Reviewerly/exHarmony huggingface dataset:
From API to NLI: A new interface for library reuse

Developers frequently reuse APIs from existing libraries to implement certain functionality. However, learning APIs is difficult due to their large scale and complexity. In this paper, we design an abstract framework NLI2Code to ease the reuse process. Under the framework, users can reuse library functionalities with a high-level, automatically-generated NLI (Natural Language Interface) instead of the detailed API elements. The framework consists of three components: a functional feature extractor to summarize the frequently-used library functions in natural language form, a code pattern miner to give a code template for each functional feature, and a synthesizer to complete code patterns into well-typed snippets. From the perspective of a user, a reuse task under NLI2Code starts from choosing a functional feature and our framework will guide the user to synthesize the desired solution. We instantiated the framework as a tool to reuse Java libraries. The evaluation shows our tool can generate a high-quality natural language interface and save half of the coding time for newcomers to solve real-world programming tasks.
A Semantic Approach of the Naïve Bayes Classification Algorithm

This study aims to study the semantic approach of Naïve Bayes Classification Algorithm.From a statistical, probabilistic machine learning model, the classical decision-level classification algorithm which is the Naïve Bayes classifier shows to be efficient on a variety of sentiment classification problems.Naive Bayes is often used in sentiment classification applications and practical experiments because of its simplicity and effectiveness.However, its performance is often degraded because of the reliability of the result.This paper focuses on developing a different approach to the primary sentiment analysis of the NB classifier.The approach leads to the implementation of providing semantic information from lexicon resources together with the semantic calculator.Addressing the problems of the baseline algorithm show that it can be solved by incorporating other algorithm approaches.The comparative results show that the semantic approach is statistically superior and yielded improvements to the baseline classifier.In comparison with the baseline NB algorithm, SNB achieved a relatively favorable classification accuracy with the threshold of 50% to 60%and an average improvement of 11.394% for its accuracy rating while significantly reducing the training time.
A Comparison of Machine Learning Classifiers for Human Activity Recognition using Magnetic Induction-based Motion signals

Human activity recognition (HAR) is a growing research field with a wide range of applications. Magnetic induction-based human activity recognition system (MI-HAR) is a wearable-based HAR system proposed for capturing human motions and detecting activities based on the collected data. In this work, we focused on the performance analysis of different machine learning classifiers using synthetic magnetic inductionbased motion (MI-motion) signals. The main aim of this analysis is to compare the performances of six commonly used classifiers for HAR applications. Furthermore, we compared the classification performance using MI-motion data with the result reported in other studies using accelerometer data correspond to the same actions. Our results showed that Random Forest obtained the best performance of 91.5% on MI-motion data. Also, k-SVM and KNN models have respectively achieved accuracy of 91.4% and 86.4% on MI-motion data, which are both higher than the reported accuracy of 85.4% and 81.75 on accelerometer data.
Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks

Abstract The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches.
Evolving developmental neural networks to solve multiple problems

We describe a neural model in which two evolved neural programs undergo development and form neural networks. The programs decide whether neurons and their dendrites move, change, die or replicate....
Social Media and Women’s Culture of Silence on Sexual Violence: Perception of Babcock University’s Female Undergraduates

The study examines the perception of female undergraduates’ about the role of social media in breaking women’s culture of silence on sexual violence. Violence against women is a subject of concern due to its prevalence and the culture of silence associated with it. Involvement of social media groups appears to be changing this trend as women are supported to speak out. The study examines some social media platforms involved in this campaign, how they contribute to knowledge of females and shape their attitude to information on sexual violence on social media as well as their perception of the effectiveness of the social media in the campaign against sexual violence among women. Anchored on two theories - agenda setting and the spiral of silence, the study through a descriptive survey, sampled the perception of 370 female undergraduates of Babcock University. Responses derived from a questionnaire were analyzed using descriptive statistics. Students were moderately exposed to campaigns on sexual violence (Mean= 3.32, SD=1.49), just as majority have a positive perception of the social media’s role in breaking the culture of silence on sexual violence (x̅= 5.00, SD=0.00). The need to intensify efforts in reaching more females especially those who still have limited knowledge and tend to be silent was underscored.
Automatic Labeled Dialogue Generation for Nursing Record Systems

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.
Load Balancing Algorithm on Cloud Computing for Optimize Respone Time

To improve the performance of cloud computing, there are many parameters and issues that we should consider, including resource allocation, resource responsiveness, connectivity to resources, unused resources exploration, corresponding resource mapping and planning for resource.The planning for the use of resources can be based on many kinds of parameters, and the service response time is one of them.The users can easily figure out the response time of their requests, and it becomes one of the important QoSs.When we discover and explore more on this, response time can provide solutions for the distribution, the load balancing of resources with better efficiency.This is one of the most promising research directions for improving the cloud technology.Therefore, this paper proposes a load balancing algorithm based on response time of requests on cloud with the name APRA (ARIMA Prediction of Response Time Algorithm), the main idea is to use ARIMA algorithms to predict the coming response time, thus giving a better way of effectively resolving resource allocation with threshold value.The experiment result outcomes are potential and valuable for load balancing with predicted response time, it shows that prediction is a great direction for load balancing.
Bitcoin’s Blockchain Peer-to-Peer Network Security Attacks and Countermeasures

Objectives:The main objective of this work is familiarizing users and researchers about Bitcoin's blockchain peer-to-peer network system and investigating security attacks that threat this critical financial digital cash network.Method: A comprehensive research analysis was conducted to identify Bitcoin's blockchain peer-to-peer network security attacks and possible countermeasures to protect the Bitcoin network against such attacks.This bibliographical survey covers the related research works from the launch of blockchain in 2008 until the end of 2019.Results: This study investigates eleven attacks that threaten Bitcoin's blockchain peer-to-peer network systems and presents the possible countermeasures to defend these attacks.Conclusion: The conclusion obtained is encouraging the researchers to explore this hot research area.Besides, the study provides perspectives for future research directions in this domain.
Attention-based Model for Evaluating the Complexity of Sentences in English Language

The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep-learning-based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in two different languages: Italian and English.
Deep Knowledge Tracking based on Attention Mechanism for Student Performance Prediction

Due to the widespread application of online tutoring systems today, researchers have developed a strong interest in using online tutoring systems to improve students' learning performance. Researchers can use deep knowledge tracking methods to model students' learning performance based on their historical learning records, recommend learning resources to students based on their learning performance, and develop personalized learning strategies. At present, some researches on deep knowledge tracing using exercise text information and have achieved good results. But this method needs to rely on the complete text of the exercise, it will cause problems in difficult to extract the concept of the exercise, and due to the reason of intellectual property and privacy protection complete exercise texts are difficult to obtain. Therefore, in this study, we use a small amount of concept information, combined with the attention mechanism, to improve deep knowledge tracking, and to promote the ability of prediction student learning performance. The experimental results show that the method proposed in this paper has some improvements in AUC and ACC.
ANALISIS FAKTOR PENYEBAB KESULITAN BELAJAR MATEMATIKA DI SEKOLAH DASAR

Abstract. This research was conducted in grade IV of SD Negeri 2 Penambongan and was aimed at investigating the characteristics of students' learning difficulties, the factors causing difficulties in learning Mathematics, and the efforts conducted to resolve the difficulties of learning Mathematics. This research applied the qualitative approach with a case study design. The participants of the research were principal, class teacher, three grade IV students (chosen by the teacher's recommendation), and parents. The data were collected through observations, interviews, and document collections. The results of the data analysis showed that 1) the characteristics of difficulties in learning Mathematics experienced by the students was the abnormality of visual-spatial perception, 2) the internal factors causing difficulties in learning Mathematics were student's attitude, learning interest, motivation, and ability of sensing, while the external factors causing difficulties in learning Mathematics were learning strategies, learning aid, family, and society, 3) the efforts conducted by teachers to resolve the difficulties in learning Mathematics were motivating and supporting students when finding difficulties, the efforts conducted by students were joining extra courses out of school, and the efforts conducted by parents were motivating and accompanying their children when studying at home.
Financial instruments entail liabilities: Ether, bitcoin, and litecoin do not

The financial assets that are subject to major EU financial legislation (i.e. (designated types of) financial instruments) have traditionally been defined in a largely exemplary and circular manner. The recent proliferation of ‘non-traditional’ financial assets, such as cryptocurrencies and stablecoins, is increasingly challenging the viability of these pragmatic financial asset definitions. Through the analysis of the technologies and functionalities underpinning non-traditional financial assets, legal scholarship has aimed to categorize novel assets within the existing framework of financial asset definitions. Although a solid understanding of e.g. distributed ledger applications and cryptography appears a prerequisite for future policy and legislative interventions, contemporary EU financial legislation is mostly indifferent to the technologies on which financial assets may be wired. Categorizations based on the purposes that non-traditional assets may serve (i.e. payment, utility, and investment) are more relevant to financial law, but suffer from subjectivity because they depend on the asset usage by the asset holder. Against this backdrop, this paper proposes a novel systematization of non-traditional assets that is based upon the conceptual substructure of the assets within the scope of EU financial legislation. More specifically, this paper submits that, irrespective of underlying technologies and functionalities, all assets that are subject to major EU financial legislation have a conceptual common denominator: they entail the liability of an entity and, hence, have intrinsic value. The proposed categorization singles out a well-defined group of novel financial assets that is not subject to EU financial law (i.e. assets that only have extrinsic value). Different from functionality- and technology-based categorizations, the suggested approach allows to eradicate some ambiguities that are present in the existing taxonomies. By exploring the conceptual common denominator of the financial assets that are subject to EU financial legislation, this paper aims to foster debate on the circular and exemplary character of financial asset definitions in EU financial legislation in general and the relation of these definitions to novel types of financial assets in particular.
Gender under-representation in teaching: A casualty of the feminisation of teaching?

Much has been written on the relationship between gender and schooling, and teaching. In particular, the focus has been on the significant dis-proportion between female and male teachers, which remarkably, inverts in relation to educational leadership positions. Arguments abound as to the social factors and hegemonies, which have created not only what is referred to as the “feminisation of teaching”, but the risks associated with feminised classrooms and pedagogies, particularly, in relation to boys. Not surprisingly, therefore, an equally dominant narrative calling for the (re)masculinisation of teaching has gained increasing momentum. The basis for this argument is not limited to a seeming need for male teachers as role-models. More disturbing, is the idea that the standing of the profession – invariably measured in terms of salary – might improve if more males are encouraged to become teachers. Our interest in this article is in the patriarchal hegemonies, which give shape to constructions of teaching as “women’s work” in the first place. We argue, that rather than contributing to what has been an overdue preoccupation gender-based socialisations, the concern should be on ensuring that teaching transcends framings of “feminisation” and “masculinisation” so that it serves all teachers and all learners.
Smart toys, smart tangibles, robots and other smart things for children

Embedding electronics and enabling digital responses to everyday objects is already a widespread reality. The term smart toys, tangibles and robots cover a range of play products that can present different levels of network, processing and reasoning capabilities. Such interfaces have also become capable of collecting and processing data in realtime. While quality aspects and intelligent features are more relevant than ever, many relying on data-driven methods and artificial intelligence (AI), this also raises important privacy issues and concerns. Additionally, the design of meaningful play and learning experiences that go beyond data collection remains a challenging task. This workshop aims to explore challenges and related opportunities to feed interaction design of smart toys, tangibles, robots and other smart things for children that may shape the lines for future work in the field. It aims at bringing together researchers, non-academicians and, practitioners from relevant disciplines to discuss potentials and limitations of using various smart technologies for children, to define required methodologies, and to envision alternative future scenarios that fully unleash the potential that the new generation of AI-powered smart tangibles and related devices may bring.
Ensuring Pluralism in Social Media Markets: Some Suggestions

Social media platforms have created unprecedented possibilities to communicate, access and share information online. However, they have also raised various challenges for both individuals and society. One is how they impact the plurality and diversity of media that each user is exposed to. This paper attempts to shed light on the current challenges concerning pluralism and content moderation in social media markets. It does so by identifying the relevant market failures and the likely theory of harm, and it suggests two possible solutions: the first is a form of regulated pluralism; the second is the unbundling of hosting and content moderation activities.
An optimal bit-rate allocation algorithm to improve transmission efficiency of images in deep space exploration

As fixed compression ratio is used in traditional deep space exploration image transmission application, the same compression code rate is allocated to each image. However, since the information of each image in a space observation mission is nonuniform, the image with more information will inevitably lead to more compression distortion than the image with less information. Obviously, it's not an efficient way to transmit information in terms of data importance or overall distortion. Therefore, we proposed a combinatorial optimal bit rate allocation algorithm to improve the efficiency of image transmission in space application. Different from traditional method, the rate-distortion model of wavelet coefficients for each image in a transmission task was built, and under the overall maximum transmission rate constraint, an bit-rate optimal allocation was applied for each image to minimize the overall distortion of a batch of images. The proposed algorithm can be widely used in image compression algorithm with embedded code stream characteristics, such as JPEG2000 and SPIHT. Experimental results shows that in the range of 2.6 to 10 compression ratio, the algorithm can reduce image distortion MSB by 40%∼64% at the same overall transmission code rate, which equivalent to improvement of PSNR 3 dB to 5.4 dB.
Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm

Hindi is the widely used spoken language in the Indian subcontinent, and is used by more than 260 million Indians citizens.Indian governments has many digital initiatives to serve Indian citizen better, hence Hindi language becomes one of the important languages to serve Indian citizen.The Government initiatives are like smart city, Hospital Services, Common Service Centers, Digital Payment Ecosystem, Pensioners Scheme, Digital Locker and many more.These all initiative are served using mobile and web based applications, which citizens can access easily instead of visiting various government departments.To serve the large Hindi speaking population, it is necessary to handle the ambiguous words which have multiple connotations in any natural language processing task.In this paper, word sense disambiguation for Hindi language is proposed.Proposed method makes use of Lesk algorithm to disambiguate the Hindi words.Novel scoring method is used to assign a sense score to each token of the Hindi sentence.The sense score is calculated based on the gloss, hypernym, hyponym and synonym of the combinations of different sense of tokens.Hindi WordNet database created by CFILT, IIT Bombay is used in the proposed system.The proposed algorithm takes a natural language (NL) sentence in Hindi (Devanagari script) and process the sentence according to the score based approach modeled on the basic Lesk algorithm with the help of Hindi WordNet designed by CFILT IIT Bombay.The solution provided in this paper can be used vividly in various web based applications like Query-Response Systems, Question-Answer Systems, Sentiment analysis, Recommendation systems etc.