Reviewerly/exHarmony huggingface dataset:
A Chatbot for Preliminary Patient Guidance System

It is important to provide clinical guidance to patients according to their symptoms. Before going to the doctor, many people search for their symptoms via the internet and misdiagnose themselves. They usually think they have a much more serious illness than they have. Nowadays, clinical guidance tasks completed by internet searching, hospital information desk, or hear saying in society may cause latency in getting accurate health care. These results demand accurate and fast guidance to clinical services. The chatbot created based on a reliable database of symptoms can assist millions of patients to receive a more accurate preliminary diagnosis. However, a chatbot cannot substitute professional healthcare consulting and a visit to a doctor is a must. In this paper, we present an AI chatbot using a newly generated database for preliminary patient guidance and then show the results of the proposed architecture. The Chatbot for Preliminary Patient Guidance System (PGS) aims to guide patients to clinical services by checking symptoms automatically and to acquire required healthcare services quickly. Patients can get information about the possible disease (s) and related clinical service (s) by telling their symptoms to the PGS.
Rethinking search: making domain experts out of dilettantes

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts - they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.
Low-code from frontend to backend: Connecting conversational user interfaces to backend services via a low-code IoT platform

Current chatbot development platforms and frameworks facilitate setting up the language and dialog part of chatbots, while connecting it to backend services and business functions requires substantial manual coding effort and programming skills. This paper proposes an approach to overcome this situation. It proposes an architecture with a chatbot as frontend using an IoT (Internet of Things) platform as a middleware for connections to backend services. Specifically, it elaborates and demonstrates how to combine a chatbot developed on the open source development platform Rasa with the open source platform Node-RED, allowing low-code or no-code development of a transactional conversational user interface from frontend to backend.
Bie-Modernism and Cultural Computing

Civilization is the parasitic host of barbarism. Pseudo-modernity has lodged in modern civilization through high technology including computer technology and artificial intelligence technology, and has formed a threat to human modern civilization. Bie-modernism is a doctrine that expresses the idea of distinguishing the true modernity or true world from the pseudo modernity or the false world. Bie-modernist Culture Computing (BCC) is a kind of cultural computing guided by Bie-modernism, so as to defend the legitimate interests of people. It includes two parts, one is Visual Identification System, which combined with the background of the times and the humanistic environment, we can build a supporting platform for calculating the characteristics of the characters, and combine big data and NLP and other computer technologies to identify the real and pseudo characters. Another is Digital Identification System, which is from the perspective of cultural value, the feature model library of real and pseudo identification system is built to form a new foundation of digital global identification system. For paper regulation here we just introduce one of them.
Geology of the Structurally Controlled Quartz Vein Gold Mineralization in the Dikko Area, Part of Zungeru—Minna—Birnin Gwari Schist Belt, NW Nigeria

Dikko area, Niger State, North Central Nigeria falls within the Pan-African thermo-tectonic event which occurred in Nigeria as in other parts of West Africa with widespread effect on the mineralization of rejuvenated basement and geosynclinal trough deposits. The Pan-African Belt in Nigeria forms part of the Benin-Nigeria (Dahomeyan) shield which is regarded as the southern prolongation of the circumcratonic Pan-African belt of the West African Craton. Structurally controlled, gold-bearing quartz veins were measured and studied in the area from an extensive geological ground investigation. Field and petrographical studies indicate that the Neoproterozoic rocks constitute gneisses, amphibolite schist, amphibolites and migmatites. The amphibolites are intruded by pegmatites. The associated quartz veins are generally concordant with the main N-S regional structural trend. The lithological and structural studies carried out in these areas have demonstrated the relationship between structures and mineralization and/or lithology. The main characteristics of the gold mineralization in the area appear to indicate hydrothermal mineralization in connection with shear zones. This study allows us to predict that quartz veins in the amphibolite can be mined at a profit and a detailed geochemical and resource valuation needs to be carried out for the Au and associated Galena and Ag mineralization to fully exploit their economic potential.
Cryptocurrencies as a Hedge and Safe Haven Instruments during Covid-19 Pandemic

This study examines the potential of cryptocurrencies such as bitcoin, ethereum, ripple, tether, and bitcoin cash as hedging instruments and a safe haven for the Indonesian capital market, especially during the Covid-19 pandemic era. Now, Indonesia's capital market condition is in turbulence. The benefit of this research is to help the investors make decisions on which cryptocurrencies can be an instrument hedge and safe haven in this Covid-19 pandemic era for Indonesia Stock Exchange (IDX). The data used in this study are data on the closing price of the Composite Stock Price Index (CSPI), bitcoin (BTC), ethereum (ETH), ripple (XRP), tether (USDT), and bitcoin cash (BCH) from January 3 to June 16, 2020. Data analysis used Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Quantile Regression (QREG). This study found that bitcoin, ethereum, tether, and bitcoin cash can act as a hedge, but only the ripple cannot act as a hedge. Bitcoin, ethereum, ripple, tether, and bitcoin cash cannot act as a safe haven when the Indonesian capital market was getting extreme, like during the Covid-19 pandemic era. The roles of bitcoin, ethereum, ripple, tether, and bitcoin cash as safe havens will fade when conditions in the Indonesian capital market become more extreme. This research can be used as a reference for investors for their investments by looking top four cryptocurrencies as a hedging instrument. However, in severe conditions such as during the Covid-19 Pandemic, the top five cryptocurrencies cannot be used as a safe haven, as revealed in this study.
Calibration of Timing Parameters of Still Image Cameras and Video Cameras by a Two Dimensional LED Array

This paper presents a t wo-dimensional 10 x 10 LED array system developed in-house at t he Standards and Calibration Laboratory (SCL) for the calibration of timing parameters of still image cameras and video cameras. In this paper, the circuit design and the calibration methods for cameras of different shutter types (rolling shutter or global shutter) are presented. This LED array may also be used to verify the shutter type of cameras. The frequency of the clock signal applied in the calibration is traceable to the SI through the cesium beam frequency standard maintained at SCL.
Non-monotone Adaptive Submodular Meta-Learning

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)Non-monotone Adaptive Submodular Meta-LearningShaojie Tang and Jing YuanShaojie Tang and Jing Yuanpp.57 - 65Chapter DOI:https://doi.org/10.1137/1.9781611976830.6PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The core idea of meta-learning is to leverage prior experience to design solutions that can be quickly adapted to new, unseen tasks. Most of existing studies consider the case where the feasible parameter space is continuous. Recently, [1] develops the framework of a discrete variant of meta-learning, called submodular meta-learning, and they treat each task as a discrete optimization problem, i.e., they intend to select a group of items that maximizes the average expected utility of all tasks. Motivated by their framework, we consider the submodular meta-learning problem under the adaptive setting. In particular, we assume that each item has a random state, which is drawn from some known prior distribution. One must select an item before observing its realized state. Given a task, the utility function is defined over items and states. Our goal is to adaptively select a group items, each selection is based on the feedback from the past, to maximize the average expected utility of all tasks. Following the framework of standard meta-learning, we propose an effective policy that is composed of two stages: We first pre-compute an initial set of items, called initial solution set, based on previously visited tasks, then, once a new task is revealed, we add more items to the initial solution set to complete the selection process. We show that our policy achieves a 1/32 approximation ratio if the utility function of each task is adaptive submodular. Our policy enjoys the benefits of providing a personalized solution to each task while reducing the computation cost at test time. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-683-0 https://doi.org/10.1137/1.9781611976830Book Series Name:ProceedingsBook Code:PRACDA21Book Pages:1-239
Implementing Informed Consent with Knowledge Graphs

The GDPR legislation has brought to light one’s rights and has highlighted the importance of consent, which has caused a major shift in how data processing and sharing are handled. Data sharing has been a popular research topic for many years, however, a unified solution for the transparent implementation of consent, in compliance with GDPR that could be used as a standard, has not been presented yet. This research proposes a solution for implementing informed consent for sensor data sharing in compliance with GDPR with semantic technology, namely knowledge graphs. The main objectives are to model the life cycle of informed consent (i.e. the request, comprehension, decision and use of consent) with knowledge graphs so that it is easily interpretable by machines, and to graphically visualise it to individuals in order to raise legal awareness of what it means to consent and the implications that follow.
IMPLEMENTASI PENGGUNAAN MEDIA PEMBELAJARAN ONLINE PADA PEMBELAJARAN DARING MATA PELAJARAN MATEMATIKA

This study aims to obtain data and information related to the implementation of the use of online learning media in online learning in mathematics. This study uses qualitative research methods through descriptive explanations to find out an overview of the use of online learning media in online mathematics learning. Participants in this study were students of SMP Negeri 1 Juwiring class VIII E, F, G, H and the math teacher in that class. Data collection was carried out by interview, direct observation, learning value, documentation and distributing questionnaires to students. The results of this study are teachers using whatsapp group and google classroom in online learning, unstable internet network, lack of student understanding of the material, and the number of assignments given. Learning online is less effective because the teacher only provides material and assignments. Teachers can increase learning online outcomes with present material through LMS, provide guidance and communication to students in need, and improve teacher skills in learning.
A Bayesian approach to insider threat detection

Insider attacks are an ever-increasing threat for organizations, with dire consequences. Rogue employees who possess legitimate access to systems, and knowledge of security policies and monitoring practices of organizations, can evade detection. Organizations remain ill-equipped in detecting, deterring and mitigating sophisticated insider attacks, as traditional security controls and detection systems are tailored to external threats. Literature on insider threat detection provides the theoretical foundation to understand the motives, behavior and patterns of insider attacks. The majority of proposed models for insider threat anomaly detection, mainly focus on processing network data. In this paper, we propose and evaluate a Bayesian Network architecture that can consider behavioral aspects in tandem with network data. Our system utilizes machine learning to understand the structure of the data, inputs specially crafted features based on theoretical foundations of insider threat and enables analysts to consider behavioral features, if such data is available. We applied our system on CMU’s synthetic dataset and our results provide justified and informed decisions on selecting parameters for Bayesian Networks and suggest that such an approach is highly effective. All attacks in the dataset were identified, with a very low number of false positives.
Fast Certified Robust Training with Short Warmup

Recently, bound propagation based certified robust training methods have been proposed for training neural networks with certifiable robustness guarantees. Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still costly. In this paper, we identify two important issues in existing methods, namely exploded bounds at initialization, and the imbalance in ReLU activation states. These two issues make certified training difficult and unstable, and thereby long warmup schedules were needed in prior works. To mitigate these issues and conduct certified training with shorter warmup, we propose three improvements: 1) We derive a new weight initialization method for IBP training; 2) We propose to fully add Batch Normalization (BN) to each layer in the model, since we find BN can reduce the imbalance in ReLU activation states; 3) We also design regularization to explicitly tighten certified bounds and balance ReLU activation states. In our experiments, we are able to obtain 65.03% verified error on CIFAR-10 ($\epsilon=\frac{8}{255}$) and 82.36% verified error on TinyImageNet ($\epsilon=\frac{1}{255}$) using very short training schedules (160 and 80 total epochs, respectively), outperforming literature SOTA trained with hundreds or thousands epochs under the same network architecture.
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic

Extracting synonyms from dictionaries or corpora is gaining a special attention as synonyms play an important role in improving NLP application performance. This paper presents a survey of the different approaches and trends used in automatically extracting the synonyms. These approaches can be divided into four main categories. The first approach is to find the Synonyms using a translation graph, The second approach is to discover new transition pairs such as (Arabic - English) (English - France) then (Arabic- France). The third approach is to construct new WordNets by exploring synonymy graphs, and the fourth approach is to find similar words from corpora using Deelp Learning methods, such as word embeddings and recently BERT models. The papers also presents comparative analysis between these approoaches, and highlights potential adaptation to generate synonyms automatically in the Arabic language as future work.
UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features

We describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021.They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, word length, and number of senses, with BERT vectors.We test numerous feature combinations and machine learning models in our experiments and find that BERT vectors, even if not optimized for the task at hand, are a great complement to classic features.We also find that employing the principle of compositionality can potentially help in phrase complexity prediction.Our systems place 45th out of 55 for single words and 29th out of 38 for phrases.
On the Need for Thoughtful Data Collection for Multi-Party Dialogue: A Survey of Available Corpora and Collection Methods

We present a comprehensive survey of available corpora for multi-party dialogue. We survey over 300 publications related to multi-party dialogue and catalogue all available corpora in a novel taxonomy. We analyze methods of data collection for multi-party dialogue corpora and identify several lacunae in existing data collection approaches used to collect such dialogue. We present this survey, the first survey to focus exclusively on multi-party dialogue corpora, to motivate research in this area. Through our discussion of existing data collection methods, we identify desiderata and guiding principles for multi-party data collection to contribute further towards advancing this area of dialogue research.
A blockchain-based evidential and secure bulk-commodity supervisory system

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.
Emotion Sentiment Analysis of Indian Twitter-Data of COVID-19 After Lockdown

This COVID-19 pandemic has severely affected countries around the world. The intensity of the pandemic in India is also increased. The number of new cases is increasing day by day. In the past six months, the total number of cases crossed 50 lakh and the total number of deaths is almost 1 lakh. It has been observed that the sudden outbreaks of such pandemics affect public mental states and emotions. This pandemic also results in either constructive or destructive behavioural changes among people. Anger, Sadness, and fear are the most common emotions witnessed among the people during several pandemics. Social media platform like Twitter and others have rich sources of information from people. So, we are here going to analyze the emotions of the peoples after the COVID-19 six month's phase. It can help to detect how a person is feeling and can help those persons to not feel negative or can be able to stop them from taking any wrong decision.
Diagnostics for Conditional Density Models and Bayesian Inference Algorithms

There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, it is challenging to assess conditional density estimates and gain insight into modes of failure. While existing diagnostic tools can determine whether an approximated conditional density is compatible overall with a data sample, they lack a principled framework for identifying, locating, and interpreting the nature of statistically significant discrepancies over the entire feature space. In this paper, we present rigorous and easy-to-interpret diagnostics such as (i) the Coverage Test (LCT), which distinguishes an arbitrarily misspecified model from the true conditional density of the sample, and (ii) Amortized Local P-P plots (ALP) which can quickly provide interpretable graphical summaries of distributional differences at any location x in the feature space. Our validation procedures scale to high dimensions and can potentially adapt to any type of data at hand. We demonstrate the effectiveness of LCT and ALP through a simulated experiment and applications to prediction and parameter inference for image data.
Secure Cloud EHR with Semantic Access Control, Searchable Encryption and Attribute Revocation

To ensure a secure Cloud-based Electronic Health Record (EHR) system, we need to encrypt data and impose field-level access control to prevent malicious usage.Since the attributes of the Users will change with time, the encryption policies adopted may also vary.For large EHR systems, it is often necessary to search through the encrypted data in realtime and perform client-side computations without decrypting all patient records.This paper describes our novel cloud-based EHR system that uses Attribute Based Encryption (ABE) combined with Semantic Web technologies to facilitate differential access to an EHR, thereby ensuring only Users with valid attributes can access a particular field of the EHR.The system also includes searchable encryption using keyword index and search trapdoor, which allows querying EHR fields without decrypting the entire patient record.The attribute revocation feature is efficiently managed in our EHR by delegating the revision of the secret key and ciphertext to the Cloud Service Provider (CSP).Our methodology incorporates advanced security features that eliminate malicious use of EHR data and contributes significantly towards ensuring secure digital health systems on the Cloud.
Wire grid polarizer-based cyclic shearing interferometer with polarization phase shifting

Imaging of transparent phase samples using a wire grid polarizer (WGP)-based shearing interferometer is demonstrated. The proposed setup employs a WGP as a beam splitter in a triangular cyclic interferometer setup, so that the two counterpropagating beams are rendered linearly polarized in orthogonal directions as they emerge from the interferometer. These sheared waved fronts are made to pass through the phase sample. The magnitude of shear is affected by controlled angular shifts of one mirror about the reference X and Y axes. For each of these directional shears, the polarization phase shifting is implemented so as to generate a three-dimensional phase pattern, which is digitally combined and integrated to yield the sample phase. The proposed setup is robust, sufficiently tolerant to ambient vibrations, and shows very encouraging experimental results.