Reviewerly/exHarmony huggingface dataset:
Design of Internet of Things Information Security Based on Blockchain

With the development of the Internet of Things (IoT), the future world will be an era of interconnection of all things. The traditional IoT has problems with single points of failure and trust, while the IoT system still has some technical challenges in terms of information security. The features of blockchain, such as decentralization, trustless and privacy protection, provide a new way of idea for solving the problem of information security in the IoT. InterPlanetray File System (IPFS) solves the problem of low storage efficiency and high cost for the application of blockchain. Therefore, this paper discusses and designs an IoT information management platform based on blockchain technology.
Offline Printed Tai Le Character Recognition Using VGGNET

It is a difficult problem to recognize the Tai Le characters, since they are close in shape and high in proportion. This paper constructs an offline printed Tai Le recognition model based on VGGNET. First, the database YDH2019.1 containing 135000 printed Tai Le character images and 35 types of Tai Le characters is built. Then, YDH2019.1 is preprocessed by noise reduction and normalization, and VGG16 is used as the classifier of the model. The results show that the Precision of the recognition model is 0.999943, recall is 0.999943, and fi-score is 0.999943, which can effectively recognize the offline printed Tai Le characters.
Construction of Social Security Fund Cloud Audit Platform Based on Fuzzy Data Mining Algorithm

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output.
GPUKV

When data is loaded from a key-value store to the GPU in a conventional GPU-driven computing model, it entails the overhead of all the heavy I/O stacks of the key-value store and file system. This paper presents GPUKV, a GPU-driven computing framework that eliminates the aforementioned overhead with less host-side usage of resources such as CPU and memory. GPUKV has the following three features: (i) GPUKV provides a key-value store abstraction to the GPU; (ii) In GPUKV, when loading data from the key-value store to the GPU, it is performed through PCIe peer-to-peer (P2P) communication without copying to the user and kernel space memory; and (iii) GPUKV uses KVSSD, which implements a key-value store inside an SSD, completely eliminating the interaction with the key-value store and file system for P2P communication. We have developed GPUKV with a KVSSD implemented on the Cosmos+ OpenSSD platform in a Linux environment. Our extensive evaluations demonstrate that GPUKV improves execution time by up to 18.7 times and reduces host CPU cycle usage by up to 175 times compared to conventional CPU-based GPU computing models.
Internet of Things-based innovations in Saudi healthcare sector: A methodological approach for investigating adoption issues

The Internet of Things (IoT) has proliferated over the last few years as the next-generation technologies that impact both on human systems and businesses. Using today's Internet network capacities, this technology has extended various benefits in healthcare sectors. For instance, existing studies already indicated that information technology (IT) applications with IoT-based innovations may revolutionise the healthcare industry and subsequently help to improve the real-time reporting of patients' health data. It should be noted that the adoption of IoT and its relevant interventions in health sector have not been as fast as the uptake been observed in other industries. To tackle this issue, we develop a qualitative phenomenological approach for investigating factors that affect IoT adoption and its integration into healthcare service delivery in Saudi Arabia. To this end, the study aims to reveal practical experiences of healthcare professionals or service administrators in using IoT devices as they deliver medical services.
Power system real time data encryption system based on DES algorithm

To ensure the safe operation of power system, this paper studies two technologies of data encryption and digital signature, and designs a real-time data encryption system based on DES algorithm, which improves the security of data network communication. The real-time data encryption system of power system is optimized by the hybrid encryption system based on DES algorithm. The real-time data encryption of power system adopts triple DES algorithm, and double DES encryption algorithm of RSA algorithm to ensure the security of triple DES encryption key, which solves the problem of real-time data encryption management of power system. Java security packages are used to implement digital signatures that guarantee data integrity and non-repudiation. Experimental results show that the data encryption system is safe and effective.
Survey of Available Datasets for Designing Task Oriented Dialogue Agents

Dialogue Systems are increasingly popular with the recent advances in neural approaches and NLP applied to conversational AI. Alexa, Siri, Cortana, Google Mini are handily used by many users to do small tasks and control their home appliances in hands free style. Enterprises are also deploying 24 × 7 dialogue agent in place of traditional customer support to increase user engagement and improve their processes. Dialogue Systems are also augmented with Robots to improve human-robot dialogues.Conversational Agents are classified into two main types: Social bots/Chitchat bots and Task Oriented Dialogue Agents. Social bots aim to engage user with unstructured human conversations. These dialogue agents don't have fixed aim to complete and focus more on carrying out open domain conversations. For example ALIZA, Microsoft XiaoIce etcOn the other hand, Task oriented dialogue agents help user to accomplish certain tasks in specific domains like Restaurant booking, Flight reservation, customer support etc. These are popularly used in controlling home appliances and carrying out simple tasks by users in day to day life. Siri, Alexa, Google Mini, Cortana are task oriented dialogue agents. There is increasing interest in building task completion dialogue agents that span over multiple sub-domains to accomplish a complex user goal.With the increasing acceptance of Dialogue Agents, there is need of high-quality, large-scale dialogue datasets for better performance of task oriented dialogue agent in changing environment. Neural approaches are applied to design intelligent dialogue agents frequently which require very large datasets. However, there are following challenges while building intelligent task completion dialogue systems. Firstly, there are a lot of datasets available for chit-chat bots but they are not directly relevant to task oriented systems. Secondly, to scale out the system to new domains with limited in-domain data.In this paper, we studied different data collection methods, important characteristics of dialogue datasets and their potential uses. This paper presents a survey of publicly available datasets and their applicability for designing modern task - oriented dialogue agents.
Research on Human Behavior Recognition Method Based on Static and Dynamic History Sequence

In this paper, a behavior recognition model based on CNN and RNN is proposed, which has high efficiency and high performance. Firstly, a key frame selected method based on the motion variation curve can intuitively represent the global change of motion, and reduce redundant information to construct the optimal key frame sequence. Secondly, the static and dynamic history sequence as the input characteristics of the model is designed based on sparse sampling method. Finally, a new Progressive network model with pyramid pooling to extract features effectively. Next, a bi-directional gate recurrent unit (Bi-GRU) is designed, which encodes all frame features of video successively. At the same time, an attention mechanism is used to dynamically assign weight to the frame in the video, and make the model can actively focus on the key information in the video clip. Moreover, the method is validated in the data sets Ut-interaction and HMDB51 and showed a better recognition performances.
Queries related to COVID-19: a more effective retrieval through finetuned ALBERT with BM25L question answering system

Purpose The purpose of this paper is to build a better question answering (QA) system that can furnish more improved retrieval of answers related to COVID-19 queries from the COVID-19 open research data set (CORD-19). As CORD-19 has an up-to-date collection of coronavirus literature, text mining approaches can be successfully used to retrieve answers pertaining to all coronavirus-related questions. The existing a lite BERT for self-supervised learning of language representations (ALBERT) model is finetuned for retrieving all COVID relevant information to scientific questions posed by the medical community and to highlight the context related to the COVID-19 query. Design/methodology/approach This study presents a finetuned ALBERT-based QA system in association with Best Match25 (Okapi BM25) ranking function and its variant BM25L for context retrieval and provided high scores in benchmark data sets such as SQuAD for answers related to COVID-19 questions. In this context, this paper has built a QA system, pre-trained on SQuAD and finetuned it on CORD-19 data to retrieve answers related to COVID-19 questions by extracting semantically relevant information related to the question. Findings BM25L is found to be more effective in retrieval compared to Okapi BM25. Hence, finetuned ALBERT when extended to the CORD-19 data set provided accurate results. Originality/value The finetuned ALBERT QA system was developed and tested for the first time on the CORD-19 data set to extract context and highlight the span of the answer for more clarity to the user.
A Spatial-Temporal Features Based Fingerprinting Method for Machine tools in DNC Networks

As the most widely used solution for intelligent manufacturing, the Distributed Numerical Control (DNC) system faces with various novel attack opportunities caused by increasing connection to industrial ethernet and external interfaces. Attackers can easily fabricate packets between machine tools and Machine Data Collection (MDC) server. This may threaten not only processing tasks and production logic but also physical safety and business efficiency. Intrusion Detection System (IDS) is seen as a promising solution to these problems. However, existing device identifier used by IDS (e.g. IP and MAC) can be forged easily. In order to address this problem, we propose a fingerprinting method (Spatial-Temporal Feature Fingerprint, STFF) enabling identification of machine tools. The fingerprint is a vector composed of spatial and temporal features extracted from payload grayscale image, which is constructed with all payloads in a polling period. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network are used to extract spatial and temporal features from payload grayscale image respectively. Finally, a real-world unencrypted data set with two vendors (three models for each) of machine tools is used to verify the performance of the fingerprinting method. Our evaluation experiments show that the STFF could be a unique identifier to describe the communication relationship between machine tools and MDC server.
An embedding method for unseen words considering contextual information and morphological information

The performance1 of natural language processing has been greatly improved through the pre-trained language models, which are trained with a large amount of corpus. But the performance of natural language processing can be reduced by the OOV (Out of Vocabulary) problem. Recent language representation models such as BERT use sub-word tokenization that splits word into pieces, in order to deal with the OOV problem. However, since OOV words are also divided into pieces of tokens and thus represented as the weighted sum of the unusual words, it can lead to misrepresentation of the OOV words. To relax the misrepresentation problem with OOV words, we propose a character-level pre-trained language model called CCTE (Context Char Transformer Encoder). Unlike BERT, CCTE takes the entire word as an input and the word is represented by considering morphological information and contextual information. Experiments in multiple datasets showed that in NER, POS tagging tasks, the proposed model which is smaller than the existing pre-trained models generally outperformed. Especially, when there are more OOVs, the proposed method showed superior performance with a large margin. In addition, cosine similarity comparisons of word pairs showed that the proposed method properly considers morphological and contextual information of words.
TCM-ISP: A Comprehensive Intelligent Service Platform for Industry Chain of Traditional Chinese Medicines

In order to promote information interaction, intelligent regulation, and scale management in Chinese medicines industry, in this paper, a Chinese medicines intelligent service platform with characteristics of flexibility, versatility, and individuation was designed under the guidance of theoretical model of intelligent manufacturing of Chinese medicines (TMIM). TCM-ISP is a comprehensive intelligent service platform that can be flexibly applied to all links of Chinese medicines industry chain, which realizes data integration and real-time transmission as well as intelligent-flexible scheduling of equipment in response to different demand. The platform took logical framework of data flow as the core and adopts the modular design in which microcontroller and sensor module are independent to obtain overall design scheme of TCM-ISP that contains the diagram of overall framework, hardware structure, and software technology. Then, on the groundwork of overall design scheme and modern science technology, TCM-ISP was successfully constructed with flexible, intelligent, and networked characteristics in which TTL-USB and TTL-RS485S were utilized to build unified interface between boards with supporting hot-plugging mode. The results of platform tests show that TCM-ISP can not only successfully realize the integration, real-time transmission, and display of data information but also well accomplish remote intelligent-flexible control of equipment and allow flexible configuration and expansion of sensors and devices according to the needs of each link in TCM's industry chain. It is of great practical significance to the pursuit of intelligent manufacturing of Chinese medicines and the promotion of modernization of Chinese medicines industry.
Soft Power: Upgrading Chain Macroeconomic Policy Through Soft Forks

Macroeconomic policy in a blockchain system concerns the algorithm that decides the payment schedule for miners and thus its money mint rate. It governs the amounts, distributions, beneficiaries and conditions required for money supply payments to participants by the system. While most chains today employ simple policies such as a constant amount per block, several cryptocurrencies have sprung up that put forth more interesting policies. As blockchains become a more popular form of money, these policies inevitably are becoming more complex. A chain with a simple policy will often need to switch over to a different policy. Until now, it was believed that such upgrades require a hard fork – after all, they are changing the money supply, a central part of the system, and unupgraded miners cannot validate blocks that deviate from those hard-coded rules. In this paper, we present a mechanism that allows a chain to upgrade from one policy to another through a soft fork. Our proposed mechanism works in today’s Ethereum blockchain without any changes and can support a very generic class of monetary policies that satisfy a few basic bounds. Our construction is presented in the form of a smart contract. We showcase the usefulness of our proposal by describing several interesting applications of policy changes. Notably, we put forth a mechanism that makes Non-Interactive Proofs of Proof-of-Work unbribable, a previously open problem.
On the CCA Compatibility of Public-Key Infrastructure

In this work, we put forth the notion of compatibility of any key generation or setup algorithm. We focus on the specific case of encryption, and say that a key generation algorithm \(\mathsf {KeyGen}\) is \(\mathsf {X}\text {-compatible}\) (for \(\mathsf {X} \in \{\mathsf {CPA},\mathsf {CCA1},\mathsf {CCA2}\}\)) if there exist encryption and decryption algorithms that together with \(\mathsf {KeyGen}\), result in an \(\mathsf {X}\)-secure public-key encryption scheme.We study the following question: Is every \(\mathsf {CPA}\text {-compatible}\) key generation algorithm also \(\mathsf {CCA}\text {-compatible}\)? We obtain the following answers: Every sub-exponentially \(\mathsf {CPA}\text {-compatible}\) \(\mathsf {KeyGen}\) algorithm is \(\mathsf {CCA1}\text {-compatible}\), assuming the existence of hinting PRGs and sub-exponentially secure keyless collision resistant hash functions. Every sub-exponentially \(\mathsf {CPA}\text {-compatible}\) \(\mathsf {KeyGen}\) algorithm is also \(\mathsf {CCA2}\text {-compatible}\), assuming the existence of non-interactive CCA2 secure commitments, in addition to sub-exponential security of the assumptions listed in the previous bullet. Here, sub-exponentially \(\mathsf {CPA}\text {-compatible}\) \(\mathsf {KeyGen}\) refers to any key generation algorithm for which there exist encryption and decryption algorithms that result in a \(\mathsf {CPA}\)-secure public-key encryption scheme against sub-exponential adversaries.This gives a way to perform CCA secure encryption given any public key infrastructure that has been established with only (sub-exponential) CPA security in mind. The resulting CCA encryption makes black-box use of the CPA scheme and all other underlying primitives.
Improved High Dynamic Range for 3D Shape Measurement based on Saturation of the Coloured Fringe

Phase-shifting fringe projection methods have been developed for three-dimensional scanning (Zuo et al., 2018). However, the 3-Dimensional (3D) scanning of objects with a high dynamic reflectivity range based on structured light is a challenging task to achieve (Feng et al., 2018). The incorrect intensities captured will cause phase and measurement errors. Thus, this paper proposes a method that improves the current High Dynamic Range (HDR) (Jiang et al., 2016)) method to increase the dynamic range. The camera and projector have 3 channels, red, green, and blue, which can absorb and project these lights independently. This paper proposes a method that makes use of this by controlling the intensity of each projected for the camera. Each image can be split into 3 channels and provide 3 images which contain different intensities, then it will be used to compute the 3D information. In general, this is done by controlling the projection of red, green and blue (RGB) channel and apply the Jiang’s algorithm (Jiang et al., 2016). The results are compared and analysed with current HDR (Jiang’s method) and the regular three-step phase-shifting methods. From the experimental results, it has shown that our proposed method outperforms the current HDR and the regular three-step phase-shifting methods. Specifically, the proposed method manages to increase the dynamic range of the reflective property of objects. Additionally, our proposed method has also significantly reduced the times of 3D object measurements.
Implicitly distributed fuzzy random forests

In the field of Data Mining for large scale datasets, also known as Big Data Mining, the availability of effective and efficient classifiers is a prime concern. Accurate classification results can be obtained with sophisticated models, e.g. using ensembling approaches and exploiting concepts of fuzzy set theory, but with an high computational cost. The quest for efficiency leads to the adoption of distributed versions of classification algorithms, and in this effort the support of proper cluster computing frameworks can be fundamental. In this paper it is proposed DFRF, a novel distributed fuzzy random forest induction algorithm, based on a fuzzy discretizer for continuous attributes. The described approach, although shaped on the MapReduce programming model, takes advantage of the implicit distribution of the computation provided by the Apache Spark framework. An extensive experimental characterization of the algorithm over Big Datasets, along with a comparison with other state-of-the-art fuzzy classification algorithms, shows that DFRF provides very competitive results; moreover, a scalability study carried out on a small computer cluster shows that the approach is well behaved with respect to an increment in the number of available computing units.
Smart Internet of Things Based Induction Motor Parameter Monitoring and Control System

Automation is the use of various control systems for operating equipment such as machinery, processes in industries such as boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. This research paper presents advanced approaches using wireless monitoring system for induction motor based on Internet of Things (IoT) for safe and economic data communication. n the first approach, state of the art fault detection strategy is exhibited for induction motor. This research paper depends on the assessment of measured voltage, current, earth leakage, rotor status and speed. Progressed embedded strategy and utilized to isolate and survey the disappointment seriousness. In this procedure, distinctive sensors are associated with the motor, and the quantities are extracted by utilizing a microcontroller. The Graphical User Interface (GUI) with cloud server IoT is used to transmit the data from base station to remote station. This arrangement allows the client to interface with the framework. The proposed research paper based induction motor control system is validated through the simulation in Raspberry Pi 3 environment.