Reviewerly/exHarmony huggingface dataset:
Automatic Disability Chair for Lower Limb Disable People

The paper proposes an idea of an electric wheelchair which supports the body movement not only in locomotion but in standing position. Center of gravity is maintained by adjusting the whole body to the center of the base. Thus with the help of the Automatic Disability Chair (ADC) an individual can locomote by using joystick and stand by pressing buttons. A microcontroller (Arduino mega) is integrated with all inputs, motor drives & actuators, that controls all operations of ADC. The wheelchair is supported with an external iron rod and suspension struts to reduce any hustle, additionally rechargeable batteries are employed in ADC. All the quantification and measurements were done before, based on this calculation, a scaled 3D prototype was first developed on AutoCAD and then it was implemented practically.
Social Interaction With Software Connectors and the Function of Facade Component in Secure Application Logic

The paper represents a conceptual study of the social interaction model of ATMs by using integration in a UML model-based design for software architecture that deals with the secure component-based software protection and communication patterns through facade based connector. The facade-based connector is developed separately from software components by using the correct contact pattern between components and the required security patterns that encapsulate the connection with other secure connectors. Each secure connector consists of both security architecture and contact patterns. It is constructed as a composite component even though connectors are usually used by component-based software development to encapsulate the mechanisms of communication with components. This paper addresses the security and privacy issues related to social interaction with the ATM model that may also be encapsulated in the software application logic through a secure facade based connector that is known as a security in a connector based social e-commerce B2c application
Trustzone-Based Dual-Architecture Scheme for Active Defense

With the rapid development of computer and communication technology, embedded systems are being widely adopted in smart devices. The increasing connectivity of these systems and the difficulty in providing overall security has resulted in such devices being vulnerable to malicious attacks. Passive defense firewall technology and traffic-based intrusion detection technology are not entirely effective against such attacks. TrustZone technology can ensure the security of the system against unknown attacks to a certain extent. However, it increases the computational burden on the central processing unit. Thus, to actively defend against attacks, we designed a trusted computing dual architecture scheme based on the TrustZone architecture. To reduce the burden on the trusted computing base, prevent the trusted execution environment from occupying the central processing unit for too long, and reduce the performance overhead, we moved certain sensitive applications and function modules out of the trusted execution environment. To overcome the severe problem that modules in the trusted execution environment require passive invocation to deliver services, we created an active measurement closed-loop immune mechanism to safeguard the internal security of function modules. Furthermore, we designed a trust chain and dynamic measurement method based on this architecture to ensure that the security of sensitive applications is ensured by trust transfer. We changed the traditional attack response method, which involves restarting the entire system once attacked, by designing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by designing an attack model. Performance testing and analysis verified that the architecture reduced the impact of the security mechanisms on the system.
Considerations for Blockchain-Based Online Dispute Resolution

Blockchain is an indestructible ledger technology with a permanent digital footprint which is bringing about disruptions in almost every aspect of life. Since its inception, blockchain was deployed to eliminate human cost and effort and bring in decentralisation of power and control. With the overburdening of cases in national court systems, alternate dispute resolution is today the preferred mechanism for resolving private commercial disputes, outside of courts, especially arbitration. Resolution of commercial disputes plays a major role in the economic growth of any nation. Success of any system calls for a comprehensive approach consisting of five building blocks: the legal basis, the organisational setup, human excellence, communications, and management of change. It is also a hypothesis that the courts in the future will be more like a service rather than a location, with courtrooms being online/virtual, and customer-centric providers leading the market space. Resolution of commercial disputes will become more competitive and differentiated on the international front.
Beyond QA: ‘Heuristic QA’ Strategies in JIMI

JD Instant Messaging intelligence (JIMI) is an intelligent service robot designed for creating an innovative online shopping experience in E-commerce. We will introduce a framework that combines the ‘intelligent prediction’ and ‘user click’ to facilitate the user input and to format the user inputs as standard queries, which we call as ‘heuristic QA’. It consists of three strategies: intent prediction before user querying, query auto-completion during user querying and next query prediction after user querying. Until now about 1/3 user queries are from heuristic QA in JIMI, which significantly improves the user experience.
Indistinguishable Leakage-Resilient Circuit Compiler

Abstract Transforming any circuit into a circuit with the same functionality but resilience against well-defined classes of leakage is an essential subject of cryptography. Whereas, according to the relationship between $VBB$ and simulated leakage-resilient circuit compiler, the compiling needs a specific mechanism to guarantee that its input is free of any leakage. Unfortunately, it makes it tough to implement this strategy in the context of actual leaks. In this work, we formally propose the indistinguishable bounded leakage-resilient circuit compiler $\bar{C}$ to solve the problem mentioned above. Specifically, it specifies that any polynomial-time adversary who derives the bound secret information from the circuit compiler can not distinguish an equivalent compiled circuit $\bar{C}^{\prime}$ with the same output. To achieve this goal, first, we utilize the leakage-resilient indistinguishable encoding as its input. Second, it takes advantage of the indistinguishability obfuscation $(IO)$ program to construct an obfuscated circuit that removes the random coins of the input encoding . Its total running time is the original circuit $C$ running time plus $poly(\kappa )$, and the size is independent of the original $C$. As this new compiler overcomes the restriction of the simulated-based leakage resilient circuit compiler,it will bring an independent interest result in leakage-resilient cryptography.
Design and implementation of national record management system with blockchain

Apart from financial transactions, blockchains are suggested for numerous application fields. While it is possible to mount generic blockchains for specific use cases, the implementation is generally lean and easy to adjust. Here we present the fundamental ideas of blockchain technology and examine a real use case for national record management over a blockchain technology. A fully customized, private, and authorized blockchain is implemented from scratch. On the basis of this use, we analyze and justify the necessity for the blockchain technology and describe the desire features of our system. We discuss in more detail about the implementation of our system. This article describes the design and implementation of national record management system by using customized blockchain. Furthermore, to provide the best user experience, we also built the web application interface with Java web application framework named PrimeFace. The implementation of a prototype revealed that the built blockchain technology from scratch is more suitable.
A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain Image Classification and Image Denoising by Improved Log Gabor Filter

In brain image classification, feature set reduction is essential to build an optimised feature subset that will lead to precise measurement. In this paper, an improved technique for feature selection by Moth Flame Optimization with Opposition Based Learning (OBL) and Simulated Annealing (OB-MFOSA) is proposed. The OBL strategy is used to create the optimum initial solution, while Simulated Annealing improves the search space. The proposed OB-MFOSA shows improved performance than other well-known existing algorithms by eliminating getting stuck in the local optima. By using this hybrid moth flame optimization, the feature set is reduced to 40%. Also, image denoising is performed by Dual Tree Complex Wavelet Transform (DTCWT) with an improved Log Gabor filtering technique. The filter bank of Log Gabor filter bank is tuned by Genetic Algorithm. The selected features from hybrid MFO algorithm are classified using SVM classifier. Experiments reveal that this hybrid algorithm shows accurate classification outputs than the previous methods.
Does BERT Know Which Answer Beyond the Question?

Medical question and answer matching validation (QAMV) aims to construct an intelligent medical question answering classification system, which can use massive medical information content to quickly meet user needs and ensure the accuracy and authority of the content. This paper presents our proposed framework for the Chinese QAMV organized by the 2021 China conference on knowledge graph and semantic computing (CCKS) competition, which requires correct classification of whether the answer can satisfy the related question. After the preliminary experiment, we analyzed the bad cases in-depth and found hard instances and insufficient model generalization capabilities. In order to solve these problems, we have proposed a series of innovative strategies, including five categories of methods: 1) Four different adversarial training methods 2) Hard instances identification and multi-round training methods 3) Target instances constructed by similarity 4) Validation set retraining with a small learning rate 5) Medical word vector combined with Easy Data Augmentation (EDA) method for text data augmentation. Our innovative approach has improved by an average of 1.164% compared to the baseline. The experiment fully shows that our innovative method is very effective. The method we proposed won the CCKS competition and was significantly ahead of the second opponent (close to 1%) in the final, showing extremely high practicality and effectiveness.
Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach

The existing methods to calculate the Eigenvector Centrality(EC) tend to not be robust enough for determination of EC in low time complexity or not well-scalable for large networks, hence rendering them practically unreliable/ computationally expensive. So, it is of the essence to develop a method that is scalable in low computational time. Hence, we propose a deep learning model for the identification of nodes with high Eigenvector Centrality. There have been a few previous works in identifying the high ranked nodes with supervised learning methods, but in real-world cases, the graphs are not labelled and hence deployment of supervised learning methods becomes a hazard and its usage becomes impractical. So, we devise CUL(Centrality with Unsupervised Learning) method to learn the relative EC scores in a network in an unsupervised manner. To achieve this, we develop an Encoder-Decoder based framework that maps the nodes to their respective estimated EC scores. Extensive experiments were conducted on different synthetic and real-world networks. We compared CUL against a baseline supervised method for EC estimation similar to some of the past works. It was observed that even with training on a minuscule number of training datasets, CUL delivers a relatively better accuracy score when identifying the higher ranked nodes than its supervised counterpart. We also show that CUL is much faster and has a smaller runtime than the conventional baseline method for EC computation. The code is available at https://github.com/codexhammer/CUL.
Simultaneous Thickness and Surface Profiling of Blank Mask Using Robust Harmonic Phase-Iterative Algorithm and Fizeau Interferometer

In this study, a harmonic phase-iterative method that uses seven-frame interferograms was developed for the simultaneous thickness and surface assessment of blank masks. The incorporation of a harmonic convergence condition and the selected pixel array into the proposed method was found to eliminate harmonics-related errors and significantly increase the computational stability. For verification, the numerical simulation and simultaneous assessment of a blank mask using a multi-surface Fizeau interferometer with wavelength scanning were performed using a seven-frame harmonic phase-iterative method. The simulation and experimental results demonstrate that the proposed harmonic phase-iterative method has a better phase-extraction ability than the conventional iterative and Fourier window methods in terms of computational stability and suppression of non-target harmonic signals and nonlinearity of phase-modulation errors. The standard deviations in thickness and surface profiling were 3.208 nm and 5.054 nm, respectively, which were smaller than those calculated by the conventional iterative and Fourier window methods.
RffAe-S: Autoencoder Based on Random Fourier Feature With Separable Loss for Unsupervised Signal Modulation Clustering

Unsupervised signal modulation clustering is becoming increasingly important due to its application in the dynamic spectrum access process of 5G wireless communication and threat detection at the physical layer of Internet of Things. The need for better clustering results makes it a challenge to avoid feature drift and improve feature separability. This article proposes a novel separable loss function to address the issue. Besides, the high-level semantic properties of modulation types make it difficult for networks to extract their features. An autoencoder structure based on the random Fourier feature (RffAe) is proposed to simulate the demodulation process of unknown signals. Combined with the separable loss of RffAe (RffAe-S), it has excellent feature extraction ability. Great experiments were carried out on RADIOML 2016.10 A and RADIOML 2016.10 B. Experimental evaluations on these datasets show that our approach RffAe-S achieves state-of-the-art results compared to classical and the most relevant deep clustering methods.
Analysis of Hyperspectral Image Denoising Using Deep Neural Network (DNN) Models

Image denoising is considered a common preprocessing step in the analysis and interpretation of hyperspectral images. Nevertheless, most of the methods developed and used previously was adopted for HSI denoising exploit architectures originally developed for grayscale and RGB images which limit the processing of high-dimensional HSI data cubes. As rich spectral information is present in HSI which is to be fully exploited considering the high degree of spectral correlation between adjacent bands in HSIs which gives in resulting poor image denoising, HSI denoising is the most important preprocessing step before the image is being classified. End to end mapping is needed between the clean and noisy images for the dataset by the deep learning method. Conventional low-rank methods lack flexibility for considering the correlation between different HSI which results to loss of information. This paper gives a brief review and analysis of the state-of-the-art available methods for hyperspectral image de-noising with the major advancements, benefits and obstacles in denoising an HSI. Due to limited availability of real time dataset of HSI and equipment expenses, researchers rely on the freely available hyperspectral datasets. This research proposes Hyperspectral image denoising for efficient classification of objects on the earth surface.
The Power of Anchor Text in the Neural Retrieval Era

In the early days of web search, a study by Craswell et al. [11] showed that anchor texts are particularly helpful ranking features for navigational queries and a study by Eiron and McCurley [24] showed that anchor texts closely resemble the characteristics of queries and that retrieval against anchor texts yields more homogeneous results than against documents. In this reproducibility study, we analyze to what extent these observations still hold in the web search scenario of the current MS MARCO dataset, including the paradigm shift caused by pre-trained transformers. Our results show that anchor texts still are particularly helpful for navigational queries, but also that they only very roughly resemble the characteristics of queries and that they now yield less homogeneous results than the content of documents. As for retrieval effectiveness, we also evaluate anchor texts from different time frames and include modern baselines in a comparison on the TREC 2019 and 2020 Deep Learning tracks. Our code and the newly created Webis MS MARCO Anchor Texts 2022 datasets are freely available.
UDON: Unsupervised Data SelectiON for Biomedical Entity Recognition

High-quality training datasets are critical for building successful Machine Learning (ML) based NLP systems. However, these datasets are not always available in low-resource contexts such as the biomedical domain. Here, selecting relevant training data is as important as the choice of the ML model. In this study we propose UDON: Unsupervised Data selectiON for biomedical entity recognition using domain-specific pretrained Language Models (LMs). We first show that pretrained LMs succeed at implicitly learning the differences between datasets without any supervision, and then use these models to select relevant data instances. Next, we evaluate the proposed methods for entity recognition on seven biomedical datasets and one news domain dataset using four LMs and three selection methods. Our results show that using pretrained domain-specific LMs for data selection outperforms all other approaches. Finally, we use domain classification as an auxiliary task for pretraining the neural network on the in-domain dataset and show this yields further improvements.
Rescue Strategy in Case of Large-Scale Flood Damage in the Koto Delta Region

A wide-area evacuation plan has been formulated for the Koto Delta region because of the large population in the inundation area and the expected shortage of evacuation centers. However, carrying out wide-area evacuation as planned may be challenging due to multiple issues such as large-scale traffic congestion. Therefore, it is expected that numerous people will be isolated and their health will deteriorate due to long-term flooding. We propose an efficient rescue strategy to reduce the number of residents who are isolated for long periods of time. Therefore, we changed the conditions for rescuing isolated people by boats, such as the order of rescuing the isolated people and the evacuation rate, and estimated the changes in the number of isolated people and the time required for rescue completion under each condition. Next, we compared the estimated results under each condition to develop efficient rescue methods for isolated people.
Facilitate Security Event Monitoring and Logging of Operational Technology (OT) Legacy Systems

Traditional networks with event monitoring and logging capability have been successfully deployed and implemented for years. However, these solutions are not always suited to be deployed and configured within an industrial control system (ICS) due to the legacy challenges and nature of devices running on this ecosystem. It is essential to understand what system, device and application to monitor, which logs to collect, where to collect them, how to acquire, secure, and preserve them as well as how best to use the collected logs. For example, users, assets, applications, and behaviours should be monitored and logged with an easy-to-read dashboard that adds context to the acquired logs. There has been a significant cyber threat to the ICS legacy system, such as malicious malware and advanced persistent threat (APT). In this paper, we propose a secure virtualised architecture based on the Purdue Enterprises Reference Architecture Model (PERA) for the ICS ecosystem that facilitates monitoring and logging of ICS legacy systems. The proposed architecture uses an enhanced open-source host-based intrusion detection system (HIDS) and network monitoring capability to maintain log integrity on transit and storage. The current Operational Technology (OT) monitoring and logging tools and software are ineffective in collecting and centralising OT legacy system logs. The novelty of the proposed virtualised architecture has an endpoint visibility feature and critical alert capabilities that detects an indication of compromise on the legacy and non-legacy systems in the ICS ecosystem.
Research on Application of Image Search for Ceramics

With the rapid development of the network, images have become the main storage method for storing information and displaying information. To solve the search problem of searching for some ceramic pictures that cannot be described by text or voice, image search by image search has become a solution. This article combines the image processing of Python and OpenCV, extracts the image feature points through the SIFT algorithm, and obtains the target image through the feature point matching calculation to realize the search of the ceramic image and develop its APP.
Design of Intelligent Recognition English Translation Model based on Improved Apriori Algorithm

Natural language processing is a method in computer science to obtain and analyze meaning from human language and interact with human in an intelligent way. The translation recognition results of Apriori algorithm model have data points overlapping, and the accuracy can not be effectively guaranteed. In order to identify phrases accurately, an intelligent recognition model of machine translation based on improved Apriori algorithm is proposed for the low accuracy of conventional algorithm model. This algorithm creates a corpus for marking phrases, so that phrases can be searched automatically. In addition, a machine translation intelligent recognition model is created to plan the intelligent recognition model based on data collection, processing and output. The information is collected and processed in a planned way, and the feature parameters are extracted to realize the intelligent recognition of machine translation. The designed machine translation intelligent recognition model is experimentally analyzed and the experimental data are recorded. The experimental analysis shows that the designed machine translation recognition model can finish translation work. The algorithm overcomes the disadvantages of Apriori, improves the operation speed and processing performance, is more suitable for machine translation tasks, and provides a novel idea of intelligent machine translation.