Reviewerly/exHarmony huggingface dataset:
PINBALL: Universal and Robust Signature Extraction for Smart Home Devices

Event-level signatures for smart home Internet-of-Things devices are the fundamental of many security enhancement systems for home networks equipped with smart home systems. However, existing event signature extraction methods for smart home devices are in a dilemma where the precision, generality and robustness of the signatures cannot be satisfied at the same time. In this paper, we present PINBALL, a universal tool that can automatically extract robust signatures for events happening on smart home devices. PINBALL uses length distributions of bidirectional packets generated by smart home devices as signatures for their trigger-events. It is lightweight and independent of the protocols. Besides, the signatures extracted by PINBALL are more robust and achieve better detection accuracy than previous state-of-the-art methods. Nevertheless, we argue that such signatures are double-edged swords that can not only help with the network management but also threaten the user privacy. We analyze these signatures from a comprehensive perspective and try to propose some possible measures to make such packet length side channels able to be used for legitimate purposes like security enhancement with the user privacy preserved.
Collaboration Is the Key to Success in Life and the Water Sector

Journal AWWAVolume 113, Issue 6 p. 8-9 From the President Collaboration Is the Key to Success in Life and the Water Sector Chi Ho Sham, AWWA PresidentSearch for more papers by this author Chi Ho Sham, AWWA PresidentSearch for more papers by this author First published: 02 July 2021 https://doi.org/10.1002/awwa.1742Read 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume113, Issue6July/August 2021Pages 8-9 RelatedInformation
A CORRELATIVE STUDY OF STUDENTS’ READING INTEREST TOWARD EXPOSITION WRITING SKILL IN THE TENTH GRADE OF SMKN 3 KARAWANG

This study aims at determining whether there is a positive relationship between reading interest and writing skill to make exposition text in the tenth grade of SMKN 3 Karawang. The study subjects are 85 respondents. A descriptive quantitative approach with correlation analysis used in this research. Data collecting techniques are tests and questionnaires. Technical analysis of prerequisite test data obtained from the normality test, homogeneity test, and linearity test by using SPSS 16. Hypothesis test obtained from simple correlation by using SPSS 16 with a significance level of 5% (α =0.05). The study result consists of (1) A normality test showed Asymp.Sig (2-tailed) = 0.875 > α = 0.05. So, the variables' data spread normally. (2) A homogeneity test showed Sig= 0.412 > α = 0.05. So, the variables' data are homogeneous. (3) Based on linearity test result, the equation is Ŷ= 50.045 + 0.369 X. (4) Based on correlation test result, T count = 5.028 > T table = 1.662. It means that there is a positive relationship between students’ reading interest and exposition wiring skill in the tenth grade of SMKN 3 Karawang. It can be concluded that if reading interest is improved, students’ exposition text writing skills will also increase.
PrIoT Demo: Example of Invariant Functionalities

This paper presents a simple IoT scenario that uses the concept of application invariant functionalities. We propose to implement our scenario with the PrIoT framework that provides a lightweight approach when designing intermediate abstraction layers with minimalist programming functionalities that are IoT application invariant.
Development of Interactive Learning Media Based on Mobile Learning in Computer Network Subject

Abstract . The purpose of this study is to produce a mobile learning-based learning media application. This study uses a type of research and development (R&D) with a 4-D development model and software quality testing based on ISO 9126 which focuses on three characteristics, namely functionality, portability, and usability. Funcitionality testing was carried out using 2 validators from UNM media expert lecturers, from the test results obtained a 100% success percentage and categorized as very good. Usability testing was carried out using 30 respondents from PTIK UNM students, from the results of these tests obtained very good responses to the use of the application. Portability testing is carried out on several types of android smartphones, learning media applications can run well. For the overall characteristics of ISO 9126 tested it shows that the application is feasible to use. Keywords: interactive learning, mobile learning, computer network
Serious Game for Caloric Burning in Morbidly Obese Children

A new report on childhood obesity is published every so often. The bad habits of food and the increasingly sedentary life of children in a border society has caused an alarming increase in the cases of children who are overweight or obese. Formerly it seemed a problem of countries with unhealthy eating habits, such as the United States or Mexico in Latin-America, where junk food is part of the diet during childhood. However, obesity is a problem that we already have around the corner and that is not so difficult to fight in children. In the present research, the development of an application that reduces the problem of the lack of movement regarding the children of a smart city is considered a future problem which It is the main contribution, coupled with achieving an innovative way of looking for an Olympic sport without the complexity of physically moving to a space with high maintenance costs and considering the adverse weather conditions. We use Unity to model each Avatar associated with a set of specific sports, such as Water polo, Handball, Rhythmic Gymnastics and others.
Lifelong learning of librarians in pandemic times

The 11th International Conference on Information Science and Information Literacy – ICISIL 2021 – will take place in Brașov, Romania, on the 11th and 12th of March, 2021. The conference will be organized by Transilvania University of Brasov, Lucian Blaga University of Sibiu, with the support of The Public Library of Brașov County and MB Think Tank. ICISIL 2021 will focus on: The Role of Information in Services Provided to Innovative Users. ICISIL 2021 will bring together professionals, academic staff, including doctoral students, with a focus on Information Sciences, along with policy-makers and stakeholders. The conference fosters an inter-disciplinary exchange of knowledge. Sessions include contributed papers, keynote presentations and panel discussions. We support PhD students in the field through reduced registration fees. In addition, a PhD workshop will be held in the morning of 12 March 2021, as part of the ICISIL 2021 annual conference, at no extra cost for all registered students. Moreover, ICISIL 2021 will host a workshop on Innovative Technologies, focusing on 3D printing. Please submit your article via Editorial Manager Editorial Manager ABSTRACTING & INDEXING 11th International Conference on Information Science and Information Literacy is covered by the
The Brief Study on Emerging Trends in Robotic

Robotics and artificial intelligence technology are disruptive innovation which provides significant growth opportunities and the potential disrupt existing everyday economic and social aspects. The future has not yet been written and who knows whether or not robots are dangerous. What's certain is that people will create new generations of robots, being the curious beings. The concern for humanity has always been strong for robots and other high-level innovations. Most scientists have invested all their lives in their laboratories to create creative strategies, improve them and build a highest quality robot. The technological drive that has taken place because of the efforts of a number of talented, consistent intellects of scientists around the world has brought this integration process its height. The most striking dreams in this area have been successfully realized and a significant number of robots support the job or production process automatically. This happens so that in this lifetime people and robots go hand in hand, are compatible even in certain realms of life, and who knows what this opposition Human and translates into. The future of the robots is pure conjecture, but the relentless advancements in technology are a foregoing inference, taking into account developments in recent years. Robots will certainly also affect different aspects of our lives and the implications for the human race will remain debated by scientists and philosophers. As the advancement of artificial intelligence progresses, robots may be superior humans. Irrespective of the future, robots will be there.
Prediction-Based Power Oversubscription in Cloud Platforms

Datacenter designers rely on conservative estimates of IT equipment power draw to provision resources. This leaves resources underutilized and requires more datacenters to be built. Prior work has used power capping to shave the rare power peaks and add more servers to the datacenter, thereby oversubscribing its resources and lowering capital costs. This works well when the workloads and their server placements are known. Unfortunately, these factors are unknown in public clouds, forcing providers to limit the oversubscription so that performance is never impacted. In this paper, we argue that providers can use predictions of workload performance criticality and virtual machine (VM) resource utilization to increase oversubscription. This poses many challenges, such as identifying the performance-critical workloads from black-box VMs, creating support for criticality-aware power management, and increasing oversubscription while limiting the impact of capping. We address these challenges for the hardware and software infrastructures of Microsoft Azure. The results show that we enable a 2x increase in oversubscription with minimum impact to critical workloads.
Lineament structural analysis of REE-Th-U-deposits within the Ukrainian and Canadian Shields

Summary Structural mapping, including lineament analysis, is an important geological tool used to identify the prevailing tectonic trends within a study area. Lineaments are straight to curvilinear landforms that are widely distributed across the Earth’s surface and are closely related to underground concealed structures, such as faults, shear zones, and folds. In this study, lineament extraction was carried out using both manual visual identification and automatic extraction approach with the suitable software. The results obtained are giving additional insight to the REE-Th-U mineral systems of the two Ukrainian and Canadian shield areas and providing vectoring guidelines for new discoveries.
Cybersecurity Posture of Higher Learning Institutions in Tanzania

In a bid to become a middle-income country by 2025, the Government of the United Republic of Tanzania promoted the productive use of ICT for rapid development, which is why Higher Learning Institutions (HLIs) adopted information systems to handle admission matters. Urgent response to directives without enforcing credible cybersecurity measures is a massive security vulnerability that cyber attackers can exploit. This study assessed the cybersecurity preparedness of HLIs in protection, detection, and response to cyber-attacks using the NIST cybersecurity framework. To achieve this objective, a qualitative method and an interpretive research approach were adopted. The target population was four (4) Higher Learning Institutions located in Arusha Municipality. A purposive sampling technique was used due to an inadequate number of cybersecurity experts in the population. Empirical data were collected using semistructured, face-to-face interviews, documentary reviews, observation, penetration testing and analyzed using content analysis. Despite implementing minimal countermeasures, the study discovered that Higher Learning Institutions are vulnerable to cyber-attacks. Higher Learning Institutions must be vigilant by; addressing identified weaknesses, providing cybersecurity training to all staff, and continuously monitoring information systems. The study recommends a need to explore the factors affecting cybersecurity preparedness in Higher Learning Institutions.
Improving Webserver Security for Local Tax Reporting Applications Using Standard Penetration Testing Execution Methods

Regional Tax Reporting Application Webserver is one of the public services for taxpayers to report their sales transactions. This application can be accessed on the domain http://sptpd.payakumbuhkota.go.id. This application is public, so the principles of information security must be applied to prevent cyber attacks. The principles of information security include confidentiality, integrity, and availability. To apply this information security principle, it is necessary to conduct vulnerability assesment of the application webserver. This study aims to improve the security of the application webserver so that the data and information in it is secure. The method used in this study is the Penetration Testing Execution Standard which is one of the methods developed by the Pentest Organization to become a standard in analyzing or auditing security systems. The results of vulnerability testing using software Acunetix, Nikto, BurpSuite and Owasp, there are seven types of vulnerabilities, namely: X-Frame Header Options is Missing, CSRF Attack, Cookie Without Only Flash, DNS Vulnerability, Ddos Attack, Bruteforce Page Login and Open Port. The vulnerability can be exploited, where the level of application vulnerability is in the medium category. The recommendations for fixing vulnerabilities can be applied by the developer, so that after repairs are made, the vulnerability level of the application webserver is in the low category and there is only one type of vulnerability, namely BruteForce Page
Media Education in Modern Society: Didactic Features and Methodology

The article examines the features of the sociological study of the phenomenon of mediatization. The role of media in modern society is explained. The main theoretical approaches to the study of the phenomenon of mediatization are given, the influence of social media on social processes is proved.
Advanced Learning Classifier Systems

tutorial Advanced Learning Classifier Systems Author: Prof Will Browne Queensland University of Technology Queensland University of TechnologyView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 669–691https://doi.org/10.1145/3449726.3461429Published:08 July 2021Publication History 0citation83DownloadsMetricsTotal Citations0Total Downloads83Last 12 Months23Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable. This paper presents variational estimates of the score function and its gradient with respect to the model parameters in a general EBLVM, referred to as VaES and VaGES respectively. The variational posterior is trained to minimize a certain divergence to the true model posterior and the bias in both estimates can be bounded by the divergence theoretically. With a minimal model assumption, VaES and VaGES can be applied to the kernelized Stein discrepancy (KSD) and score matching (SM)-based methods to learn EBLVMs. Besides, VaES can also be used to estimate the exact Fisher divergence between the data and general EBLVMs.
Modular Irreducible Representations of the FpW4-Submodules of the Modules as Linear Codes, where W4 is the Weyl Group of Type B4

The modular representations of the FpWn-Specht modules  as linear codes is given in our paper [6], and the modular irreducible representations of the FpW4-submodules  of the Specht modules  as linear codes where W4 is the Weyl group of type B4 is given in our paper [5]. In this paper we are concerning of finding the linear codes of the representations of the irreducible FpW4-submodules  of the FpW4-modules  for each pair of partitions  of a positive integer n = 4, where Fp = GF(p) is the Galois field (finite field) of order p, and p is a prime number greater than or equal to 3. We will find in this paper a generator matrix of a subspace  representing the irreducible FpW4-submodulesof the FpW4-modulesand give the linear code of for each prime number p greater than or equal to 3. Then we will give the linear codes of all the subspaces  for all pair of partitions  of a positive integer n = 4, and for each prime number p greater than or equal to 3.We mention that some of the ideas of this work in this paper have been influenced by that of Adalbert Kerber and Axel Kohnert [13], even though that their paper is about the symmetric group and this paper is about the Weyl groups of type Bn.
The Use of Data Mining to Improve Breast Cancer Diagnosis

Healthcare organizations currently employ predictive analytics to improve patient care. Using data mining models to predict breast cancer is significant for improved care outcomes and patient experience.Providing data-driven decision-making process. Furthermore, historical records help to create models that aid disease detection at an early stage, which might also positively impact care outcome and patient experience. Breast cancer screening is a common service offered by healthcare providers. The study aims to evaluate the performance of five data mining algorithms for the prediction of breast cancer. Five data mining namely deep learning, naive base, generalized linear models, support vector machines and random forest are applied to predict breast cancer. Four metrics will be used to assess the performance and highlight the best classification model. Deep learning yielded higher performance results on all metrics and ranked the variables based on their importance in building the classifier. Deep learning algorithms can be used successfully to predict breast cancer. Glucose and Resistin were the most important in the classification process. Therefore, high levels of these variable need to be monitored.
COMPUTER VISION DETECTION OF SUBMERGED OBJECT THROUGH MACHINE LEARNING

Object Recognition is a widely-held innovation that distinguishes examples inside an image. InordertoeliminatethebarriersinComputerVisioninnovation because of the disintegration of the RGB (Red-GreenBlue) constituents with the increment inside and out,it has been a need that the precision and effectiveness of recognizing any item submerged is ideal.Inthisresearch,, we direct Submerged Item Recognition utilizing AI through Tensor stream and image Handling alongside Fast-RCNN (Faster Region-Convolution Neural Network) as a calculation for execution. An appropriate climate will be made so that AI calculation will be utilized to prepare various pictures of the submerged object. Open source PC Vision has different capacities which can be utilized for the picture preparing needs when a picture is Open source PC Vision has different capacities which can be utilized for the image preparing needs when an object is acquire.
Knowledge Sharing Technologies to Enhance Employees’ Performance in Organizations

ABSTRACTKnowledge sharing adoption has become the main criterion for any organization’s development. This study proposed a suitable collaborative model for implementing intelligent knowledge sharing techniques to enhance employees’ performance in GDE-Babylon, Iraq. The study also discusses potential knowledge-sharing barriers, categorizing them into three main domains of recently published works: individual/personal, organizational, and technological barriers. The research used a mixed method approach, combining both qualitative and quantitative data. The study also, utilizes the quantitative methodology in the form of data collected via a survey to identify the challenges faced by the employees in adopting knowledge sharing behaver. Additionally, the study adopts the qualitative method in the form of interviews conducted with four top management in GDE. The results of this study, most employees believe in the need to share knowledge but Knowledge sharing is not part of the GDE’s culture, supporting senior management is insufficient to provide an appropriate environment for knowledge sharing as well as lack financial support as there are no rewards or financial allocation for these activities even moral support is not encouraging. The authorities of GDE must be more seriousness to eliminate the concerns of some employees that sharing their knowledge with others will lose their strengths and position as well as they need invest in IT as one of the most important pillars of knowledge sharing.
Analysis of the Vegetation Index Dynamics on the Base of Fuzzy Time Series Model

Based on the fuzzy analysis of satellite monitoring data, the annual dynamics of the vegetation index for the selected crop area is investigated by means of MODIS images (LPDAAC – the Land Processes Distributed Active Archive Center). To reconstruct and predict the weakly structured vegetation index time series, the fuzzy models are proposed, compiled taking into account the analysis of internal connections of the first and second orders, which are presented in the form of fuzzy relations. The proposed models were investigated for adequacy and suitability from the point of view of the analysis of the peculiarities of the intra-annual mean annual dynamics of the index, typical for the cultivated area. On the basis of the proposed approach, the results of the study of long-term dynamics of vegetation indices can be used for a complex analysis of the dynamics of vegetation cover, including modeling and forecasting the efficiency and productivity of agricultural crops.