Reviewerly/exHarmony huggingface dataset:
A General Framework Information Loss of Utility-Based Anonymization in Data Publishing

To build anonymization, the data anonymizer must determine the following three issues: Firstly, which data to be preserved? Secondly, which adversary background knowledge used to disclosure the anonymized data? Thirdly, The usage of the anonymized data? We have different anonymization techniques from the previous three-question according to different adversary background knowledge and information usage (information utility). In other words, different anonymization techniques lead to different information loss. In this paper, we propose a general framework for the utility-based anonymization to minimize the information loss in data published with a trade-off grantee of achieving the required privacy level.
A Benchmarking application on Workload and Performance forecasting of micro services

Micro services are increasingly understood as the ideal architectural framework for building large cloud applications within and beyond organizational boundaries. These micro services architectures scale up the application, but are expensive to work on, so pay attention to workflow planning and workflow planning. However, this issue is not very clear. In this work, we are developing independent micro services workflows suitable for modeling and prediction methods and designing three-step game models for based applications. Solved the problem of designing micro services based applications to reduce end-to-end delays under user-specific limitations (MAWS-BC) and recommended micro services routing algorithms. The design process and estimation methods are improved and adequate. The experimental results produced by a well-known micro service bank cover a wide variety of statistical analyzes and the production utility of graphic design is shown by a large comparison copy compared to current algorithms.
FDEIR: Content-Based Image Retrieval using Fast Demeanor Ensemble Features

The demand for CBIR-based systems is increasing day-by-day with the increase of its applications. Nowadays, various digital information systems like Medicine, Digital Libraries, Biodiversity information systems, Fingerprint Identification, crime prevention, and many more are in trend, and all these systems need accuracy. Several Automated Systems were earlier developed using features to improve the systems' accuracy, but it introduces complexity and speed. A fast Demeanor Ensemble features-based approach is proposed to cope with this issue, which also deals with accuracy, speeds up the system, and reduces its complexity. Different shape-based features are used to form 3-set composite features, followed by the selection phase where the ALO algorithm is utilized and reduces system complexity. The accuracy of the proposed system is estimated using distance-based matching methods. Correl-1000 dataset is used to analyze the system's performance based on different metrics, and the results show the proposed system's ability.
Comprehending Personal Experience: Implementing Buzz Group in Teaching Reading

The students’ reading comprehension of SMA Negeri 2 Tumijajar is still low especially in reading personal recount text. To solve the problem, the researcher applied Buzz Group. The objective of this research is to know whether there is a significant influence of using buzz group towards students’ reading comprehension on personal recount text at the eleventh grade of SMA Negeri 2 Tumijajar in the academic 2018/2019. The research methodology was quasi experimental design. In this research, the population was the eleventh grade of SMA Negeri 2 Tumijajar. The sample of this research was two classes consisting of 33 students for experimental class and 33 students for control class. In the experimental class, the researcher used buzz group and in the control class the teacher used think pair share. The treatments were held in 3 meetings in which 2 x 45 minutes for each class. In collecting data, the researcher used instrument in the form of multiple choice questions which had been tried out prior to the treatments. The instrument was given in pre-test and post-test. The researcher analyzed the data using SPSS to compute independent sample t-test. From the data analysis computed by SPSS, it was obtained that Sig. = 0.002 and α = 0.05. It means Ha is accepted because Sig. &lt; α = 0.002 &lt; 0.05. Therefore, there is a significant influence of using buzz group towards students’ reading comprehension on personal recount text at the eleventh grade of SMA Negeri 2 Tumijajar in the academic year 2018/2019.
Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds

The problem of Multi-Agent Path Finding (MAPF) calls for finding a set of conflict-free paths for a fleet of agents operating in a given environment. Arguably, the state-of-the-art approach to computing optimal solutions is Conflict-Based Search (CBS). In this work we revisit the complexity analysis of CBS to provide tighter bounds on the algorithm's run-time in the worst-case. Our analysis paves the way to better pinpoint the parameters that govern (in the worst case) the algorithm's computational complexity. Our analysis is based on two complementary approaches: In the first approach we bound the run-time using the size of a Multi-valued Decision Diagram (MDD)--a layered graph which compactly contains all possible single-agent paths between two given vertices for a specific path length. In the second approach we express the running time by a novel recurrence relation which bounds the algorithm's complexity. We use generating functions-based analysis in order to tightly bound the recurrence. Using these technique we provide several new upper-bounds on CBS's complexity. The results allow us to improve the existing bound on the running time of CBS for many cases. For example, on a set of common benchmarks we improve the upper-bound by a factor of at least 2 to the power of 10,000,000.
Interpersonal Intelligence: A Strengthening in Efforts to Improve Student Learning Achievement

The purpose of this study was to determine the relationship between interpersonal intelligence and student learning outcomes so that there is an increase and strengthening of student achievement. The method used in this research is field research with a correlational quantitative approach which aims to analyze the relationship between interpersonal intelligence and student achievement. The results of this study found that there is a significant relationship between interpersonal intelligence and student achievement. This can be proven from statistical calculations, namely r count is greater than r table (0.995&gt; 0.347) with a significant stage of 5%. Thus the alternative hypothesis (Ha) in this study is accepted and the hypothesis (H0) in this study is rejected.
Comparative Study of Digital literacy in Language Learning among Indonesian Language Education and English Language Education Students in the New Normal Era

The impact of the pandemic of Covid 19 forces students to stay on top of online learning.Technology with reasonable sophistication can be used better by English education students than Indonesian Language Education students because, in addition to language acquisition, information and applications in digital are generally Englishspeaking and are more numerous and easily obtained.This study aims to describe digital literacy in language learning among Indonesian Language Education students and English Language Education students in the new normal era.This research is a quantitative descriptive study with qualitative data verified in the form of a percentage.The data was obtained using Google forms for each of 100 students (200 samples) in Java and Sumatra randomly and by purposive sampling.The instrument uses closed questions with a Likert scale (five options) and open questions.Research results show that digital literacy in language learning among Indonesian Language Education students and English Language students in the new normal era is not significantly different, at the same level.Digital literacy in language learning is at level 4, that is, students have been able to improve significantly (quantitatively stated) the performance of daily life activities through the use of information and technology with an average bind of 3.59 (71.8%) and bind 3.46 (69.2%).It shows that learning in networks has increased students' mastery and digital literacy abilities.
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?

An in-depth analysis of the level of language understanding required by existing Machine Reading Comprehension (MRC) benchmarks can provide insight into the reading capabilities of machines.In this paper, we propose an ablation-based methodology to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations.We define seven MRC skills which require the understanding of different discourse relations.We then introduce ablation methods that verify whether these skills are required to succeed on a dataset.By observing the drop in performance of neural MRC models evaluated on the original and the modified dataset, we can measure to what degree the dataset requires these skills, in order to be understood correctly.Experiments on three large-scale datasets with the BERT-base and ALBERT-xxlarge model show that the relative changes for all skills are small (less than 6%).These results imply that most of the answered questions in the examined datasets do not require understanding the discourse structure of the text.To specifically probe for natural language understanding, there is a need to design more challenging benchmarks that can correctly evaluate the intended skills 1 .
CR CyberMar as a Solution Path towards Cybersecurity Soundness in Maritime Logistics Domain

Cybersecurity is now considered as one of the main challenges for the maritime sector. At the same time, the maritime transport industry remains one of the most relevant and driving sectors for the global economy in terms of both the number and operations of active companies, and infrastructure and investments, thanks to the policies pushed to attract the latter. Maritime information systems, whether on board ships or in ports, are numerous, built with standard components available on the market and in many cases designed without factoring in well the ever-growing cyber risk. Digital infrastructure has become essential in operating and managing systems critical to the safety and security of shipping and ports. Specifically, Cyber-MAR is focused upon the simulation and emulation of the real world of maritime systems (e.g. Logistics, Supply Chain). This research effort will examine the creation of a federated Cyber Range (CR Cyber-MAR) which will include various platforms and interconnected systems on board a vessel or ashore, in order to allow a hyper-realistic simulation of cyber-attacks and trying to assimilate them into real-life. Then the identified CR Cyber range will be integrated in the Cybersecurity training needs for different levels of operators. The investigation of the discussed topic will essentially use qualitative techniques, analysing data obtained from publications, official and commercial reports, and interviews of a targeted audience.
Augmented Analytics for Data Mining: A Formal Framework and Methodology

Since the emergence of business intelligence in decision-making, efforts to automate decision-making capabilities relating to data preparation, data analysis, and data visualization have continued with varying degrees of success. Much of these activities have been independently performed, lacking an architecture for integrated development and implementation. Such decision-making environment, besides remaining largely manual, is also prone to individual biases. As the need to process data volumes is increasing exponentially and sourced cross-functionally, decision-making is increasing in complexity. Further, as the dimensionality of data is increasing owing to the number of variables driving an outcome or best action, it is becoming either impossible or impractical to gain valuable insights in decision-making using existing analytics approaches. This has led to biased, inferior, and untimely decision-making. The primary objective of this open research challenge is to explore the promising area of Augmented Analytics in big data that has the promise to harness features and capabilities of both Machine Learning and Natural Language Processing to offer an integrated toolbox that enables executing search-based analytics and conversational analytics in unison. In this chapter, a framework and methodology for Augmented Analytics is described that has its roots in machine learning and natural language processing fields of research. It unifies capabilities of machine language algorithms and natural language processing to offer a new and innovative paradigm of augmented analytics in decision-making.
Implementation and Effectiveness of New Technologies in Housekeeping Department

When everything from front workplace to food and liquid has started the new technology within the same time work department additionally go with new technology they must not be in the age. In nowadays in building and hospital or any tourist sector work can not be outlined as cleanup and maintaining totally different surfaces there's a such a lot issue on the far side that additionally. currently days every and each trade is functioning through a technology-driven transformation and there's no totally different in cordial reception industry additionally. In these topics we tend to are planning to highlights the present challenges that we face from the worker and therefore the best practices on new technologies which may be innovated for the building trade, and if it goes well then we are able to apply this new technologies effectively within the building in work department. it's the upper growth and the and therefore the and additionally the cut tools for the building and hospital trade also. These articles delineated the teachers and up to date building work technologies effectiveness. This new technological innovation within the work service is made public on very cheap of the model guest cycle. fine quality technological service is that the pillar of the work department it will create the work easier. building work have to be compelled to be a IT savvy housekeeping, cross coaching for the worker, TV, radio lightweight curtain area service laundry assortment every and each issue that is an element of the work are going to be controlled from one device. If these all are often implementing in a very systematic manner within the cordial reception trade then it will cause be an enormous opportunities and future profit for the hospital industry.&#x0D;
Dependency parsing with structure preserving embeddings

Modern neural approaches to dependency parsing are trained to predict a tree structure by jointly learning a contextual representation for tokens in a sentence, as well as a headdependent scoring function.Whereas this strategy results in high performance, it is difficult to interpret these representations in relation to the geometry of the underlying tree structure.Our work seeks instead to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree.We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser.We provide a thorough evaluation of these geometric losses, showing that the majority of them yield strong tree distance preservation as well as parsing performance on par with a competitive graph-based parser (Qi et al., 2018).Finally, we show where parsing errors lie in terms of tree relationship in order to guide future work.
INDIVIDUAL IDENTIFICATION SYSTEM DESIGN THROUGH VOICE USING LINEAR PREDICTIVE CODING METHOD AND K-NEAREST NEIGHBOR

Humans have a variety of characteristics that are different from one another. Characteristics possessed by humans are genuine which can be used as a differentiator between one individual and another, one of which is sound. Voice recognition is called speech recognition. In this study, it was developed as an individual voice recognition system using a combination of the Linear Predictive Coding (LPC) method of feature extraction and K-Nearest Neighbor (K-NN) classification in the speech recognition process. Testing is done by testing changes in several parameters, namely the LPC order value, the number of frames, the K value, and different distance methods. The results of the parameter combination test showed a fairly good presentation of 73.56321839% with the combination parameter or LPC 8, the number of frames 480, the value of K 5, with the distance method used by Chebychev.
Optimize Cryptography Algorithm for Efficient Data Security on Cloud Computing

Services in the cloud environment are distributed between all servers and users. Cloud providers have problems with file protection as security is a major problem when processing and transferring information, as the original data type can be viewed, misused and lost. In the cloud computing world, cloud protection is a major concern. A variety of research projects are planned to safeguard the cloud climate. Cryptography is used to address the security problem and to achieve the CIA (confidentiality, honesty and disponibility). The most effective technique for ensuring high data transfer and storage protection is cryptography. There are certain drawbacks in traditional symmetric and asymmetrics. We will introduce a new technique of hybrid data protection and confidentiality to solve this issue. We use the ECC and Blowfish to build a hybrid algorithm in this article. The hybrid scheme output is compared to the current hybrid technique and demonstrates the high safety and confidentiality of the patient data in the proposed method. The hybrid encryption is used to remove both symmetrical and asymmetrical drawbacks.
ROBUST K-NEAREST NEIGHBOR CLASSIFIER AND NEAREST MEAN CLASSIFIER BASED INFORMATIVE KNOWLEDGE DISTILLATION FACE RECOGNITION

Human Face identification procedures have made tremendous progress in the last decade. Nevertheless, identifying faces with incomplete impediment is as yet challenging for the present face identifiers, and is very much required within certifiable application programs regarding reconnaissance and protection. Though great examination exertion has been dedicated to creating face de-impediment techniques, the greater part of them can just function admirably under obliged conditions. In this manuscript is proposed a Robust K-NNC (K-Nearest Neighbor Classifier) and NMC(Nearest Mean Classifier ) (RKNNC-NMC) prototype to efficiently reestablish incompletely occluded faces even in nature. This model comprises of two-stream, first introduced to perceive high-resolution faces and goal corrupted appearances with a student stream and a teacher stream, separately. A Teacher stream is signified by a Complex RKNNC-NMC for the sake of high-exactness recognition, and the student stream is signified by an a lot more straightforward RKNNC-NMC for low-unpredictability recognition. Broad examinations on synthetic and real datasets datasets of countenances with impediment plainly show the viability of RKNNC-NMC in eliminating various kinds of impediment in one’s face at different locations. The suggested technique additionally gives better behaviour gain than other de-occlusion strategies in advancing recognition execution through partially-occluded faces.
Hashing based Data Transaction and Optimized Storage for IoT Applications

Blockchain technology, which would be the underlying technology, has recently become very popular with the increase in cryptocurrencies and is being used in IoT and other fields. There have been shortfalls, however, which impede its implementation, including the volume of space. Transactions will be produced at a significant level due to the huge amount of Connected systems that often work in many networks as data processors. In IoT, the storage issue will become more intense. Current storing data platforms have a wide range of features to respond to an extensive variety spectrum of uses. Nevertheless, new groups of systems have arisen, e.g., blockchain with data version control, fork semantics, tamper-evidence or some variation thereof, and distributed analysis. They're showing new challenges for storage solutions to effectively serve such energy storage Systems by integrating the criteria mentioned in the processing. This paper discusses the potential security and privacy concerns of IoT applications and also it is shown that in first step the storage is enhanced by 50% and further in the next step, it is improved and it takes only 256 bytes irrespective of the input data size.
Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches

Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the learnt vectors and returns the top-k items from the ranked list. For preference elicitation, there have been numerous works to build accurate MF-based recommendation algorithms that can learn from large datasets. However, for the recommendation retrieval phase, naively scanning a large number of items to identify the few most relevant ones may inhibit truly real-time applications. In this work, we survey recent advances and state-of-the-art approaches in the literature that enable fast and accurate retrieval for MF-based personalized recommendations. Also, we include analytical discussions of approaches along different dimensions to provide the readers with a more comprehensive understanding of the surveyed works.
An Optimized Approach for Image Segmentation on Mobile Devices

Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.
Environmental Earth Sciences Progress Report 2020 and Outlook 2021

The present editorial 2020 continues the series of status reports in Environmental Earth Sciences (EES) in previous years 2017 and 2019 (Kolditz et al. in Environ Earth Sci 77: 8, 2018, Kolditz et al. in Environ Earth Sci 79: 11, 2020). The year 2020 coming to an end was heavily influenced by the COVID-19 pandemic affecting all areas of life including research work and, therefore, scientific publishing as well ("Introduction"). One bright spot which shows longevity of journals that produce a quality product is that Environmental Earth Sciences (EES) is celebrating its 45th anniversary of publication. To this extent EES continues the tradition to honor the most cited papers contributing to the 2020 Impact Factor (IF) ("Highly and most cited topics") and provide information on the current status of EES as well as an outlook to 2021 ("Progress report").