Reviewerly/exHarmony huggingface dataset:
Market Basket Analysis To Identify Stock Handling Patterns &amp; Item Arrangement Patterns Using Apriori Algorithms

The process of managing the pattern of handling stock of goods and the pattern of arranging goods on store shelves requires an identification process by utilizing data from sales transaction results. Market basket analysis of sales transaction data using Apriori Algorithm stages produces an information in the form of association rules with a minimum support value of 50% and a minimum confidence of 60%. It can be a reference in the arrangement of items on store shelves by referring to a combination of items that are often bought by consumers simultaneously. In addition, the stock inventory pattern can take advantage of the results of determining the high frequency value in the combination pattern 1 - itemset C1 with a minimum support value of 50% which is compared with the initial inventory.
Covid-19: environmental health officers and retired doctors step up to fill contact tracing void

Hundreds of environmental health professionals have offered their time and expertise to help with contact tracing in England, Wales, and Northern Ireland, in the hope of getting the covid-19 outbreak in the UK under control.More than 400 signed up to the Chartered Institute of Environmental Health (CIEH) voluntary register, which has been shared with Public Health England and the governments in Wales and Northern Ireland.Meanwhile, a team of retired doctors and public health experts in Sheffield, frustrated by the lack of contact tracing, have set up their own community initiative—combining contact tracing with support for those who need to self-isolate.The moves come as health secretary Matt Hancock announced that the government would reintroduce contact tracing of people who have had symptoms of covid-19, supported by increased testing.1Despite the World Health Organization repeatedly emphasising the importance of contact tracing, the UK abandoned the practice in March, arguing that when the number of cases rises it becomes less effective and resources are more …
Utilizing WhatsApp Application in English Language Learning Classroom

&lt;p&gt;WhatsApp (WA) is one of the most popular messaging applications which can be accessed by using mobile phone and Personal Computer (PC). Nowadays, most people prefer to use this application for communicating with other. In education field, WhatsApp also give beneficial features such as text, call, send video, audio, links, location, document, and pictures. This research is aimed at observing WA which is tailored to support English Language Learning (ELL) activities. For gathering data, this study applied case study using observation and questionnaire as main instrument. A case study is drawn from participants of 5&lt;sup&gt;th&lt;/sup&gt; semester students at English Department Universitas Bina Sarana Informatika. It is found that WA was significant in helping ELL activities since it was used by both teachers and students for ELL related activities. The results are: 1) In the field of technical advantages, WA provides simple operation; 2) WA can enhance learning opportunities outside classroom for students. In brief, WA facilitated ELL activities as an effort toward MALL (Mobile-Assisted Language Learning) implementation.&lt;/p&gt;
Logic-Guided Data Augmentation and Regularization for Consistent Question Answering

Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model. Improving the global consistency of predictions, our approach achieves large improvements over previous methods in a variety of question answering (QA) tasks including multiple-choice qualitative reasoning, cause-effect reasoning, and extractive machine reading comprehension. In particular, our method significantly improves the performance of RoBERTa-based models by 1-5% across datasets. We advance the state of the art by around 5-8% on WIQA and QuaRel and reduce consistency violations by 58% on HotpotQA. We further demonstrate that our approach can learn effectively from limited data.
AI Patents: A Data Driven Approach

The global technology market exceeds $12 trillion. The market’s fastest growing niche is artificial intelligence (AI). Yet, while the literature on technology patents is theoretically robust - the literature on AI patents is relatively uncharted. As a consequence, lawyers, scholars, and commentators often refer to AI as a black box – arguing not even advanced computer scientists understand how it works. But all AI technology is written with formal logic, mathematics, and computer code. Thus, all AI systems are syntactically describable, repeatable, and explainable. In other words, there is no black box.This Article empirically analyzes the unique intellectual property strategy decisions technology firms face by introducing a dataset including four specific types of machine learning patents: deep learning, reinforcement learning, deep reinforcement learning, and natural language processing. Dataset charts, models, and graphs, provide insight into market alcoves, while analysis of each machine learning technology shines a light through the “black box.” Further, patent claims analysis reveals significant overlap in patented AI technologies. In sum, this Article draws on a growing body of informatics, intellectual property, and technology scholarship to provide novel patent analysis and critique.
Even Faster Exact k-Means Clustering

A naïve implementation of k-means clustering requires computing for each of the n data points the distance to each of the k cluster centers, which can result in fairly slow execution. However, by storing distance information obtained by earlier computations as well as information about distances between cluster centers, the triangle inequality can be exploited in different ways to reduce the number of needed distance computations, e.g. [3–5, 7, 11]. In this paper I present an improvement of the Exponion method [11] that generally accelerates the computations. Furthermore, by evaluating several methods on a fairly wide range of artificial data sets, I derive a kind of map, for which data set parameters which method (often) yields the lowest execution times.
Cognitive Competencies of Graduates in the Conditions of Formation of the Knowledge-Digital Economy

The processes of globalisation and internalisation in the world community, the development of high technologies and new industries, the digitalisation of the economy lead to the need for training with a fundamentally new set of competencies to meet the needs of customer organisations. The article is devoted to the study of cognitive competencies as key components in the structure of a university graduate’s personality in the knowledge-digital economy, which is in demand in the labour market. The article deals with the trend of changing the educational paradigm, based on updating the methodology of the modern educational process, including through the integration of educational, research and innovation activities as a strategy for the development of the modern economy. The necessity of inclusion in the list of key competencies of graduates. The author also discusses the concept of “cognitive competence” formulated by leading scientists involved in the study of this problem. The article presents the author’s model and structure of cognitive competencies developed for University graduates in the new economy, which is a set of development of mental, physical, emotional and spiritual intelligence. The author paid special attention to the fundamentally new role of the teacher in the educational process, in which the student does not just receive information from the teacher, but learns the methodology of independent “production” of knowledge.
Models, information and meaning

There has recently been an explosion of formal models of signaling, which have been developed in order to learn about different aspects of meaning. This paper discusses whether that success can also be used to provide an original naturalistic theory of meaning in terms of information or some related notion. In particular, it argues that, although these models can teach us a lot about different aspects of content, at the moment they fail to support the idea that meaning just is some kind of information. As an alternative, I suggest a more modest approach to the relationship between the informational notions used in models and semantic properties in the natural world.
Unreal as a simulation environment for offroad autonomy

We describe contributions made to the Unreal gaming engine community supporting our development of algorithms that enable navigation in off-road and unstructured environments. Simulation of autonomous ground vehicles traversing complex 3D terrain requires accurate modeling of vehicle-terrain interaction. Widely used community gaming engines model this interaction well but do not include sensor model and other features found in open source tools. We have extended the Unreal engine to include models for LiDARs, IMU, and GNSS systems that are compatible with the Robot Operating System middle-ware. We have also added features that allow for the automated testing of off-road navigation algorithms.
Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data

Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level.
Texture category-based matching cost and adaptive support window for local stereo matching

We present a local stereo matching method with a robust texture category-based matching cost and adaptive support window to deal with the disparity errors caused by repetitive patterns, occlusions, and nontextured cases. First, we decompose an input reference image into textured regions and nontextured regions. Then, different cost computation strategies are adopted for these two regions. For textured regions, we use the common absolute intensity difference and gradient similarity. For nontextured regions, we propose a matching cost computation method that is a combination of gradient and epipolar distance transform (EDT). In the cost aggregation step, we introduce an adaptive support window based on a modified linearly expanded cross skeleton. To obtain the cross skeleton, a depth edge detection technique and a triple expansion strategy are presented. The experimental results demonstrate that the proposed algorithm achieves outstanding matching performance compared with other existing local algorithms on the Middlebury stereo benchmark, especially in repetitive patterns, occlusions, and nontextured regions.
Enabling Privacy Preservation and Decentralization for Attribute-Based Task Assignment in Crowdsourcing

Crowdsourcing allows people who are endowed with certain skills to accomplish special tasks with incentive.Despite the state-of-art crowdsourcing schemes have guaranteed low overhead and considerable quality, most of them expose task content and user's attribute information to a centralized server.These servers are vulnerable to single points of failure, the leakage of user's privacy information, and lacking of transparency.We therefore explored an alternative design for task assignment based on the emerging decentralized blockchain technology.While enabling the advantages of the public blockchain, changing to open operations requires some additional technology and design to preserve the privacy of user's information.To mitigate this issue, we proposed a secure task assignment scheme, which enables task content preservation and anonymous attribute requirement checking.Specifically, by adopting the cryptographic techniques, the proposed scheme enables task requester to safely place his task in a transparent blockchain.Furthermore, the proposed scheme divides the attribute verification process into public pre-verification and requester verification, so that the requester can check only the identity of the worker, instead of verifying the attributes one by one, thereby preserving the identity of worker while significantly reducing the requester's calculation burden.Additionally, security analysis demonstrated unrelated entities cannot learn about the task content and identity information from all data uploaded by requester and worker.Performance evaluation showed the low computational overhead of our scheme.
Inovasi Pengembangan Sistem Informasi Pengelolaan Surat Keterangan Pendamping Ijazah Berbasis Web

This research was aiming to produce a management information system for Diploma Companion Certificate (SKPI). The target of the research to be achieved is the existence of an information system that can be used to manage the filling and printing of Companion Certificates. Management information system This Certificate of Companion is made to be applied in the Informatics Engineering Study Program S1 Faculty of Computer Science, University of Kuningan. This study discusses how the process of designing and developing an information system for managing Diploma Certificates of Completion in the S1 Informatics Engineering Study Program. The absence of an application or information system for managing Diploma Certificates is a serious problem that must be considered. Managers of the S1 Informatics Engineering Study Program find it difficult in the process of making and filling Diploma Companion Certificates for their graduates. In the absence of a management information system, a Diploma of Companion Certificate, the process of making and filling with Certificate ofCompanion Certificate will take a long time. The method used in this study is Rational Unified Process (RUP). There are four stages in the use of this RUP method, namely the stages of inception, elaboration, construction and transition. The design model used in this study uses Unified Modeling Language (UML). The testing process is carried out using the Black Box Testing. Therefore, to solve these problems, an innovation is needed In the management ofa web-based Diploma Companion Certificate. It is expected that this information system can help and ease the Study Program in managing Diploma Companion Certificates.
Multi-Label Classification using Deep Convolutional Neural Network

Convolutional Neural Networks (CNN) have proven to perform well in single-label classification tasks. However, performing a multi-label classification using a Convolutional Neural Network is still under research. Several variants of Convolutional Neural Networks have come into existence due to extensive research work with numerous improvisations. From the several variants of CNN, VGG-Net has been proven to provide a stable performance and hence it is used for experimental purposes in this paper. Generally a network trained using a specific classifier will be tested using the same classifier, to test the learning capability of the model. Models trained in this manner has the capability to recognize only a single object at a time. To overcome this scenario, this work intends to train a VGG-Net to recognize more than one label in a single instance of image sample, without increasing the complexity of the network architecture. To implement this objective, the network is trained and tested using Sigmoid: a binary classifier, instead of using Softmax: a multi-class classifier. This proposed model is experimented on self-curated datasets scrapped from Google images and improvised using manual pruning for relevancy and balanced samples in each category. The first dataset created is Colour Clothes and the second dataset created is Fruits & Vegetables. The performance of the network is evaluated with standard metric, Binary Cross-Entropy.
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.
Impact of leadership behavior on psychological capital: the mediating role of organizational support

Even though psychological capital has been identified as an important issue in the workplace, little research has focused on it through organizational support. Drawing on leadership theory and conservation of resources theory, this article examines the mediating role of organizational support in the relationship between leadership behavior and psychological capital in insurance companies in Jordan. The questionnaires were distributed randomly. Out of 350 questionnaires, 335 questionnaires were effective for analysis, after analyzing the data using the statistical program SmartPLS version 3.2.6. Based on the theories, a significant impact of organizational support on the relationship between leadership behavior and psychological capital of employees was found. Thus, the study recommended maintaining the interest of senior management in the insurance companies operating in Jordan to identify and educate the managers of these companies in choosing the appropriate leadership behavior in accordance with the company’s data and the circumstances surrounding it at the right time and place.
Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method

In this time, the need of research, the development and the implementation of the result of research in health is increasing both from the researchers, the government, the academic even of from the public general. One of the ways to find out the health research trend is by topic modeling. The method that used in this research is topic modeling LDA (Latent Dirichlet Allocation) method. The purpose of this research is to identify how modeling topic method LDA analyze modeling topic to some health research in Indonesia by Sinta Journal and to know how the coherence value in each topic of the model that has been made. Besides, hopefully it can be used as a reference to do heath research in Indonesia based the topic that has been modeled. The development of this research uses Anaconda3 Python Programming Language Tools and utilizes the LDA library that provided to get the topic model. To examine the result of this research the respondent are medical worker, health researcher and academics. The result of this research the topic modeling that used 94,1% respondent say very good and 5,9% say good.
Exponentially faster implementations of Select(H) for fermionic Hamiltonians

We present a simple but general framework for constructing quantum circuits that implement the multiply-controlled unitary $\text{Select}(H) \equiv \sum_\ell |\ell\rangle\langle\ell|\otimes H_\ell$, where $H = \sum_\ell H_\ell$ is the Jordan-Wigner transform of an arbitrary second-quantised fermionic Hamiltonian. $\text{Select}(H)$ is one of the main subroutines of several quantum algorithms, including state-of-the-art techniques for Hamiltonian simulation. If each term in the second-quantised Hamiltonian involves at most $k$ spin-orbitals and $k$ is a constant independent of the total number of spin-orbitals $n$ (as is the case for the majority of quantum chemistry and condensed matter models considered in the literature, for which $k$ is typically 2 or 4), our implementation of $\text{Select}(H)$ requires no ancilla qubits and uses $\mathcal{O}(n)$ Clifford+T gates, with the Clifford gates applied in $\mathcal{O}(\log^2 n)$ layers and the $T$ gates in $O(\log n)$ layers. This achieves an exponential improvement in both Clifford- and T-depth over previous work, while maintaining linear gate count and reducing the number of ancillae to zero.
Technical Scrutiny of Block chain Technology Protocols and its Applications

Block chain technology is one which is evolving over time and is still brand new to the mindset of various people. The question of how to use it even if one has the want to use it arises thereby questioning its need in necessary functioning. This technology may be hand in hand if required for the following features:-Various distributed participants; lack of trust in mediating parties; if maximum work is done through transactions. A need to reduce manual labor and be comfortable with digital listing of activities to avoid disputes. When one has a real time need of sharing the various activities with other clients/partners and get a valid and crisp work history. In this paper the importance of block chain and its implications are presented along with various case studies of technical glitches whether in IoT or banking sectors, this paper covers it all.
Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech

In this paper, we propose multi-band MelGAN, a much faster waveform generation model targeting to high-quality text-to-speech. Specifically, we improve the original MelGAN by the following aspects. First, we increase the receptive field of the generator, which is proven to be beneficial to speech generation. Second, we substitute the feature matching loss with the multi-resolution STFT loss to better measure the difference between fake and real speech. Together with pre-training, this improvement leads to both better quality and better training stability. More importantly, we extend MelGAN with multi-band processing: the generator takes mel-spectrograms as input and produces sub-band signals which are subsequently summed back to full-band signals as discriminator input. The proposed multi-band MelGAN has achieved high MOS of 4.34 and 4.22 in waveform generation and TTS, respectively. With only 1.91M parameters, our model effectively reduces the total computational complexity of the original MelGAN from 5.85 to 0.95 GFLOPS. Our Pytorch implementation, which will be open-resourced shortly, can achieve a real-time factor of 0.03 on CPU without hardware specific optimization.