Reviewerly/exHarmony huggingface dataset:
Smart Intelligent Drone for Painting Using IoT: An Automated Approach for Efficient Painting

There are many occasions, a few people may die because of lack of efficient resources, lack of technology, and dependent more on human efforts when painting for high buildings and high temple arches. Whenever the high buildings and temples arches need to be coloring, they consume more time and a greater number of labor to complete it. The finishing of the given task depends on many external factors. To be independent of many external factors, a smart drone with loaded colors is assigned and loads the texture that is expected as output. As the intelligence is loaded to the drone as well as the specific sensors are embedded to it in order to notify the information about the color’s deficiency is detected, any resource to be required to complete the given task according to the given texture, any accidental collisions also notified and be a safeguard, and etc. The output of this study is an array that allows analysis on how many are identical textures, coloring were processed done using SSIM measure. The major advantages are manpower is becoming almost NIL while been painting; the time consumed for the task given is to be completed in less time. The accuracies and their performances are measured and are depicted in the results when comparing this approach against the traditional and semi-automated approaches.
Development of Android Applications and Its Security

Smartphones have gotten to be a major portion of a human’s life. These days’ versatile applications are playing a major part in numerous zones such as keeping money, social organizing, money-related apps, amusement, and so on. For each desktop or Web application, a substitute portable app is accessible. With fair single press number of versatile apps are accessible from Google’s play advertise. With this tremendous number of applications, security is a critical issue. This investigative article talks about the security of the applications and the malevolent apps that will influence or spill delicate information such as Worldwide Portable gear Character Number (IMEI) of gadget, credit or charge card data, area data, and so on. As the Android showcase is developing, security hazard has expanded, and, in this way, a center ought to be given to security.
3D printed children's prostheses as enabling technology? The experience of children with upper limb body differences

Purpose This paper examines the extent to which 3D printed children's prostheses function as enabling technology. The focus lies on the experiences of children with upper limb body differences using 3D printed prostheses in the context of (posthuman) cyborg theories. Design/methodology/approach This article is based on several years of field research applying a grounded theory approach. (Health) technology and the body are examined with special regard to the vulnerability of the technology user who is, also, the technology designer. Taking these children's particular vulnerability and sensitivity into account, the method of “cultural probes” was further developed applying distributed socio-(bio-)technical probes, which conceive soma design as the matter of a socio-material world. Findings It was shown that the e-NABLE device is not only a socially enabling somatechnic but can itself be limiting, vulnerable and painful for children due to its materiality. The somatechnical construction of children's bodies and identities are presented as heroic figures, which, in part, produces and experiences a corporeal being that is based on and identifies with these heroes and heroines – but may not always be in the interests of children with disabilities. In order to meet these children's needs, the author argues in line with crip technoscience that 3D printed prostheses should be co-developed with (and specifically for) them. Originality/value This paper is the first of its kind to consider the daily lives of children with 3D printed prostheses and their experiences as knowers and makers of such. This paper adds to the body of knowledge in the field of crip technoscience and enabling technologies. Peer review The peer review history for this article is available at: https://publons.com/publon 10.1108/JET-02-2022-0017
The Possibility of Vibrodynamic Data from Phone Accelerometers for the Rapid Assessment of the Technical Condition of Buildings and Structures Use

The possibility of diagnosing buildings and structures, from the frequencies of natural oscillations obtained from sensors built into the phone was examined. A special application program to read data from phone sensors was written. Experimental studies on registration of building vibrations using telephone sensors and a software–hardware complex with external accelerometers have been carried out.
Development of hyperspectral database and web based classifying system for rock type identification

In geological surveys, it is necessary to employ geological experts in determining rock samples correctly, this is however very costly and time consuming. Sinaice et al. (2020) demonstrated how employing hyperspectral data of igneous rocks and machine learning can be used in classifying rocks without any knowledge or background in geology. Although machine learning is able to achieve high accuracy with a sufficient amount of data (teaching data), there are no existing big-data-sets of rock hyperspectral data. In order to improve the accuracy and robustness of our machine learning model, we collected a large amount of spectral data of various types of rocks as an attempt to solve the machine learning bottleneck by creating web application that is able to share hyperspectral data among users. We created a web application that allows users to upload hyperspectral image data of rocks taken by various users, determine the rock type using our trained machine learning model, and subsequently browse the spectral database of the Mining Museum of Akita University. The machine learning model is capable of automatically improving its accuracy as data is uploaded by various users. Hence, each user is able to use the database and the determination function. By using this web application, it is possible to collect spectral data from a wide variety of rocks from various users of the web application, thereby improving the accuracy and robustness of rock type determination using hyperspectral data and machine learning, hence solving the aforementioned difficulties borne by researchers in previous studies.
Hostile-emotional excess of zeal in public social media: A case study of an online firestorm against an organization

Caused by perceived norm violations, online firestorms confront organizations with large volumes of hostile-emotional comments on public social media leading to a damage to reputation or the cancellation of products and projects. Relying on social norm theory we analyze how people express perceived norm violations in their online comments and how this relates to their use of hostile-emotional online sanctions. We distinguish negative externalities; propriety judgements; excess of zeal, which combines negative externalities with propriety judgements; and no justification, meaning no speculations about why norm violations occurred, as four types of motive for hostile verbal expression. Using hostile-emotional sanctioning is differently associated with these motives: (1) weak association with negative externalities to maintain credibility; (2) moderate association with propriety judgements as a result of altruistic punishments; (3) moderate association with no justification, triggered by arousal; and (4) strong association with an excess of zeal because norm enforcers believe that a latent group exists which rewards them with positive sanctions for working toward the common goal and punishes them with negative sanctions for shirking. We analyze one specific online protest signed by 305,122 people that led to a massive hostile-emotional firestorm against an organization. We find that 37% of the 44,173 individuals who additionally commented their protest participation were hostile and/or emotional. As predicted, we find that compared to the other motives, the excess of zeal is most likely to motivate hostile-emotional sanctions. Overall, our theory and findings explain why most online firestorms are hard to stop: with an excess of zeal, a latent group of norm enforcers must be appeased.
Personal Examples of Sounds and Spectra as Symbols

Abstract This chapter discusses how Jacob Sudol’s compositional practice has primarily relied on sounds and spectra to convey extra-musical meaning. In particular it describes how the composer created a personal musical language in which specific sonic and musical materials function as complex symbols that progressively reveal themselves and interact with each other throughout time. This chapter also describes the extra-musical inspirations for a number of these symbols, as well as how they have developed across multiple works. The chapter concludes with a suggestion that using spectral techniques as symbols or hierophanies can potentially be an efficacious approach to imbue compositions with greater spiritual, transcendental, or just experiential depth.
Research Trends in Education in the Context of COVID-19 in Spain: A Systematic Literature Review

COVID-19 has caused many difficulties worldwide, education being one of the most affected areas. This research aims to disseminate the research trends about education in the context of COVID-19 in Spain. Through a systematic review, all research works about this topic in the Spanish context, that were published between 2020–2022 in national and international high-impact journals, have been analysed. After analysing 242 articles, the results show: (a) the keywords that were used most frequently: “learning” (93); “teaching” (53); “higher education” (43); “pandemic” (30); “competence” (29); and “ICT” (22); (b) research trends, which can be categorised as the following topics, among others: “ICT and Digitalisation”; “Teaching, Learning, and Innovative methodologies”; “Educational Policies”; “Sustainability”; “Acquisition and Development of Skills and Competences”; “Health and Welfare”; (c) that the most popular topic in educational research in Spain was “Teaching, Learning, and Innovative Methodologies” (19.30%), followed by “ICT and Digitalisation” (18.04%), whereas articles about educational policies were a minority (2.85%); (d) that in Spanish educational research, articles about formal education have been the most popular (86.36%), followed by articles about non-formal education (7.02%) and articles about informal education (6.62%). Consequently, the scientific community has highlighted the impact of the pandemic on education, especially in relation to “Teaching, Learning, and Innovative Methodologies”.
WhatsApp as a University Tutoring Resource

Tutoring, as a process of accompaniment of university students, has among its purposes identification of and reflection on the various difficulties that can interfere in the development of student competencies. To the same end, the present research explores the possibility of using WhatsApp as a tutoring resource in universities, with an emphasis on identifying and reflecting on the academic, personal, and family problems of students. This case study uses a quasi-experimental approach with a treatment group and a control group intentionally selected at the Escuela Profesional de Educación Secundaria de la Universidad Nacional del Altiplano de Puno, Peru, during the first semester of the 2020 academic year. Data were collected using semi-structured interview guides and individual tutoring cards. The results show that this technological resource improved the university tutoring service among the students in the experimental group. It proved more flexible and attractive to students, who were motivated to externalize and reflect on their personal, family, and academic difficulties and showed increased openness to receiving help in problem solving them. On the other hand, student participation was less active in the control group. These findings affirm that WhatsApp is a resource that can allow more spontaneous and friendly communication between tutors and students, creating a context conducive to achieving the objectives of university tutoring.
Success and Failure Factors Affecting Software Development Projects from IT Professionals’ Perspective

During the last few years, there has been an increase in the number of failed software development projects. As such, it has become a matter of interest for researchers who are trying to find the right formula and ways in order to ensure project success. Normally, a project’s success or failure depends on how project team members abide by the best project management principles. This research makes a critical appraisal of existing works of researchers and further investigates on the factors that contribute to the success or failure of a software project. A non-probability sampling technique was carried out among software development companies in the Republic of Mauritius, and an online survey was used to collect data. Cross tabulation is performed to identify the factors that impact on the success or failure of software projects. Finally, a set of guidelines and best practices have been proposed. The results obtained from this research consolidate on what has been observed in existing literature while adapting it to the context of Mauritius, where the ICT industry is one of the pillars of the economy. Some of the success factors which were identified were clear statement of requirements, adequate user involvement, and proper planning, whereas some of the failure factors included lack of clear goals from top management and unrealistic objectives and expectations.
Smart Question Paper Evaluation: Automated Approach Using the Jaccard Similarity

Nowadays, after the examination is completed, the manual dependent on evaluation may lead to delay the process of announcing the results. There is a semi-automated process so far which leads to certain activities are manual, and other activities are automated. The correlated activities like assigning to each booklet, each question answers were translated into the text using integrated sensor for reading and conversion, provide weighted marks according to the matched content against the key of scheme of valuation, and sum the marks of attempted questions, update that total marks of that booklet at the booklet number. This automated dumping and all the tasks involved during this approach are connected one after the other. If any of negative scenarios such as invalid question numbers, invalid contexts are suppose be identified, would be notified to the controller and respective authorities for immediate follow up and resolution. The factors considered such as performance and accuracy are made significant in judging the evaluation, as well as update the revaluation schedule. The significant modules involved here are uploading of key of scheme of valuation, similarity matching between optimized text and key context of respective questions, assigning of marks weightage for the similarity, summing up of marks excluding the choice, update total marks in the results report. Once negative scenarios are all rectified and resolved, gets the approval, then only results to be notified to the respective year and semester, specific category of examination to which those students are registered.
Cross-World Covert Channel on ARM Trustzone through PMU

The TrustZone technology is incorporated in a majority of recent ARM Cortex A and Cortex M processors widely deployed in the IoT world. Security critical code execution inside a so-called secure world is isolated from the rest of the application execution within a normal world. It provides hardware-isolated area called a trusted execution environment (TEE) in the processor for sensitive data and code. This paper demonstrates a vulnerability in the secure world in the form of a cross-world, secure world to normal world, covert channel. Performance counters or Performance Monitoring Unit (PMU) events are used to convey the information from the secure world to the normal world. An encoding program generates appropriate PMU event footprint given a secret S. A corresponding decoding program reads the PMU footprint and infers S using machine learning (ML). The machine learning model can be trained entirely from the data collected from the PMU in user space. Lack of synchronization between PMU start and PMU read adds noise to the encoding/decoding ML models. In order to account for this noise, this study proposes three different synchronization capabilities between the client and trusted applications in the covert channel. These are synchronous, semi-synchronous, and asynchronous. Previously proposed PMU based covert channels deploy L1 and LLC cache PMU events. The latency of these events tends to be 100-1000 cycles limiting the bandwidth of these covert channels. We propose to use microarchitecture level events with latency of 10-100 cycles captured through PMU for covert channel encoding leading to a potential 100× higher bandwidth. This study conducts a series of experiments to evaluate the proposed covert channels under various synchronization models on a TrustZone supported Cortex-A processor using OP-TEE framework. As stated earlier, switch from signaling based on PMU cache events to PMU microarchitectural events leads to approximately 15× higher covert channel bandwidth. This proposed finer-grained microarchitecture event encoding covert channel can achieve throughput of the order of 11 Kbits/s as opposed to previous work's throughput of the order of 760 bits/s.
Research on unsupervised anomaly data detection method based on improved automatic encoder and Gaussian mixture model

Abstract With the development of cloud computing, more and more security problems like “fuzzy boundary” are exposed. To solve such problems, unsupervised anomaly detection is increasingly used in cloud security, where density estimation is commonly used in anomaly detection clustering tasks. However, in practical use, the excessive amount of data and high dimensionality of data features can lead to difficulties in data calibration, data redundancy, and reduced effectiveness of density estimation algorithms. Although auto-encoders have made fruitful progress in data dimensionality reduction, using auto-encoders alone may still cause the model to be too generalized and unable to detect specific anomalies. In this paper, a new unsupervised anomaly detection method, MemAe-gmm-ma, is proposed. MemAe-gmm-ma generates a low-dimensional representation and reconstruction error for each input sample by a deep auto-encoder. It adds a memory module inside the auto-encoder to better learn the inner meaning of the training samples, and finally puts the low-dimensional information of the samples into a Gaussian mixture model (GMM) for density estimation. MemAe-gmm-ma demonstrates better performance on the public benchmark dataset, with a 4.47% improvement over the MemAe model standard F1 score on the NSL-KDD dataset, and a 9.77% improvement over the CAE-GMM model standard F1 score on the CIC-IDS-2017 dataset.
Privacy Attributes Aware Framework in Information Disclosure Models for Public During Pandemic

Pandemics are a bane to the developing society. But the ability of human race to withstand the difficulties posed by the pandemics is worth more than anything. It has been nearly two centuries since pandemics are raving through the needs and lives of people of all countries. As technology has evolved, we are becoming more and more self-equipped to handle the adverse effects of the pandemic. So, the information disclosure models have come to the rescue of people who are getting engulfed in the vicious circle of pandemics. There are numerous benefits of information disclosure models as well as pitfalls to be covered while using such models. It is easier said than done. For this same purpose, we have proposed privacy attributes aware information disclosure model. This model as the name indicates is aware of the privacy attributes which effect the extent to which information will be disclosed by the model. We have developed an algorithm for the same where a metric which indicates the degree to which private data will be released is an indicator of the robustness of the proposed model. We have compared our proposed model with popular information disclosure models and infer that our proposed privacy attribute aware model has edged past the popular models by nearly 2%.
Smart Patterns: Searching a Pattern in the Unknown Environment Using Hybrid Framework

Nowadays, there exist many video datasets that consume more in size. Searching for a certain pattern in the video dataset became very difficult. Hence, the proposed system takes certain patterns and searches the videos that consist of such patterns. This system requires converting the appropriate video into the text and saves such files as a respective video file name with suffix text as a keyword. Such a conversion should be done efficiently and manage the files in the cloud. Searching multiple patterns in the set of videos and displaying which videos consist of which patterns. These statistics are much useful for further analysis. Such analysis is helpful in justifying which videos are useful for their concern field. The advantages obtained are searching in an unknown environment such as a video dataset, producing the videos only for the given patterns, and methodology is made is simple and efficient. Not only destination type is video, but it could be even other types also. In such a situation also, the pattern will be processed in that environment and generates a report. Here, efficient text mining occurs on any video dataset. In the future, the input could be taken as audio-only and searching that given audio bit in the given audio file.
The Application of Diversified Blended Models in Teaching British and American Culture Based on Big Data Analysis

Language is a symbol reflecting ideas and is an important medium for people to realize information transfer and communication. Culture is all the material and spiritual wealth created during the historical development of human society. Language and culture are inseparable. When we learn a language, we must also understand the cultural content it contains. Learning English requires an understanding of the culture of British and American countries, and thus teachers must necessarily teach culture in the English classroom. At present, the software and hardware of Internet technology in our country have been developed in-depth, and the total amount of big data has been in the trend of growth, and China has officially entered the era of big data. This paper takes the reform of teaching mode of British and American culture courses as the main research object and explores how to build a diversified and blended teaching mode based on the existing teaching mode and integrating various online teaching resources under the premise of various technologies in the background of “big data.” In order to accelerate the integration and optimization of such course resources, promote the development of theoretical teaching and practical teaching of such courses and improve students’ professional ability.
Introduction

With the advent of Cloud Computing, Big Data, and Internet of Things, the twenty-first century can be fairly seen as an age of data and unearthing patterns from them. Everywhere, the move is toward extracting information from data and embedding intelligence in devices/applications like smartphones, security systems, home appliances, etc. Such advances indeed help us to make our lives easier, more efficient, and resourceful.