Reviewerly/exHarmony huggingface dataset:
A Deep Learning based approach to VM behavior identification in cloud systems

Cloud computing data centers are growing in size and complexity to the point where monitoring and management of the infrastructure become a challenge due to scalability issues. A possible approach to cope with the size of such data centers is to identify VMs exhibiting a similar behavior. Existing literature demonstrated that clustering together VMs that show a similar behavior may improve the scalability of both monitoring andmanagement of a data center. However, available techniques suffer from a trade-off between accuracy and time to achieve this result. Throughout this paper we propose a different approach where, instead of an unsupervised clustering, we rely on classifiers based on deep learning techniques to assigna newly deployed VMs to a cluster of already-known VMs. The two proposed classifiers, namely DeepConv and DeepFFT use a convolution neural network and (in the latter model) exploits Fast Fourier Transformation to classify the VMs. Our proposal is validated using a set of traces describing the behavior of VMs from a realcloud data center. The experiments compare our proposal with state-of-the-art solutions available in literature, demonstrating that our proposal achieve better performance. Furthermore, we show that our solution issignificantly faster than the alternatives as it can produce a perfect classification even with just a few samples of data, making our proposal viable also toclassify on-demand VMs that are characterized by a short life span.
TinBiNN: Tiny Binarized Neural Network Overlay in about 5,000 4-LUTs and 5mW

Reduced-precision arithmetic improves the size, cost, power and performance of neural networks in digital logic. In convolutional neural networks, the use of 1b weights can achieve state-of-the-art error rates while eliminating multiplication, reducing storage and improving power efficiency. The BinaryConnect binary-weighted system, for example, achieves 9.9% error using floating-point activations on the CIFAR-10 dataset. In this paper, we introduce TinBiNN, a lightweight vector processor overlay for accelerating inference computations with 1b weights and 8b activations. The overlay is very small -- it uses about 5,000 4-input LUTs and fits into a low cost iCE40 UltraPlus FPGA from Lattice Semiconductor. To show this can be useful, we build two embedded 'person detector' systems by shrinking the original BinaryConnect network. The first is a 10-category classifier with a 89% smaller network that runs in 1,315ms and achieves 13.6% error. The other is a 1-category classifier that is even smaller, runs in 195ms, and has only 0.4% error. In both classifiers, the error can be attributed entirely to training and not reduced precision.
Virtual Ground Truth, and Pre-selection of 3D Interest Points for Improved Repeatability Evaluation of 2D Detectors

In Computer Vision, finding simple features is performed using classifiers called interest point (IP) detectors, which are often utilised to track features as the scene changes. For 2D based classifiers it has been intuitive to measure repeated point reliability using 2D metrics given the difficulty to establish ground truth beyond 2D. The aim is to bridge the gap between 2D classifiers and 3D environments, and improve performance analysis of 2D IP classification on 3D objects. This paper builds on existing work with 3D scanned and artificial models to test conventional 2D feature detectors with the assistance of virtualised 3D scenes. Virtual space depth is leveraged in tests to perform pre-selection of closest repeatable points in both 2D and 3D contexts before repeatability is measured. This more reliable ground truth is used to analyse testing configurations with a singular and 12 model dataset across affine transforms in x, y and z rotation, as well as x,y scaling with 9 well known IP detectors. The virtual scene's ground truth demonstrates that 3D pre-selection eliminates a large portion of false positives that are normally considered repeated in 2D configurations. The results indicate that 3D virtual environments can provide assistance in comparing the performance of conventional detectors when extending their applications to 3D environments, and can result in better classification of features when testing prospective classifiers' performance. A ROC based informedness measure also highlights tradeoffs in 2D/3D performance compared to conventional repeatability measures.
Dimension Reduction for Projective Clustering

The high dimensionality of data may be a barrier to algorithmic efficiency, mainly because of the well known “curse of dimensionality” that imposes exponential time and/or memory complexity for algorithms. It is natural then to search for ways to break this curse by relaxing the problem with approximate versions and by finding good ways to reduce the dimension of data. Our aim is to integrate and state slightly stronger results of approximation via dimension reduction for clustering under the l_2^2 metric. The dimension reduction is achieved by combining randomized techniques (the Johnson-Lindenstrauss Lemma) and deterministic techniques (the singular value decomposition).
Towards a Process for NFRs Evaluation in IoT Applications

The Internet of Things (IoT) is a paradigm characterized by a variety of objects that interact with each other using the internet to achieve common goals. IoT applications are more and more present in our lives, either in our homes, or on the streets, or in our work environments. These applications bring new Non-Functional Requirements highly related to the user interaction quality, such as context-awareness, synchronicity, and calmness. The evaluation of these NFRs can be challenging, because we may have to take into account qualitative and quantitative aspects. In this way, the evaluators can be confused about the steps they need to follow and the best order to perform the NFRs evaluation. After conducting a systematic mapping study about the NFR evaluation in IoT applications, we could not find a process that systematizes these activities stepby-step. Therefore, this paper proposes a process for evaluating non-functional requirements in IoT applications. This process divides the quality evaluation into two aspects, qualitative and quantitative, to capture quality measurements and subjective aspects of the IoT applications. To verify whether the established process meets the evaluators' needs, we applied 2 questionnaires with the experts and, based on the obtained results, we made some adjustments and came up with a preliminary version of the process that is presented in this paper.
Thematic Development Analysis on Sunday Sermon Texts in Batak Christian Protestant Church

This research article investigates thematic development used on Sunday sermon texts in Batak Christian Protestant Church that were delivered by the preachers. Based on systemic functional linguistics with the framework of textual function, this research article focus on analysing how the thematic development constructed and construed on Sunday sermon texts. In addition, this research article looks and discusses how the preachers used and adapted the thematic development in delivering the Sunday sermon texts. This study was a descriptive qualitative. Model interactive data analysis was used in analysing the data. There were seventeen texts of Sunday sermon texts that were analyzed in this research article. The findings of this research article showed that only five patterns of thematic developments that were found from eight patterns of thematic development. This research article highlights how the usage of thematic development in delivering the Sunday sermon texts. It is also recommended that another preachers use thematic development in delivering their sermon to the congregation in understanding the sermon they delivered.
Implementation of the Regita Model to Improve Learning Activities of Fifth-Grade Elementary School Students

The target of the research implemented is to describe teacher activities, student activities and learning outcomes on the theme of events in the life of social studies through the REGITA model. The research was CAR type and the subject of the fifth-grade students of SD Alalak Selatan 2 Banjarmasin in 2019/2020 was 34 students. The research instrument was in the form of an observation sheet of teacher and student activities. Based on the implementation of the results of this research, it can be concluded that the REGITA model is able to increase the activities of teachers, students and student learning outcomes. Therefore, the hypothesis can be accepted.
Débruitage de maillages

We study in this paper four methods of mesh denoising: a linear filtering, a heat diffusion method, the Sobolev regularization and to a lesser extent the Sinkhorn algorithm. We show that on an image, the use of the Gibbs Kernel is counterproductive. We show that if the Sobolev regularization is the fastest of all, it is the one that makes the mesh less faithful to the original. We show empirically that for large meshes, the heat diffusion method is slower and less efficient than filtering, which is not the case for small meshes. We finally find that the first three methods work significantly better with large meshes.
Non-Malleable Code in the Split-State Model

Non-malleable-codes introduced by Dziembowski, Pietrzak and Wichs [DPW18] encode a classical message $S$ in a manner such that tampering the codeword results in the decoder either outputting the original message $S$ or a message that is unrelated/independent of $S$. Providing such non-malleable security for various tampering function families has received significant attention in recent years. We consider the well-studied (2-part) split-state model, in which the message $S$ is encoded into two parts $X$ and $Y$, and the adversary is allowed to arbitrarily tamper with each $X$ and $Y$ individually. We consider the security of non-malleable-codes in the split-state model when the adversary is allowed to make use of arbitrary entanglement to tamper the parts $X$ and $Y$. We construct explicit quantum secure non-malleable-codes in the split-state model. Our construction of quantum secure non-malleable-codes is based on the recent construction of quantum secure $2$-source non-malleable-extractors by Boddu, Jain and Kapshikar [BJK21].
Supplemental Material for Comparing Revised Latent State–Trait Models Including Autoregressive Effects

The Random Experiment in LST-R Theory LST and LST-R theory differ in their formulation of the random experiment.For the sake of simplicity, we considered a design with two measurement occasions.The random experiment defined in LST-R theory implies a temporal ordering of the elements in Ω.The set of possible outcomes Ω is defined as,where Ω Ut is the set of persons at the time of measurement t, and Ω Et is the set of experiences between measurements t = t -1 and t.Ω St is the set of situations at measurement t, and Ω Ot is the set of observations at measurement t.The person variable at measurement t=1 is defined by the mappingU 1 assigns the Cartesian product of Ω U 0 and Ω E 1 to each outcome Ω.In other words, a person at measurement time t = 1 results from the same person at measurement time t = 0 and the experiences the person has had in the meantime (Eid, Holtmann, Santangelo, & Ebner-Priemer, 2017; Steyer, Mayer, Geiser, & Cole, 2015).For measurement occasion t=2, the mapping isand Ω E 2 to each outcome Ω.In other words, a person at measurement time t = 2 results from the same person at measurement time t = 0, the experiences the person has had between t = 0 and t = 1, as well as the experiences the person has had between t = 1 and t = 2, the situation the person was in at measurement time t = 1, and lastly, the observations of the person at measurement time t = 1.An element ω of Ω implies that a person-at-time-1 (u 1 ) is a part of the-person-at-time-2 (u 2 ).Compared to u 1 , u 2 also encompasses the situation s 1 , the observations o 1 , and the experiences e 1 (Eid et al., 2017;Steyer et al., 2015).Lastly, the situation variable is defined for measurement t as the mapping S t : Ω → Ω St , meaning that
SISTEM PENILAIAN KINERJA PEGAWAI BERBASIS WEBSITE PADA SDIT BINA INSANI SEMARANG

&lt;p&gt;&lt;em&gt;Performance appraisal is a process to determine the quality of human resources in an institution, school, and company. In improving employee performance, SDIT Bina Insani Semarang conducts an assessment of employees as measured by assessment indicators. Employee performance appraisal is carried out by each head of the field with the aim of knowing whether employees have increased or decreased in performance. Based on the problems that exist in SDIT Bina Insani Semarang, which still uses a performance appraisal system with only one side, a system that is able to input performance values from two sides is needed, namely, an assessment system where employees input their own performance values, then the validator will validate. value for its performance. Therefore, the author will build an application that can simplify and assist the school in assessing employee performance, and in completing the writing, the author compiles this report by building an application that uses the PHP Native programming language and MySQL database so that later the results of testing the system carried out on the table questionnaire, the application built successfully runs all features and runs well.&lt;/em&gt;&lt;/p&gt;
Privacy-preserving method of edge computing based on secret sharing and homomorphic encryption

Edge computing has been applied to all aspects of our life, and has a good development prospect. But the problems of “isolated data island” and “data privacy security” hinder the development and application of technology. Therefore, based on federated learning, this paper proposes a privacy-preserving method based on secret sharing and homomorphic encryption, which can enhance the privacy protection of edge computing without affecting the accuracy of the model. At the same time, theoretical analysis proves that this method can resist collusion attacks and reasoning attacks.
Accurate face swap using cycleGAN

Face swap is to transfer a face image of one person to another given person. It can generate false but very realistic images and video data. At present, numerous frameworks have been proposed, such as CNN, FCN, GAN. Recently, GAN-based methods are popular with satisfying performance. In this paper, we aim to improve the GAN-based face swap methods under the cycleGAN framework and achieve a more realistic face-swapping result. In this method, cycleGAN is applied to to face swap task, which mainly solves the problems of unavailable paired training images, and our model can be trained without pairing real images. More than 1,000 pictures in total have participated in the model training. The test needs to input a video and output a video of the facial changes in a few minutes. We collect Hillary and Trump videos data on the google website for testing and analysis, obtain a trained model, and accurately change the face of the video. Our method can handle face swap effectively with reasonable speed. The quality and speed of its generation are no less than that of other frameworks like GAN, which is more popular nowadays.
Performance Improvement of Image-Reconstruction-Based Defense against Adversarial Attack

Deep Neural Networks (DNNs) used for image classification are vulnerable to adversarial examples, which are images that are intentionally generated to predict an incorrect output for a deep learning model. Various defense methods have been proposed to defend against such adversarial attacks, among which, image-reconstruction-based defense methods, such as DIPDefend, are known to be effective in getting rid of the adversarial perturbations injected in the image. However, this image-reconstruction-based defense approach suffers from a long execution time due to its iterative and time-consuming image reconstruction. The trade-off between the execution time and the robustness/accuracy of the defense method should be carefully explored, which is the main focus of this paper. In this work, we aim to improve the execution time of the existing state-of-the-art image-reconstruction-based defense method, DIPDefend, against the Fast Gradient Sign Method (FGSM). In doing so, we propose to take the input-specific properties into consideration when deciding the stopping point of the image reconstruction of DIPDefend. For that, we first applied a low-pass filter to the input image with various kernel sizes to make a prediction of the true label. Then, based on that, the parameters of the image reconstruction procedure were adaptively chosen. Experiments with 500 randomly chosen ImageNet validation set images show that we can obtain an approximately 40% improvement in execution time while keeping the accuracy drop as small as 0.4–3.9%.
Decision-making method for railway emergency based on prospect theory

Aiming at the problems of randomness and fuzziness in the railway emergency，and multiple scenarios in the evolution of the railway emergency, a multi-attribute group decision-making method of railway emergency is proposed based on prospect theory. Firstly, the uncertainty transformation of linguistic decision-making information is realized using the qualitative and quantitative transformation characteristics of cloud model. Secondly, the cloud distance and the cloud possibility degree are defined according to the concept of interval number, then the cloud prospect decision matrices of each decision-maker are constructed. The attribute weights of each decision-maker are obtained through establishing the attribute weight optimization models based on the principle of maximum prospect value of every alternative. Thirdly, the decision-maker weights are determined based on group’s consistency and the maximum entropy, and the best alternative is obtained by comparing the whole prospect value of alternatives. Finally, the validity and feasibility of the method are verified by an example.
Tanz-Indicator: A Novel Framework for Detection of Perso-Arabic-Scripted Urdu Sarcastic Opinions

Automatic sarcasm detection in textual data is a crucial task in sentiment analysis. This problem is complex because sarcastic comments usually carry the opposite meaning and are context-driven. The issue of sarcasm detection in comments written in Perso-Arabic-scripted Urdu text is even more challenging due to limited online linguistic resources. In this research, we proposed Tanz-Indicator, a lexicon-based framework to detect sarcasm in the user comments posted in Perso-Arabic Urdu language. We use a lexicon of over 3000 sarcastic tweets and 100 sarcastic features for experimentation. We also train two machine learning models with the same data to compare the performance of the lexicon-based model and machine learning-based model. The results show that the lexicon-based model correctly identified 48.5% sarcastic and 23.5% nonsarcastic tweets with the recall of 69.6% and 87.9% precision. The recall rate of Naïve Bayes and SVM-based machine learning models was 20.1% and 24.4%, respectively, with an overall accuracy of 65.2% and 60.1%, respectively.
Comparing Indonesian and English Culture in EFL Context

This research aims at describing the implementation of comparing Indonesian and English culture in EFL context and describing the students’ perception on it. This is a descriptive qualitative. This research is applied to non-English department in English lesson. The data used in this research is the questionnaire results. To collect the data, the writer gives questionnaire to the students via Google Form. The writer uses 4 topic of cultural material such as comparing fairy tale (Bawang Merah Bawang Putih VS Cinderella), tradition (The Night of One Suro VS Halloween), Indonesian VS English proverbs, and Food around the world (Acar/Pickle). The students feel interest and enjoy with all materials. Even after learning those materials, they are proud of Indonesian culture.
Managing the motivation provision of universities in the context of the educational process digitalisation

The paper proposes a method for increasing the motivation of university teachers to work with digital technologies on the example of teaching foreign languages. The fear of complete displacement of the teacher by digital technologies leads to many ethical and psychological problems, resulting in a reluctance to use training programmes in the educational process. To increase the motivation of teachers to work in the digital environment, it is proposed to involve them in the use of digital educational technologies to adapt the work plans of disciplines to the preparation level of students entering universities. The specialists team composition required for the implementation of this method in practice has been formed. Additional factors for increasing the teachers motivation to work in a digital environment when working in a team have been shown. The proposed approach can be used in the implementation in digital educational systems of educational institutions.
This Better Be Interesting: A Speaker’s Decision to Speak Cues Listeners to Expect Informative Content

Abstract In anticipating upcoming content, comprehenders are known to rely on real-world knowledge. This knowledge can be deployed directly in favor of upcoming content about typical situations (implying a transparent mapping between the world and what speakers say about the world). Such knowledge can also be used to estimate the likelihood of speech, whereby atypical situations are the ones newsworthy enough to merit reporting (i.e., a nontransparent mapping in which improbable situations yield likely utterances). We report four forced-choice studies (three preregistered) testing this distinction between situation knowledge and speech production likelihood. Comprehenders are shown to anticipate situation-atypical meanings more when guessing content (a) that a speaker announces (rather than thinks), (b) that is said out of the blue (rather than produced when prompted), and (c) that is addressed to a large audience (rather than a single listener). The findings contrast with prior work that emphasizes a comprehension bias in favor of typicality, and they highlight the need for comprehension models that incorporate expectations for informativity (as one of a set of inferred speaker goals) alongside expectations for content plausibility.
Tangential documentary review that allowed the reorganization of health services in the covid-19 pandemic

Objective: Analyze the documents in the form of ordinances, decrees and federal laws that allowed the reorganization of health services in the pandemic. Methodology: Theoretical, descriptive study of critical document analysis of electronic material made available on the websites of the Ministry of Health. Results: COVID-19 demanded a high demand from the Unified Health System in Brazil, at different levels of care and complexity, totally outside the usual standards, with emphasis on the need for highly complex hospital resources to treat about 5% of patients of severe cases and readjustment of health services. Final Considerations: The material constructed resulted from a consolidation of the actions developed by all the municipalities that make up the aforementioned Health Care Network, according to each local reality, to face the pandemic.