span&gt; Schizont (k=1, LR=83.33&lt;span&gt;%&lt;/span&gt;), and Gametocyte (k=1, LR=&lt;span lang="AR-SA" dir="RTL"&gt;91.67&lt;/span&gt;&lt;span&gt;%&lt;/span&gt;) whereas &lt;/span&gt;&lt;span&gt;Plasmodium Falciparum in ring form&lt;/span&gt;&lt;span&gt; (k=7, LR=91.67%)&lt;span&gt;,&lt;/span&gt; Trophozoite (k=1, LR=83.33%), Schizont (k=1, LR=91.67%) and Gametocyte (k=1, LR=100%).&lt;/span&gt;
Vision-based highway traffic accident detection

The highway traffic scene is relatively simple, the traffic flow is small but the speed of traffic is fast. The traffic accidents on highways are sudden and harmful. Based on highway traffic monitoring video, this paper uses machine learning to accurately detect vehicles on the highways and extract the vehicle trajectory. Then, the background extraction algorithm is used to extract the highway lane boundary lines. According to the change of vehicle trajectory and the position relationship between vehicle trajectory and lane boundary lines, the occurrence of highway traffic accidents can be detected. The experimental results show that the proposed algorithm can achieve good results in traffic accident detection rate and false alarm rate, and can accurately detect the occurrence of highway traffic accidents under different lighting conditions and traffic environment with strong robustness.
Is Data Dark? Lessons from Borges’s “Funes the Memorius”

In Funes the Memorius Jorge Luis Borges tells the tale of an Argentinian man who falls off a horse, becomes paralyzed, but with his misfortune acquires the strange gift of infinite memory. Funes remembers everything, which is to say he forgets nothing. l will use Borges' story as the backdrop for my response to Professor Selwyn.&#x0D; My commentary is in three parts. First, I begin by stating some core areas of agreement, of which there are many. Second, I examine Selwyn’s use of the word “data”. I argue that it perpetuates a number of common misconceptions about statistics and the scientific method. We cannot understand the importance of learning analytics without first clarifying these misconceptions and moving beyond them. In the course of my argument I challenge Selwyn’s central thesis that “Education is inherently social, inherently contextual, inherently subjective; it means you can’t objectively rate it, measure it, indicate it.” Third, I turn the table on Selwyn. As a critic of learning analytics Selwyn suggests that data “disadvantages large numbers of people”. I argue that the root problem in education is the status quo, which Selwyn unwittingly represents, and not learning analytics. If we care about equity in education, as part of a broader interest in social justice, then learning analytics and the use of educational data can be a powerful instrument for empowering the disadvantaged.
Unsupervised machine learning to analyze City Logistics through Twitter

City Logistics is characterized by multiple stakeholders that often have different views on such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analyzing social media content could help in understanding the public perception of City Logistics. This paper offers a methodology for collecting content from Twitter and implementing machine learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.
The Digital Competence of Future Teachers: Self-Assessment in the Context of Their Development

In the context of a transforming school of the 21st century and in the context of the digital transformation of the economy and education globally, the digital competence of pedagogical specialists is crucial for the implementation of the new professional roles of the teacher in response to the ever-increasing requirements for it. This article examines the framework of teachers’ digital competency by specifying the main competencies that guarantee their full practical experience. The empirical study presents a self-assessment of the digital competence of future pedagogical specialists trained in the professional field of pedagogy, as well as highlights from their real possibilities of solving specific practical tasks based on the application of information and communication technologies. Some recommendations have been made to rethink the education of future pedagogical specialists and the professional qualification of current teachers in the context of the formation and development of their digital competence.
A Framework for Assessing Security Strength of Cloud Computing Service

Cloud computing is mentioned to evolve dynamically and cloud transformation is getting easier all the time. Different cloud aspects are emerging in an efficient manner and have the potential to transform the traditional way of computing. With the advent of data sharing in cloud computing, the demand for outsourcing data has rapidly increased in the last decade. However, several security and privacy challenges exist impeding the acceptance of cloud computing. A highly secure system is required to guard an organizational entity, its resources, and assets. In this article, we propose and implement a novel architecture, the first of its kind, providing an enhanced level of security for outsourcing data in a cloud computing environment consisting of multiple independent cloud providers. The framework consists of dual encryption combining Homomorphic encryption at the client end and Blowfish cryptographic technique at the server side for authorization. The diverse security issues associated with information integrity, security, confidentiality, and authenticationmust be addressed. Simulations and analysis were performed on an Oracle virtual machine Virtual-Box and a Fog environment on Ubuntu 12.04 platform. Extensive safety measures and performance analysis considering the Encrypted file size and Encryption time demonstrate that our projected proposal is vastly proficient and satisfies the requirements for secure data sharing.
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model.Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand.However, since it differs from users to users what they are interested in, it is necessary to develop a method to generate various summaries according to users' requests.We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors.In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei 225), as time-series data, and daily summaries of Nikkei Quick News as textual data.We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.Experiments show both models using additional information of target document achieved higher performance in terms of BLEU and human evaluation.We found that human-designed topic labels are superior to extracted keywords in terms of controllability.
Zac: Towards Automatic Optimization and Deployment of Quantized Deep Neural Networks on Embedded Devices

With the development toward commercial and civil use, the need for deploying Deep neural network (DNN) models on resource-constrained embedded devices is growing. Quantization has a para-mount impact on the performance, storage, and energy efficiency. However, to fully realize these benefits, programmers need to manually utilize low precision operations while maintaining accuracy, which is very challenging. Hence, we present a framework Zac to automatically optimize and deploy quantized DNN models on embedded devices. In order to do this, Zac performs necessary data type conversion and chooses proper data types for the intermediate data. Then it automatically customizes the operations according to the chosen types. Experiments demonstrate that by utilizing quantized models, Zac offers up to 19.18X and 25.44X improvement for throughput and energy efficiency compared with full precision designs, respectively. The automatically generated hardware designs from Zac achieve comparable performance to the highly optimized state-of-the-art accelerators which are designed manually.
Maximum Margin Multi-Dimensional Classification

Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. Due to the heterogeneity of class spaces, the major difficulty in designing margin-based MDC techniques lies in that the modeling outputs from different class spaces are not comparable to each other. In this paper, a first attempt towards maximum margin multi-dimensional classification is investigated. Following the one-vs-one decomposition within each class space, the resulting models are optimized by leveraging classification margin maximization on individual class variable and model relationship regularization across class variables. We derive convex formulation for the maximum margin MDC problem, which can be tackled with alternating optimization admitting QP or closed-form solution in either alternating step. Experimental studies over real-world MDC data sets clearly validate effectiveness of the proposed maximum margin MDC techniques.
Temporal Pyramid Recurrent Neural Network

Learning long-term and multi-scale dependencies in sequential data is a challenging task for recurrent neural networks (RNNs). In this paper, a novel RNN structure called temporal pyramid RNN (TP-RNN) is proposed to achieve these two goals. TP-RNN is a pyramid-like structure and generally has multiple layers. In each layer of the network, there are several sub-pyramids connected by a shortcut path to the output, which can efficiently aggregate historical information from hidden states and provide many gradient feedback short-paths. This avoids back-propagating through many hidden states as in usual RNNs. In particular, in the multi-layer structure of TP-RNN, the input sequence of the higher layer is a large-scale aggregated state sequence produced by the sub-pyramids in the previous layer, instead of the usual sequence of hidden states. In this way, TP-RNN can explicitly learn multi-scale dependencies with multi-scale input sequences of different layers, and shorten the input sequence and gradient feedback paths of each layer. This avoids the vanishing gradient problem in deep RNNs and allows the network to efficiently learn long-term dependencies. We evaluate TP-RNN on several sequence modeling tasks, including the masked addition problem, pixel-by-pixel image classification, signal recognition and speaker identification. Experimental results demonstrate that TP-RNN consistently outperforms existing RNNs for learning long-term and multi-scale dependencies in sequential data.
Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms

For robots to successfully operate as members of human-robot teams, it is crucial for robots to correctly understand the intentions of their human teammates. This task is particularly difficult due to human sociocultural norms: for reasons of social courtesy (e.g., politeness), people rarely express their intentions directly, instead typically employing polite utterance forms such as Indirect Speech Acts (ISAs). It is thus critical for robots to be capable of inferring the intentions behind their teammates' utterances based on both their interaction context (including, e.g., social roles) and their knowledge of the sociocultural norms that are applicable within that context. This work builds off of previous research on understanding and generation of ISAs using Dempster-Shafer Theoretic Uncertain Logic, by showing how other recent work in Dempster-Shafer Theoretic rule learning can be used to learn appropriate uncertainty intervals for robots' representations of sociocultural politeness norms.
PRACTICING CONTEXTUAL TEACHING AND LEARNING (CTL) APPROACH TO IMPROVE STUDENTS� READING COMPREHENSION IN RELATION TO MOTIVATION

The need to acquire reading skills is increasing along with the needs of students to attain information. Therefore, this study aims at improving student�s reading comprehension viewed from the motivation level using the contextual teaching and learning (CTL) approach at SMPN 1 Jonggat. To this end, a classroom action research consisting of two cycles was applied. Each cycle consisted of four stages, namely plans, actions, observations, and reflections. The subject of this study was the second-grader of SMPN 1 Jonggat, while the object of this study was the use of contextual teaching and learning (CTL) approach to improve students� reading comprehension in relation to motivation. The observation sheets, questionnaires, and reading tests were employed to collect the data. As result, the study showed that the use of CTL approach was effective to improve students� reading comprehension and motivation to learn. Students� achievement was 75 meaning that the passing grade enacted in the school was achieved. Meanwhile, the students� motivation to learn was at the high level as proven by the percentage of students� motivation which was at the standard value. Thus, it can be concluded that Contextual Teaching and Learning (CTL) approach can improve students� motivation to learn as well as their reading comprehension.
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: simile sentence classification and simile component extraction. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT. Source Code of this paper are available on https://github.com/DeepLearnXMU/Cyclic.
Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents

We extend goal recognition design to account for partially informed agents. In particular, we consider a two-agent setting in which one agent, the actor, seeks to achieve a goal but has only incomplete information about the environment. The second agent, the recognizer, has perfect information and aims to recognize the actor's goal from its behavior as quickly as possible. As a one-time offline intervention and with the objective of facilitating the recognition task, the recognizer can selectively reveal information to the actor. The problem of selecting which information to reveal, which we call information shaping, is challenging not only because the space of information shaping options may be large, but also because more information revelation need not make it easier to recognize an agent's goal. We formally define this problem, and suggest a pruning approach for efficiently searching the search space. We demonstrate the effectiveness and efficiency of the suggested method on standard benchmarks.
Analytic Continued Fractions for Regression: A Memetic Algorithm Approach

We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming methods for symbolic regression and ten machine learning methods. The comparison on training and test generalization was performed using 94 datasets of the Penn State Machine Learning Benchmark. The statistical tests showed that the generalization results using analytic continued fractions provides a powerful and interesting new alternative in the quest for compact and interpretable mathematical models for artificial intelligence.
A New Improved Baum-Welch Algorithm for Unsupervised Learning for Continuous-Time HMM Using Spark

Hidden Markov Models are widely used for time continuous problems modelling and prediction.This paper presents two new improved algorithms for Gaussian continuous and mixture of Gaussian continuous Hidden Markov Models cases for solving learning problem for large scale multidimensional data.The design of our parallel distributed algorithms is based on Spark, the Big Data framework, thereby we can distribute data over several nodes through Resilient Distributed Datasets which allow to apply, in parallel, a set of operations.The proposed algorithms have two main advantages: a high computational time efficiency and a high scalability as well as an easy integration in Big Data frameworks.The complexity comparison results show great improvements in computational complexity (by a factor of (states number) 2 ) and execution time.Moreover, the new algorithms might be more effective by reducing the communication costs between the elements of the system involved in the learning task.
Electronic Information-Educational Environment As A Quality Of Life Improvement Factor

The article discusses the issues of improving the quality of life in the region. An important component of the quality of life is the quality of education. As one of the ways to improve the quality of education the authors see the development of the electronic information and educational environment in the educational organization. The article considers the concept of “quality of education” as a component of the quality of life of the population; the role and capabilities of the electronic information and educational environment as a factor for the development of the educational organization itself and the improvement of the quality of education. The article also presents the results of a survey of graduates and participants of additional education programs at one of the universities in Russia. This questionnaire contained questions regarding satisfaction with the quality of educational services in the context of the basis for improving the quality of life in the region, namely: satisfaction with the quality of education in general, the contents of programs, compliance with the requirements of the modern market, and the quality of certain educational services. Also, the research questions were aimed at identifying satisfaction with the professional competence of teachers, different university facilities and premises, equipment of the educational process, class schedules, food quality, and living conditions in the dormitory. The relationship between satisfaction with the quality of education and the geography of employment of university graduates and participants of additional education programs was analyzed.