Reviewerly/exHarmony huggingface dataset:
A Deep Meta-learning Framework for Heart Disease Prediction

Heart disease is a leading cause of death. This is proved by the high mortality rates annually published. The factors and symptoms of this disease which are significantly common among other diseases, grabbed the attention of the scientific community to integrate advanced tools and computing systems such as Machine Learning (ML), and Artificial Neural Networks (ANN) to early diagnose and predict this disease. Among recent research work, Restricted Boltzmann Machine (RBM) formed a viable model to solve similar problems in the medical domain. Therefore, we introduce this paper to present a heart disease classification system based on RBM. In this paper, we relied on a standard dataset, namely the Cleveland dataset to conduct our experimental work. In addition, we developed a meta-learning framework to train stacked RBM classifiers, where we use the same type of classifier with different levels of abstractions of the data to get several classifications. The experimental results show that our proposed model achieves an accuracy that is on par with the state-of-the-art.
Application of Artificial Intelligence to Higher Vocational English Teaching in the Information Environment

Abstract In the information environment, artificial intelligence (AI) has begun to penetrate into college English teaching, where a vast corpus is provided for learners to practice college English skills through the AI platform. AI has offered more learning resources and changed the learning styles of learners. It can analyze the personal characteristics of learners, design suitable learning approaches, select the reasonable corpus, and match the corresponding learning resources, thereby making college English learning more efficient and personalized.
GFocus: User Focus-based Graph Query Autocompletion

Graph query autocompletion ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gQAC</small> ) generates a small list of ranked query suggestions during the graph query formulation process in a visual environment. The current state-of-the-art of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gQAC</small> provides suggestions that are formed by adding subgraph increments to arbitrary places of an existing (partial) user query. However, according to the research results on human-computer interaction (HCI), humans can only interact with a small number of recent software artifacts in hand. Hence, many of such suggestions could be irrelevant. In this paper, we present the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GFocus</small> framework that exploits a novel notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user focus of graph query formulation</i> (or simply <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">focus</i> ). Intuitively, the focus is the subgraph that a user is working on. We formulate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locality principles</i> inspired by the HCI research to automatically identify and maintain the focus. We propose novel monotone submodular ranking functions for generating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">popular</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">comprehensive</i> query suggestions only at the focus. In particular, the query suggestions of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GFocus</small> have high result counts (when they are used as queries) and maximally cover the possible suggestions at the focus. We propose efficient algorithms and an index for ranking the suggestions. Our results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GFocus</small> saves 12-32 percent more mouse clicks and is 35× more efficient than the state-of-the-art competitor.
Mosaic Super-resolution via Sequential Feature Pyramid Networks

Advances in the design of multi-spectral cameras have led to great interests in a wide range of applications, from astronomy to autonomous driving. However, such cameras inherently suffer from a trade-off between the spatial and spectral resolution. In this paper, we propose to address this limitation by introducing a novel method to carry out super-resolution on raw mosaic images, multi-spectral or RGB Bayer, captured by modern real-time single-shot mosaic sensors. To this end, we design a deep super-resolution architecture that benefits from a sequential feature pyramid along the depth of the network. This, in fact, is achieved by utilizing a convolutional LSTM (ConvLSTM) to learn the inter-dependencies between features at different receptive fields. Additionally, by investigating the effect of different attention mechanisms in our framework, we show that a ConvLSTM inspired module is able to provide superior attention in our context. Our extensive experiments and analyses evidence that our approach yields significant super-resolution quality, outperforming current state-of-the-art mosaic super-resolution methods on both Bayer and multi-spectral images. Additionally, to the best of our knowledge, our method is the first specialized method to super-resolve mosaic images, whether it be multi-spectral or Bayer.
A Covert Digital Communication System Using Skewed α-Stable Distributions for Internet of Things

Information security is a key issue involved in the utilization of different technological innovations, such as the Internet of Things (IoT). This study proposes a new covert communication scheme in which the transmitted waveform modulated by the covert bit is statistically similar to the background noise to achieve information security. The impulsive noise in many communication fields can be well modeled with a symmetrical α -stable distribution. Thus, the proposed scheme exploits skewed α -stable distributions to conduct the covert bit. The polarity transition of the skewness parameter within the first and second halves of a bit period represents a covert bit. The resultant signal exhibits a near-symmetrical behavior similar to ambient interference to achieve concealment. The sample skewness estimator based on the signed fractional lower order moment is used to determine the binary message at the receiver. Then, the expression of the skewness estimator and its probability density function are derived. With the aid of the hard decision, the theoretical expression of the bit error rate of the proposed communication system is obtained. The proposed scheme works at the physical layer with the advantages of simple structure, easy implementation, and extreme suitability with low-cost, resource-limited IoT devices exhibiting low-rate transmission.
A Survey on Threats and Security Solutions for IoT

Architecture of a typical Internet of Things (IoT) system, is defmed as a four layers construction as follows: Data acquisition layer, Communication Layer, Cloud computing and storage layer andFinal UserApplication layer. Based on this type of architecture we can identify various threats for each layer in part and solutions for security and integrity of data to those threats. Through those solutions, the benefits and the disadvantages of the Blockchain technology were analyzed. This paper is a survey of threats and solutions to Internet of Things security and data integrity issues classified according to Internet of Things architecture layers. As a conclusion, the classification of the threats of the Internet of Things application can give an overview of the system needs in terms of security and data integrity solutions that can be adopted, depending on the functionality that the Internet of Things system must perform.
Implementation of Distributed Crawler System Based on Spark for Massive Data Mining

In the era of rapid development of Internet technology and increasing social needs of people, web crawlers have been maturely applied to major search engines and search fields. By using Spark's RDD elastic computing architecture and task assignment algorithm, this paper integrates the architecture of Spark-based distributed crawler system, gives the corresponding framework diagram, and introduces the distributed framework system in detail. Through this Spark-based distributed crawler system we can solve the problem of insufficient resource utilization and low collection efficiency, and then solve the contradiction between the current explosive growth of data scale and the speed of obtaining information.
The Effectiveness of Mobile Application in Learning Malay Foreign Language

Abstract The use of new technologies has impacted effectiveness in learning which contributes to the smoothness of language performance. The language implication involves all subjects, including Malay foreign language. For instance, the use of mobile learning has been shown a considerable and dynamic potential in learning Malay foreign language. Mobile application usage enables a variety of teaching innovations that assist educators and students in pursuing knowledge and language skills. Thus, this concept paper aimed to identify the Learning Theory which related to mobile application. Apart from that, it is also to explore the importance and effectiveness of mobile application usage in Malay foreign language instruction. The literature review is based on publications from 2015 to the present. It is hoped this study can be used to benefit the foreign students while integrating the mobile application in their learning, and to improve the language performance in line with current developments.
Research on the Cultivation of Non-English Majors’ English Reading Interest: POA Teaching Mode in the Internet + Era

Abstract Reading plays an important role in foreign and second language learning and teaching. It not only provides sufficient and important linguistic input, but also lays a foundation for the further development in listening, speaking and writing. However, there are still many problems in college English reading instruction. It is imperative to find an effective teaching model with Chinese characteristics to change the situation. Based on analyzing the current situation of college English reading instruction and analyzing the impact of “Internet +” on it, explaining POA teaching model and its effect on non-English majors’ reading interest in the Internet + era, this study finds that POA can promote college students’ reading interest in four aspects.
Moving in rhythms: what dancers and drummers can teach us about designing digital interactions

Interviews with three dancers and three percussionists about performing, perceiving and composing rhythms are used to develop insights into ways that interaction designers might understand and shape the rhythmic experiences of their users. These insights coalesce around three themes: feeling your way into a rhythm, performing rhythms and the dance of synchrony. The article argues that a user’s experience of the rhythms within a design will play a significant role in its affective impact.
Interval Adjoint Significance Analysis for Neural Networks

Optimal neural network architecture is a very important factor for computational complexity and memory footprints of neural networks. In this regard, a robust pruning method based on interval adjoints significance analysis is presented in this paper to prune irrelevant and redundant nodes from a neural network. The significance of a node is defined as a product of a node’s interval width and an absolute maximum of first-order derivative of that node’s interval. Based on the significance of nodes, one can decide how much to prune from each layer. We show that the proposed method works effectively on hidden and input layers by experimenting on famous and complex datasets of machine learning. In the proposed method, a node is removed based on its significance and bias is updated for remaining nodes.
ABC: Abstract prediction Before Concreteness

Learning techniques are advancing the utility and capability of modern embedded systems. However, the challenge of incorporating learning modules into embedded systems is that computing resources are scarce. For such a resource-constrained environment, we have developed a framework for learning abstract information early and learning more concretely as time allows. The intermediate results can be utilized to prepare for early decisions/actions as needed. To apply this framework to a classification task, the datasets are categorized in an abstraction hierarchy. Then the framework classifies intermediate labels from the most abstract level to the most concrete. Our proposed method outperforms the existing approaches and reference baselines in terms of accuracy. We show our framework with different architectures and on various benchmark datasets CIFAR-10, CIFAR-100, and GTSRB. We measure prediction times on GPUequipped embedded computing platforms as well.
Sampling from Discrete Distributions in Combinational Hardware with Application to Post-Quantum Cryptography

Random values from discrete distributions are typically generated from uniformly-random samples. A common technique is to use a cumulative distribution table (CDT) lookup for inversion sampling, but it is also possible to use Boolean functions to map a uniformly-random bit sequence into a value from a discrete distribution. This work presents a methodology for deriving such functions for any discrete distribution, encoding them in VHDL for implementation in combinational hardware, and (for moderate precision and sample space size) confirming the correctness of the produced distribution. The process is demonstrated using a discrete Gaussian distribution with a small sample space, but it is applicable to any discrete distribution with fixed parameters. Results are presented for sampling schemes from several submissions to the NIST PQC standardization process, comparing this method to CDT lookups on a Xilinx Artix-7 FPGA. The process produces compact solutions for distributions up to moderate size and precision.
Breaking Type Safety in Go: An Empirical Study on the Usage of the unsafe Package

A decade after its first release, the Go programming language has become a major programming language in the development landscape. While praised for its clean syntax and C-like performance, Go also contains a strong static type-system that prevents arbitrary type casting and arbitrary memory access, making the language type-safe by design. However, to give developers the possibility of implementing low-level code, Go ships with a special package called unsafe that offers developers a way around the type-safety of Go programs. The package gives greater flexibility to developers but comes at a higher risk of runtime errors, chances of non-portability, and the loss of compatibility guarantees for future versions of Go. In this paper, we present the first large-scale study on the usage of the unsafe package in 2,438 popular Go projects. Our investigation shows that unsafe is used in 24% of Go projects, motivated primarily by communicating with operating systems and C code, but is also commonly used as a source of performance optimization. Developers are willing to use unsafe to break language specifications (e.g., string immutability) for better performance and 6% of analyzed projects that use unsafe perform risky pointer conversions that can lead to program crashes and unexpected behavior. Furthermore, we report a series of real issues faced by projects that use unsafe, from crashing errors and non-deterministic behavior to having their deployment restricted from certain popular environments. Our findings can be used to understand how and why developers break type-safety in Go, and help motivate further tools and language development that could make the usage of unsafe in Go even safer.
SketchADoodle: Touch-surface Multi-stroke Gesture Handling by Bézier Curves

Touch-surface multi-stroke gestures, as well as freehand drawings, are typically acquired by devices and sensors as a suite of timestamped points on a plane. This Cartesian coordinate system, although useful for computation like complexity analysis, gesture classification and recognition, becomes complex and inefficient when gestures need to be visualized and directly manipulated for editing. To address these challenges, a new mathematical representation of these gestures via a Bézier curve is defined to initiate a model-based approach for gesture direct manipulation (e.g., cut, copy, paste, translate, scale, rotate, deform, crop, compose, decompose). SketchADoodle, an Android-based mobile application for drawing, gesturing, demonstrates how the pseudo-code of the Bézier-based operations are engineered for real-time direct manipulation. We release the programming code for further development of gesture-based user interfaces based on Bézier curves.
Sentiment Analysis of Tweets- A Comparison of Classifiers on Live Stream of Twitter

Social media is gaining huge attention in today's era due to its increased accessibility by individuals. Wherein, a mere tweet can reflect upon an individual's right to say and express any sort of political, emotional, intellectual views disregarding the fact of how impactful of an impression it leads on the minds of society. Social media nowadays has become a solid platform for young minds to make an interaction with the outside world without having to understand the consequences. Therefore, it can have its pros and cons. Twitter is one such social media site used by people to ventilate their sentiments by posting messages popularly called "Tweets". The sentiments expressed over tweets can be categorized into positive, negative, and neutral. Thus, our model reflects on the sentiment analysis using NLP and various Machine Learning classifiers. Herein, we have compared various ML classifiers using the dynamic dataset which brings novelty in our findings.
Beyond Open Data: A Model for Linking Digital Artifacts to Enable Reproducibility of Scientific Claims

The last few years has seen a substantial push toward "Open Data" by policy makers, researchers, archivists, and even the public. This article postulates that the value of data is not intrinsic but instead derives from its ability to produce knowledge; the extraction of which from data is not deterministic. The value of data is realized through a focus on the reproducibility of the findings from the data, which acknowledges the complexity of the leap from data to knowledge, and the inextricable interrelationships between data, software, computational environments and cyberinfrastructure, and knowledge. Modern information archiving practices have a long history and were shaped in a pre-digital world comprised of physical objects such as books, monographs, film, paper, and other physical artifacts. This article argues that "data," the modern collection of digital bits representing empirical measurements, is a wholly new entity and not a digital analog to any physical object. It further argues that a focus on the interrelationships between digital artifacts and their unique properties, instead of Open Data alone, will instead produce an augmented and more useful understanding of knowledge when it is derived from digital data. Data-derived knowledge, represented by claims in the scholarly record, must persistently link to immutable versions of the digital artifacts from which it was derived, including 1) any data, 2) software that allows access to the data and the regeneration of those claims that rely on the version of the data, and 3) computational environment information including input parameters, function invocation sequences, and resource details. In this sense the epistemological gap between data and extracted knowledge can be closed. Datasets and software are often subject to change and revision, sometimes even with high velocity, and such changes imply new versions with new unique identifiers. We propose considering knowledge, rather than data in isolation, with a schematic model representing the interconnectedness of datasets, software, and computational information upon which its derivation depends. Capturing the interconnectedness of these digital artifacts, and their relationship to the knowledge they generate, is essential for supporting the reproducibility, transparency, and cognitive tractability of scientific claims derived from digital data.
Research Status and Prospect of Deep Learning in Secondary State Monitoring of Smart Substation

The network of the secondary system of the smart substation realizes the sharing and interaction of equipment information, and also brings a large amount of secondary system status data. Conventional secondary condition monitoring methods have deficiencies in processing big data. As a research hotspot of artificial intelligence, deep learning has strong data mining capability that meets the needs of state monitoring of smart substation secondary systems. In this context, the paper first outlines the basic ideas of deep learning and the typical structure of several commonly used models. Secondly, the concept, monitoring object and index selection of secondary system monitoring in smart substation are discussed. The advantages and disadvantages of using conventional methods and deep learning in communication network condition monitoring and secondary device status evaluation are then analyzed. Finally, combined with the current research and application status of deep monitoring of secondary system status in smart substation, the future development prospects are prospected.
Crime Monitoring from Newspaper Data based on Sentiment Analysis

Crime is one of the major challenges of the world which is affecting the normal life and socio-economic development. Therefore, many governments are trying to use advanced technology to address or tackle such issues to maintain the peace of the country. So the analysis on Crime data has a great impact and value for the current scenario of the world. Nowadays, online newspaper is very popular among the people and contents varieties of crime news which can be a great source to understand the types and occurrence of crime. The aim of this paper is to monitor the crime, based on the headlines of the online newspaper provided in Twitter. Our approach is based on sentiment analysis by applying lexicon based methods and understand the crime categorized in a day, month, location and week. This piece of research work will help to deep understanding the pattern of the crime as well as the possibilities of occurrence of the crime in the specific time or day which will bear a great value to ensure the security purpose.
OccGAN: Semantic image augmentation for driving scenes

Difficult images with complicated environments and occlusion have significant impacts on the performance of algorithms. They obey the long-tail distribution in the widely used datasets, which results in rare samples being overwhelmed during training. This paper presents a new approach to generate plausible occluded images with annotation as a kind of data augmentation with scenes semantics. To achieve this task, we proposed the Occlusion-based Generative Adversarial Network (OccGAN) structure, which consists of a Rationality Module and an Authenticity Module. The Rationality Module generated preliminary occluded samples under the guidance of prior semantic knowledge. And the Authenticity Module is a generative adversarial structure to ensure the reality of the produced images. Qualitative results of the visualization process are given to verify the ablation study. Experiments on the semantic segmentation task indicate that several state-of-the-art algorithms combined with our OccGAN such as DRN, Deeplabv3+, PSPNet and ResNet-38, have boosts on IoU class scores and IoU category scores successfully.