Reviewerly/exHarmony huggingface dataset:
Interpretable Explanations for Probabilistic Inference in Markov Logic

Markov Logic Networks (MLNs) represent relational knowledge using a combination of first-order logic and probabilistic models. In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box classifiers, explaining M LN inference is harder since the data is interconnected. We develop an explanation framework that computes importance weights for MLN formulas based on their influence on the marginal likelihood. However, it turns out that computing these importance weights exactly is a hard problem and even approximate sampling methods are unreliable when the MLN is large resulting in non-interpretable explanations. Therefore, we develop an approach where we reduce the large MLN into simpler coalitions of formulas that approximately preserve relational dependencies and generate explanations based on these coalitions. We then weight explanations from different coalitions and combine them into a single explanation. Our experiments illustrate that our approach generates more interpretable explanations in several text processing problems as compared to other state-of-the-art methods.
Internet of Things as a Logical Way to Tackle the Emerging COVID-19 Virus

The outburst of thenovel coronavirus (COVID-19) that hasledto millionsof deathsglobally appears tobe overwhelming. This virushas become a focus of worldwide consideration. The Internet of Things (IoT) can be well-thought-out as a practical way to forecast, monitor, and avert the emerging COVID-19 virus. Internet of Things represents a connected-network of smart objects, sensors, devices, and people over which information can be acquired and communicated via the cloud for trend and patterns analysis. This technology has gained immense research plethora in health care and industrial applications. The IoT revolution is reforming contemporary healthcare schemes through integrating high-tech, commercial, and societal views. This paper briefly examines the potential of IoT technology as a logical way to tackle COVID-19 by evolving healthcare systems from conventional to more personalized healthcare systems through whichillness can be detected, treated, and supervisedeffortlessly
Entropy Weight-TOPSIS Method Considered Text Information with an Application in E-Commerce

With the rapid development of e-commerce, enterprises are no longer satisfied with making use of traditional data to make decisions on product selling points and marketing strategies. They hope to make decisions more reasonably and rapidly with the help of big data, especially they want to combine a large amount of comment text information by customers. Therefore, a new method is needed to solve this problem. This paper takes the data of pacifier, microwave oven and hair dryer sold by Amazon in recent 10 years as an actual case, and proposes a data preprocessing method for this kind of problem. Based on Feature Engineering and LDA, an indicators system considering the text information of comments is established. Then, the entropy weight TOPSIS algorithm is used to sort the products, and the top products are selected for further analysis. Finally, by analyzing the comment text and related features of the top ranked products, the reasons for their marketing success are extracted to assist enterprises in making relevant decisions, including but not limited to product quality characteristics, pricing, marketing strategies.
CT-VAE: An Unsupervised Noise Filtering Algorithm for Weibo Topic Datasets

Weibo Topic has become a popular data source for text analysis. Its quality is important to the results of related research or applications. However, as a crowdsource dataset, Weibo Topic suffers from the noises generated by malicious bloggers. To attract more views, these bloggers tend to tag their blogs with unrelated topic tags, which brings significant noises to this dataset. To filter these noises, researchers have proposed automated filter methods using supervised or semi-supervised learning. However, these methods require human-annotated data to train the models, which significantly raises the cost to build such filtering systems. In this work, we propose an unsupervised filtering method, CT-VAE, based on Variational Auto-Encoder. CT-VAE trains multiple VAE models on different topics to identify the noises. Our experiments show that CT-VAE can achieve better results than supervised learning methods when more unlabeled data are collected.
Application of "Green Channel Card" in Critical Patients in Emergency Department

Objective To explore the effect of "green channel card" in critically ill patients in emergency department. Methods Seventy-five critically ill patients in the emergency department of our hospital from August 2019 to April 2021 were selected as the experimental group, and 63 critically ill patients from June 2018 to July 2019 were selected as the control group. The experimental group was admitted by using the "green channel card", and the control group was admitted by the green channel signed by the general administrative duty in the hospital. The success rate of emergency rescue, patient satisfaction, waiting time, effective complaints and other related conditions were compared between the two groups. Results The success rate of rescue in the experimental group increased, but there was no statistical significance; while the waiting time in the experimental group was significantly higher than that in the control group (P &lt; 0.05), the patient satisfaction was also higher than that in the control group (P &lt; 0.05); the complaint rate was lower than that in the control group (P &lt; 0.05). Conclusion The use of "green channel card" in emergency critically ill patients can significantly shorten the waiting time for rescue, improve patient satisfaction, reduce the complaints of patients and their families, and make the green channel more efficient, usual and rapid. It is worthy of being widely popularized in the clinical application.
AuraRing

Wearable computing platforms, such as smartwatches and head-mounted mixed reality displays, demand new input devices for high-fidelity interaction. We present AuraRing, a wearable magnetic tracking system designed for tracking fine-grained finger movement. The hardware consists of a ring with an embedded electromagnetic transmitter coil and a wristband with multiple sensor coils. By measuring the magnetic fields at different points around the wrist, AuraRing estimates the five degree-of-freedom pose of the ring. AuraRing is trained only on simulated data and requires no runtime supervised training, ensuring user and session independence. It has a resolution of 0.1 mm and a dynamic accuracy of 4.4 mm, as measured through a user evaluation with optical ground truth. The ring is completely self-contained and consumes just 2.3 mW of power.
Iterative Fuzzy C Means, Fuzzy Silhouette, and Imputation for Missing Values in a Dataset

A missing value is an error that always happened, and it is unavoidable. This error should be handled correctly before data is processed into the processing model. This paper proposes a method of imputation by employing iterative Fuzzy C Means (FCM), centroid values and, fuzzy silhouette to handle missing values problem. Missing values can be treated by imputing the missing values. The advantage of FCM is it can provide a better separation of instances when an object is not well separated. It is a well-known clustering method that can provide better clustering result. The optimal clustering value can be measure by using fuzzy silhouette. In this paper, the relationship between imputation based on FCM, fuzzy silhouette and the optimal cluster is identified. Also, the factors that can give impact to accuracy of imputation is recognized,
Out of the Box Artificial Intelligence (OBAI): The Beginning of a New Era in Artificial Intelligence

The main purpose of writing this article is to unify all the OUT OF THE BOX ideas (under Artificial Intelligence) invented by the corresponding author of this work during the period (2013-2022) under a single umbrella titled “Out of the BOX Artificial Intelligence Field (OBAI Field)”. All the OUT OF THE BOX ideas which are proposed under Artificial Intelligence will come under new field titled OBAI Field which is defined in this work. A new Artificial Intelligence field titled “Artificial Cartoon Algorithms (ACA)” is invented in this work. ACA is a sub-field of OBAI field as it is an OUT OF THE BOX idea. Four new algorithms titled “Artificial Cartoon Popeye Algorithm”, “Artificial Cartoon Chhota Bheem Algorithm”, “Artificial Cartoon Jerry Algorithm” and “Artificial Cartoon Happy Kid Algorithm” are designed in this work.
Adult and Continuing Education

Higher Education AbstractsVolume 56, Issue 4 p. 320-320 ADULT AND CONTINUING EDUCATION Adult and Continuing Education First published: 08 January 2022 https://doi.org/10.1111/hea.2_12178Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue4October 2021Pages 320-320 RelatedInformation
User-level Enclave Protection Scheme based on ARM TrustZone

All Existing popular commercial trusted execution environment (TEE) technologies include ARM TrustZone and Intel SGX, which are widely deployed to secure sensitive digital resources. Though TrustZone and SGX use different design principles. SGX adopts peer-to-peer (P2P) model and executes secured codes in private enclaves created by corresponding applications. The application and its private enclave can communicate with each other. Compared with SGX, existing TrustZone-based solutions commonly adopts the client-server (CS) model, and executes secured code in a common secure world. However, existing TrustZone-based solutions lack of strong security and openness: (1) the trusted applications (TA) share the same isolation environment, which leads to weak isolation between TAs, (2) and client applications (CA) in the normal world can only have one-way access to services provided by the secure world. That means TA cannot access the service provided by the CA, causing vendor needs to write more codes to complete TA's function and increases the TEE's attack surface. Therefore, existing TrustZone-based solutions cannot meet the strong security and openness requirements of cloud computing, artificial intelligence and blockchain technologies.
Applying Machine Learning Algorithms to Automatically Classify Emergency Messages

The rapid development of machine learning and natural language processing leads to the possibility of improving the efficiency of unified duty and dispatch services. It is possible to develop algorithms for the automatic classification of emergency messages to three classes: ambulance service, fire service, and police service. In this paper various methods of text classification were studied and compared with each other. The transfer learning method shows the best accuracy with F1-score exceeds 95%. Based on the experimental study a website prototype for handling emergency calls was developed. The site saves information about calls to emergency services, displays them on an interactive map. Authorized operators can add, view, edit, and delete calls. The site supports the REST API to add and view emergency calls. The Yandex Alice voice assistant can be used to call the service.
Quality of Life Marketing, Innovation and Change Management: The Rasti Robotics and Education Team

“ERRE’‘(Equipo Rasti de Robótica y Educación, or Rasti Robotics and Education Team, in English) is an example of good practice in terms of innovation, social marketing and development through networking processes between the corporate world and education system. The proposed case focuses on quality of life improvement for school communities and families, through change management based on digital advances.Rasti is an Argentine company specialized in toy brick production. In 2019, the ERRE department was created to sell kits and services for education, in order to teach educational robotics and create digital opportunities for students. However, the 2020 SARS-CoV-2 pandemic forced ERRE to redesign and implement a new communication and relationship management strategy. This case study shares innovation, communication and marketing practices to show how the company remained resilient through complexity via a social marketing strategy which improved teachers and student’s quality of life during mandatory lockdown.
Issues and Challenges Faced by SME’s in Sultanate of Oman during COVID-19: A Case Study of the Tourism Sector

The world economy has been greatly affected by Covid-19 and Oman has been among the countries facing challenges in the operation of SMEs in handling of business needs and meeting regular fixed costs. The SMEs most affected are those in the tourism sector where travel restrictions and lockdowns could not allow the businesses to receive visitors as expected who are the customers to the services and products offered. Using the sample size of 187 participants who own or work at the SMEs in tourism sector, it was established that the applied legislations and restrictions of movement had a positive correlation to SMEs business operations with a positive correlation coefficient of 0.478. However, a recommendation to the issues and challenges were issued stating that SMEs should invest in technology &amp; tools, creation of an oversight of SME financial status, prioritising of value over culture, and trusting &amp; empowering the people that deal with SMEs.
Complex signal constellations in cumulantsbased AMC: Statistics and performance

In this paper various complex signal constellations are considered in the context of Automatic Modulation Classification (AMC) based on a higher-order normalized cumulants value. Most of the constellations have been addressed so far with fourth-order cumulants as AMC features only. The goal of this paper is to provide comparable values of sixth-order cumulants' statistics for complex signal constellations as well, while resolving ambiguity in constellation shapes addressed at the same time and directing towards the criterion for estimation of expected classification performance.
ProphetChain: Hostaging the Blockchain with Ransom Block Release

Mining in proof-of-work blockchains is a lucrative practice, and revenue driven mining strategies in these blockchains, adversarial or otherwise, have been extensively explored. We present a new revenue maximization attack suite named ProphetChain, where a rational attacker leverages Blockchain Denial-of-Service (BDoS) to paralyze the blockchain network, with the intent of forcing the victim miners to pay a ransom before the network can regain liveness. We detail the ProphetChain attacks, and outline an analytical framework for optimal ransom transaction valuation as a function of the advantage of the attacker in the blockchain system.
Short videos on the “TikTok” platform as a means of developing sociocultural skills in English language learners

The tense epidemiological situation caused by the extremely rapid spread of COVID-19 has accelerated the transition of educational institutions to distance learning, which, in turn, has entailed colossal changes in the choice of teaching tools and made pedagogical innovations an in-tegral part of the educational process. However, the importance of online communities in learning was noted even before the transition to distance learning, since many teachers used social net-works as an effective channel of communication with their students. In addition to communication, social platforms can be used to achieve other goals, including solving educational problems. Despite the fact that social platforms were initially created for group communication and creativity and were not associated with education, every year they are becoming an increasingly common means of exchanging information and organizing the educational process. Social platforms may help managing a wide variety of educational tasks and solve the problem of organizing a productive learning process. The study considers the role of videos in foreign language teaching and the educational potential of short videos from TikTok, their advantages and possibility of integration into the educational process in order to increase the efficiency and quality of education, and the role of the teacher in organization of such activities.
Compression Algorithm for End-to-End Communication using CNN

Autoencoder has been applied in end-to-end physical layer communication, and its performance exceeds that of traditional communication systems in some scenarios. However, autoencoder has very high computational complexity and requires a lot of storage resources. This makes them difficult to deploy on embedded systems with limited hardware resources. In this paper, we introduce model compression algorithm for the end-to-end communication system composed of convolutional neural networks (CNNs). Firstly, the pruning algorithm for end-to-end communication system is designed to delete the weight coefficients that are not important to the performance. Secondly, according to the characteristics of autoencoder, we design a codebook-based quantization scheme to further reduce the memory consumption. In the case of Gaussian, Rayleigh and bursty fading channels, the experimental results show that the number of weight coefficients and the required resources are greatly reduced after compression, without performance degradation in low Eb/N0 and negligible performance degradation in high Eb/N0.
The Effect of Female College Students' Perception of VR on the Expectation of VR-using Beauty Education

In the era of the 4th industrial revolution, various 4th industrial technologies are being used in the beauty industry. Therefore, education courses using the 4th industrial technology are needed for beauty education as well. Therefore, this study tried to apply to beauty education by empirically examining the perception of VR and expectations for VR applied beauty education among beauty major learners. A total of 106 beauty-related learners from 1st to 4th grade participated in the study. As a result of analyzing the perception of VR, there was a statistically significant difference according to the grades (p&lt;.01), and the higher the grade, the higher the perception of VR. In addition, the perception of VR was found to have a statistically significant (p&lt;.001) positive (+) correlation with educational, immersion, interest, and deviance by sub-factors of VR applied beauty education expectations. It was found that the higher the awareness, the higher the educational, immersive, interesting, and deviant factors of VR applied beauty education expectations. These results confirm the learners' expectations for the operation of beauty courses using VR and the necessity of developing VR-using beauty courses in the beauty education environment. Therefore, in beauty education, it is necessary to quickly realize the development and operation of beauty education courses using VR in line with the 4th industrial revolution era, thereby reviving the expectations of learners. For follow-up research, it is necessary to develop a VR-using beauty course or study after the course is operated.
Syntactic Analysis of Graphical Information Using Recursive Scoping

The report presents a new method for structuring, segmentation, and algorithmic design of the character array parser using VRML data as an example. The key feature of the method is the possibility of forming a hierarchically complex object by means of recursive data structuring, which allows you to cover the entire contents of the object, including its arbitrary nesting of child objects. This leads to a highly manageable development of the parsing algorithm, allowing you to focus each time on a specific piece of data, while not losing sight of the entire aggregate coherence of the information. The results obtained can easily be used in plans for creating convenient data storage structures related to information security, solving the problem of containing the amount of data in files, data management problems in heterogeneous systems, and hierarchical data solutions in the Internet of Things.
Regional-Confidence Weight for Accurate Multi-Label Image Retrieval

Recently, most methods proposed for multi-label image retrieval focus on using hashing functions to improve retrieval efficiency. However, hashing functions may reduce retrieval accuracy and be sub-optimal for some scenarios where accuracy is more important than efficiency. In this paper, we propose a deep feature descriptor without hashing for multi-label image retrieval. We use Faster R-CNN with FPN to perform object detection on images and extract feature maps. Especially, we propose Regional-Confidence Weight, which can evaluate the importance of different regions, to generate more powerful feature maps. Additionally, GeM pooling is used to produce compact feature vectors from feature maps for retrieval. Extensive experiments demonstrate that our approach significantly outperforms several state-of-the-art methods with higher retrieval accuracy for multi-label image retrieval.
Registration of Lithium Battery x-ray Images Based on an Improved RANSAC Algorithm

Image registration and stitching are required in the defect detection of lithium batteries. However, existing image registration methods will suffer from a large number of mismatches caused by similar textures and structures in lithium battery x-ray images. In order to address the problem, we propose an improved RANSAC (random sample consensus, RANSAC) algorithm, which is optimized based on the structure characteristics of lithium battery. When solving the relationship model, this method calculates the matching quality of the matching pair based on the longitudinal pixel distance of the matching pair, and then eliminates the wrong matching point pairs according to the matching quality of the matching pair, thereby reducing the number of mismatched pairs. Experiments show that the algorithm proposed in this paper can eliminate obvious mismatched pairs, and the registration accuracy of lithium battery X-ray digital images is improved by 20.5% on average.
Digital Forensics Process of an Attack Vector in ICS environment

Industrial control systems (ICS) can be exposed to cyberattacks with potentially catastrophic consequences. Intrusion detection is a fraud prevention technique derived from big data that play a key role in detecting attacks at the earliest stage. Data historian is essential to understanding all events and activities across the network. This article introduces the basic mechanisms by which common attacks on ICS can be detected and analyzed through different forensic tools. We explored the common vulnerabilities and potential attack vectors present in critical infrastructures and described measures that can be deployed to mitigate those threats. We discussed several common attack scenarios and artifacts that a forensic analysis of an affected ICS device can recover to help diagnose an attack. An ICS test lab was implemented and used to examine the common attacks. A menu driven set of forensic tools specific for ICS was developed to allow the extraction and analysis of the resulting attack vector.