Reviewerly/exHarmony huggingface dataset:
Typingwristband: A Human Slight Motion Sensing System Based on Vibration Detection

With the widespread of Human-Cyber-Physical Systems (HCPS), the fine-grained human movement detection becomes more and more important. Especially for the slightly motions of human’s hands, they are not only bring abundant information, but also provide a new way for the interaction between users and systems. In this paper, we focus on the problem of how to detect the human’s typing motion, and designed a new system, named as Typing Wristband, to obtain the vibration of wrist using piezoelectric transducer (PZT). Then, a robust denoising, event detection and classification algorithms are proposed to deal with the signal collected by Typing Wristband and detect the typing motions. Typing Wristband can recognized the movements of 3 fingers and 9 keys with high accuracy. Furthermore, it is very cheap and can be embedded into existing smart devices, e.g. a smart watch, so that it supports the wireless sensing very well in practice. Both of the analysis and experimental results verify that our Typing Wristband has the better performance in terms of accuracy and convenience.
Comparative Study of Supervised Machine Learning Algorithms for Healthcare Dataset Using Orange

Machine learning algorithms are special computer programs that improve their efficiency through experiences. It is the combination of statistics and computer algorithms that is used for identifying the hidden pattern and forecasting. In this paper, seven supervised machine learning algorithms are compared with three datasets of heart disease patients, diabetes patients, and depression patients. These seven algorithms are decision tree, logistic regression, K-nearest neighbor, support vector machine, Naïve Bayes, random forest, and Adaboost are implemented on Orange. Orange is an open-source graphical user interface platform for the implementation of machine learning algorithms. From the study, it is found that logistic regression and Naïve Bayes algorithm shown better results as compared to other algorithms of average accuracy of 81.23% and 79.65%. Support vector machine does not fit with these types of classification and performed accuracy of 56.34%. As an inference, SVM should not be implemented on such classification problems.
Subdomain Takeover : A Challenge as Web App Vulnerability or Server-Side Vulnerability

A subdomain is a domain that is a part of another domain. Subdomains are used to organize and navigate to various parts of your website. For example, your primary domain could be “xyz.com,” while your blog could be on a subdomain at “blog.xyz.com.” A subdomain takeover occurs when an attacker gains control over a subdomain of a target domain. Sub-domain takeover vulnerability occurs When a subdomain (subdomain.example.com) that refers to a service (eg GitHub, AWS / S3, ..) is deleted or deleted In this way, the attacker can create pages on the service in use and forward the pages to this subdomain.. If any person wants to take over, a subdomain then the person seeks to manually check one by one subdomain that takes too much time. Moreover, are there some tools available to check the subdomain takeover is possible or not? However, these tools take input as a text file, which has a particular subdomain. This means finding a subdomain with the other tools and then using one of these tools to identify subdomain takeover vulnerability. In my tools, we find the subdomain of a particular domain, then check the CNAME is available for a list of subdomains and if CNAME finds for a specific subdomain, then check the status code of the CNAME if it returns 404-status code. We might say that a particular subdomain is possible to takeover.
Super Resolution for Remote Sensing Images via Improved Residual Network

According to the processing characteristics of remote sensing image super-resolution, this paper studies a super resolution method based on improved residual network. First, we optimize the structure of the residual block to meet the needs of super-resolution tasks; then, we further deepen the network level, so that the network has a stronger learning ability. The reconstruction results on the remote sensing images dataset show that the improved residual network achieves better visual effect, and the objective evaluation index is significantly improved, which proves the effectiveness of the proposed method.
Formation of Skills of Professional Creative Activity

The article examines the experience of forming the foundations of professional creative activity of students, on the example of training specialists in educational institutions of secondary vocational education of a technical profile. The influence of the experience of participation of students in professional competitions is considered. A number of theoretical provisions are proposed that allow to form the creative activity of students from the perspective of a competence-based approach. The research carried out acquaints with the competent composition of the activities of future specialists. The analysis of specific activities, production requirements and educational standards made it possible to single out such components as technological, rationalization, modernization, organizational and management activities in the future graduate's activities. The potential possibility of optimizing the system of secondary vocational education is analyzed.
Multiple Event Detection using Minimum Inputs

To go with the current trend of using smart environments or Internet of Things (IoT) and to make the human life easier, many systems have been developed up to date which use special purpose sensing or direct sensing approaches. However, these approaches are expensive due to the need to affix sensors on each and every equipment and also they lack flexibility of usage due to being battery powered. As a solution for these issues, based on the concept of synthetic sensors introduced by a previous research work, a general purpose sensing approach is discussed by the present study. The system can be placed in a corner of a room and will only be taking a maximum of three sensors into account to detect many events happening across the room making it rather cost-effective. Furthermore, performance of several machine learning (ML) algorithms is analysed by this work. With real time testing of the proposed system, the possibility of applying this approach as a cost-effective solution to be used in the day-to-day work is proven.
Webform Optimization using Machine Learning

Forms are used in websites for improving business and user experience[3]. If the form length is big, then we get more information but information is less accurate and vice versa. Nowadays, A/B testing algorithm is being used for getting the optimal web form from a number of web forms to choose. A/B test distributes the forms uniformly to the visitors. The winning form will be the form which has more no. of conversions. So, after deploying new version of form, A/B test explores all the forms equally[1]. Even though some forms do not perform well, it still explores which is wastage of time and resources. So, there is more time given for exploration in A/B test. And also owner of website during testing period focuses more on testing than running the website, due to which there may be a loss of visitors. To overcome this problem, we can use some automated machine learning algorithms to predict the optimal web form from a number of forms to choose. These algorithms does not waste time and resources exploring inferior forms due to which we will get the results in less time and also we can simultaneously run the website.
An Intelligent Bangla Conversational Agent: TUNI

This research paper represents a method of a man–machine communication system named ‘TUNI’, where TUNI acts like a psychologist and continuously asks questions based on the response of the user. It is important in Bangla natural language processing as it processes Bangla text and responses like a human. In this paper, a new rule-based method is used to extract the input information and to map them with output. After preprocessing the input text, it is checked with the rules of the corpus. If a rule is matched, then one corresponding response rule is selected from the corpus. Then, the response rule is processed and given to the user as output using the user interface. In this method, most of the time, TUNI produces syntactically correct responses.
Augmented Reality Systems and Their Future in Tourism: Before, During and After the Journey

Abstract In this chapter, we describe Mixed Reality (MR) systems and applications in tourism from the perspective of when they support tourists: before, during or after the journey. MR technologies have been around for five decades and have seen many developmental stages and breakthroughs. However, only in the past two decades have the technologies been available to the wider public. As such, they provided a platform to be used in tourism as well. We present a review following the PRISM methodology by which we uncovered 92 relevant applications, prototypes, and services. These were labelled based on the time when the provided functionalities would normally be used, e.g. before, during and/or after the journey. The review highlights that the majority of prototypes and applications support the travel as it happens (89 out of 92 systems) whereas functionalities that support users before and after the visit only start to emerge later on as the field matures (9 and 11 out of 92, respectively). Therefore, MR applications that support tourist applications before and after the journey offer an untapped potential for future MR applications, prototypes and services. We provide possible directions in which this domain could evolve in the future.
User study of an AR reading aid system to promote deep reading

We proposed a reading aid system combining AR HMD and occlusion technology to cultivate students' intensive reading habits and promote deep reading. A user study was conducted to explore the differences in reading comprehension and subjective evaluation between AR and VR mode. Analysis results show that there are statistically significant differences for realism factors in subjective evaluation between the two modes. The participants confirmed the usefulness of the AR system and preferred the setting of actual paper reading. These results indicate there is potential for AR reading aid system to be used as an educational aid to promote deep reading.
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks

The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human “visual tracking” capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update. Our TS-RCN can be integrated with existing deep learning based visual trackers. To further improve the tracking performance, we adopt a “wider” residual network ResNeXt as its feature extraction backbone. To the best of our knowledge, TS-RCN is the first end-to-end trainable two-stream visual tracking system, which makes full use of both appearance and motion features of the target. We have extensively evaluated the TS-RCN on most widely used benchmark datasets including VOT2018, VOT2019, and GOT-10K. The experiment results have successfully demonstrated that our two-stream model can greatly outperform the appearance-based tracker, and achieves state-of-the-art performance. The tracking system can run at up to 38.1 FPS.
Novel Method for Key Storage Systems

To ensure for the security of PKI users' private keys, a new method for accessing and storing private keys is proposed in this article. Using advanced features of watermarking and symmetric encryption, the shared private key slices are encrypted and stored in different boxes, and the entire private key is reconstructed as long as the boxes are effective. The secure performance of storing and accessing private keys is also presented in the scheme. At the same time, the legal users can be verified by system using biometric or signature authentication.
한국 중도입국 청소년 공교육진입의 문제점에 관한 연구

Based on the experiences of youth who went on to public education and factors for adapting to public education this study believes that it is possible to examine the importance of the role of preparatory schools by revealing the factors of their impact on entering public education. this study aims to draw policy implications through literature research on the factors of their experiences in preliminary schools and how their experiences in preliminary schools affect public education adaptation. Until now, a proper status survey has not been conducted in Korea. because it is difficult to calculate accurate statistics due to the diverse status of children from multicurtural families. Through cooperation from government ministries and private organizations, the government should conduct a nationwide survey on the status of youth and make accurate statistics. The expansion of multicultural pre-schools is needed to help multicurtural youth enter public education. (Healthy Family·Multi-Cultural Family Support Center of Gimhae Office)
Art-techniques in the preparation of a musician-performer

The authors of the article consider one of the directions of musical pedagogy - the use of art pedagogy in the process of training a musician-performer. Based on the analysis of the research literature and experience describes the possibility of using psychological reserves of music and performing activities, in particular, reception of musical color modeling of the emotional and figurative content of music, are revealed.
Which HCI Lab in South Korea Fits Best for Me? : Designing an HCI Lab Browsing System to Support Prospective Students’ Decision-Making Process

Prospective students looking for a human-computer interaction (HCI) laboratory that fits their needs and goals go through a challenging decision-making process. Often, labs and their body of work are ambiguous to the student. In this study, we aim to (1) design an HCI Lab browsing system named <HCI LAB DIRECTORY> and (2) investigate how the system supports decision making. We designed a system that can browse laboratories through research topics based on author-defined keyword (ADK) data from publications. We then conducted a user study, including an information browsing task and in-depth interviews. Findings show that our system supported decision making in (1) identifying what to look for at the initial stage and (2) developing one's own criteria by gaining saliency of the field at the drill-down stage. Our findings provide empirical understanding regarding decision-making theory and presents a new browsing system that organizes scattered and unstandardized information.
GhostBuster: understanding and overcoming the pitfalls of transient execution vulnerability checkers

Transient execution vulnerabilities require system administrators to evaluate whether their systems are vulnerable and whether available mitigations are enabled. They are aided in this task by multiple community-developed tools, transient execution vulnerability checkers. Yet, no analysis of these tools exists, in particular with respect to their shortcomings and whether they might mislead administrators into a false sense of security. In this paper, we provide the first comprehensive analysis of these tools and underpinning methodologies. We run the tools on a large set of combinations of Intel/AMD architectures and Linux kernel versions and report on their efficacy and shortcomings. We also run these tools on 17 of the most prominent cloud providers, report the collected results and present the current status on the preparedness of the IT hosting industry against this class of attacks. Finally, we present a new tool called GhostBuster, that combines methodologies and results gathered by existing tools to provide a more accurate view a system&#x2019;s stance against transient execution attacks for a given use case.
Automatically mining Relevant Variable Interactions Via Sparse Bayesian Learning

With the rapid increase in the availability of large amount of data, prediction is becoming increasingly popular, and has widespread through our daily life. However, powerful nonlinear prediction methods such as deep learning and SVM suffer from interpretability problem, making it hard to use in domains where the reason for decision making is required. In this paper, we develop an interpretable non-linear model called itemset Sparse Bayes (iSB), which builds a Bayesian probabilistic model, while simultaneously considering variable interactions. In order to suppress the resulting large number of variables, sparsity is imposed on regression weights by a sparsity inducing prior. As a subroutine to search for variable interactions, itemset enumeration algorithm is employed with a novel bounding condition. In computational experiments using real-world dataset, the proposed method performed better than decision tree by 10% in terms of r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We also demonstrated the advantage of our method in Bayesian optimization setting, in which the proposed approach could successfully find the maximum of an unknown function while maintaining transparency. Apart from Bayesian optimization with Gaussian process, i SB gives us a clue to understand which variables interactions are important in optimizing an unknown function.
Defending Against Misspeculation-based Cache Probe Attacks Using Variable Record Table

Meltdown and Spectre attacks exploit speculative execution in a processor to leak sensitive data that would otherwise be inaccessible. Existing countermeasures based on temporary patches come at the cost of significant performance overhead. In this work, we present a novel approach to detect misspeculation based cache probe attacks. For a given function call, our approach keeps track of the misspeculative cache accesses and flags any accesses outside of the function. A variable record table (VRT) which is inaccessible to the programmer, is employed for such purpose. We validate our approach with SimpleScalar/PISA toolset for six (6) benchmarks chosen from MiBench benchmark suite. Experimental results demonstrate that our approach detects illegal misspeculative accesses with zero additional instruction overhead. The VRT with 512 entries (25Kb) incurs an area and power overhead of 7.98% and 10.22%, respectively with no penalty in time due to parallel search mechanism.
Enabling the Disabled using Machine Learning and Natural Language Processing

Humans communicate with each other through natural language channels such as words and writing, or through body language (gestures) such as hand and head gestures, facial expression, lip motion, etc. There are some examples of natural languages that people use to communicate with each other. We all know that understanding natural language is essential, learning sign language is also very important. For disable people, sign language is the primary communication method for hearing. As there is no translator for communicating among them, so they face problems in communicating. So this is the motivation for us to create a system that recognizes sign language to impact deaf people's social lives significantly.
Exploring Distance Based Approaches for Reducing Sensor Data in Defect Related Prognosis

Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors.
Research on Semantic Similarity of Entities with the Case of Event Knowledge Graph

Evaluating entity similarity is a common problem in recommendation systems. In the business field, entity similarity generally uses a collaborative recommendation calculation method to recommend object features and user features. However, in the military field, especially for the recommendation of military knowledge graph entities, more consideration needs to be given to the information implicit in the association between entities. Therefore, the existing similarity calculation model needs to be supplemented and improved. Knowledge graphs have important practical significance in the management, visualization, analysis and reasoning of military data. On the basis of the knowledge graph, we propose a framework for calculating the asymmetric similarity of the domain knowledge graph, using as much as possible the attribute constraints of the ontology design and the convenience of long-distance reasoning of knowledge graph. War consists of various campaign events and this article takes the war knowledge graph as an experiment example. This model WKGSM that has been proposed draws on the traditional entity similarity calculation framework, considering the attribute value of the entity, the relationship between the entities, and the number of sub-entities. The framework adopts a three-layer structure to decompose big problems into small ones. Finally, the three-layer scores are merged through the AHP. The parameters of the fusion function and the coefficients of the similarity matrix can be modified according to the needs of the actual problem. The experimental results show the effectiveness of the method. In addition, this article also discusses the use environment and limitations of the model.
Crossing the Tepper Line

Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially embodied AI. We propose an ontological heuristic for describing the threshold: the Tepper line. We reinforce our theoretical work with expert insights from a card sort workshop. We end with two case studies to illustrate the dynamic and contextual nature of this heuristic.