Reviewerly/exHarmony huggingface dataset:
LEASH: Enhancing Micro-architectural Attack Detection with a Reactive Process Scheduler

Micro-architectural attacks use information leaked through shared resources to break hardware-enforced isolation. These attacks have been used to steal private information ranging from cryptographic keys to privileged Operating System (OS) data in devices ranging from mobile phones to cloud servers. Most existing software countermeasures either have unacceptable overheads or considerable false positives. Further, they are designed for specific attacks and cannot readily adapt to new variants. In this paper, we propose a framework called LEASH, which works from the OS scheduler to stymie micro-architectural attacks with minimal overheads, negligible impact of false positives, and is capable of handling a wide range of attacks. LEASH works by starving maliciously behaving threads at runtime, providing insufficient time and resources to carry out an attack. The CPU allocation for a falsely flagged thread found to be benign is boosted to minimize overheads. To demonstrate the framework, we modify Linux's Completely Fair Scheduler with LEASH and evaluate it with seven micro-architectural attacks ranging from Meltdown and Rowhammer to a TLB covert channel. The runtime overheads are evaluated with a range of real-world applications and found to be less than 1% on average.
Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions

Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and can be overly focused and narrowed to certain online groups. This work proposes a complete solution to accelerate the qualitative analysis of problematic online speech, focusing on opinions emerging from online communities by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we use keywords to collect a large dataset from three online social media platforms (Facebook, Twitter, and Youtube). Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler -- which balances exploration and exploitation of unlabeled data -- the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team, and, finally, the retraining of the automatic opinion classifiers. We present both qualitative and quantitative results. First, we show that our human-in-the-loop method successfully augments the initial qualitatively labeled and narrowly focused dataset and constructs a more encompassing dataset. Next, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Finally, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some pathways through which extreme opinions creep into the mainstream online discourse.
Optimized Design of Decoder 2 to 4, 3 to 8 and n to 2n using Reversible Gates

The design of low consumption CMOS circuits, nanotechnologies and quantum computing has becomed more attached to the reversible logic.A set of gates have been recently exploited in reversible computer science for the design of certain circuits.Among them, we find the decoders.In this paper we have exploited a recent study making the design of the decoder 2 to 4, 3 to 8, and n to 2 n , our work aims to enhance the previous designs , by replacing some reversible gates by others while maintaining their functionality and improving their performance criteria namely the number of gates (CG), number of garbage outputs (NGO), number of constant inputs(NCI), Quantum cost (QC) and hardware complexity (HC), compared to our study of the base and other recent studies from which we have obtained remarkable results.
MODEL PROBLEM BASED LEARNING TERHADAP KETERAMPILAN PEMROSESAN HUJAN PADA ANAK TUNANETRA

Abstract: Difficulties in understanding scientific concepts and processes result in the limited ability of blind children in the creation of concrete principles, totality, and independent activities in learning. The purpose of this study was to examine the effect of the problem based learning model on rain processing skills in blind children. The study design in this study used a pre-experimental study of one-group pretest-posttes because the study was conducted in one group without comparison. Based on the results of the pre-test it was found that the average scientific literacy ability of students was 40.27, then given treatment as many as 6 meetings, and in the post-test results obtained an average of 73.5 rain processing skills in blind children. It can be concluded that the use of problem based learning models in learning natural sciences has a positive influence on rain processing skills in blind children. Keywords: Problem Based Learning Model, Skills Rain Processing
Robust Attentive Deep Neural Network for Exposing GAN-generated Faces

GAN-based techniques that generate and synthesize realistic faces have caused severe social concerns and security problems. Existing methods for detecting GAN-generated faces can perform well on limited public datasets. However, images from existing public datasets do not represent real-world scenarios well enough in terms of view variations and data distributions (where real faces largely outnumber synthetic faces). The state-of-the-art methods do not generalize well in real-world problems and lack the interpretability of detection results. Performance of existing GAN-face detection models degrades significantly when facing imbalanced data distributions. To address these shortcomings, we propose a robust, attentive, end-to-end network that can spot GAN-generated faces by analyzing their eye inconsistencies. Specifically, our model learns to identify inconsistent eye components by localizing and comparing the iris artifacts between the two eyes automatically. Our deep network addresses the imbalance learning issues by considering the AUC loss and the traditional cross-entropy loss jointly. Comprehensive evaluations of the FFHQ dataset in terms of both balanced and imbalanced scenarios demonstrate the superiority of the proposed method.
AusAEM 02 WA/NT, 2019-20 Airborne Electromagnetic Survey: TEMPEST(R) airborne electromagnetic data and conductivity estimates

Survey commissioned by Geoscience Australia as part of the Exploring For The Future (EFTF) program.
Geological Survey of Queensland: Strategic Resources Exploration Program concludes, New Economy Minerals Initiative and geophysical programmes commence, and MT results released

In August the GSQ marked the conclusion of the 4-year (2017 - 21) Strategic Resources Exploration Program (SREP) with a workshop in Mount Isa to present all the final reports and datasets completed...
What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016--2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions.
Optimizing Latency for Online Video Captioning Using Audio-Visual Transformers

Video captioning is an essential technology to understand scenes and describe events in natural language. To apply it to real-time monitoring, a system needs not only to describe events accurately but also to produce the captions as soon as possible. Low-latency captioning is needed to realize such functionality, but this research area for online video captioning has not been pursued yet. This paper proposes a novel approach to optimize each caption's output timing based on a trade-off between latency and caption quality. An audio-visual Trans-former is trained to generate ground-truth captions using only a small portion of all video frames, and to mimic outputs of a pre-trained Transformer to which all the frames are given. A CNN-based timing detector is also trained to detect a proper output timing, where the captions generated by the two Trans-formers become sufficiently close to each other. With the jointly trained Transformer and timing detector, a caption can be generated in the early stages of an event-triggered video clip, as soon as an event happens or when it can be forecasted. Experiments with the ActivityNet Captions dataset show that our approach achieves 94% of the caption quality of the upper bound given by the pre-trained Transformer using the entire video clips, using only 28% of frames from the beginning.
Development of environmental competence of future teachers

The structure and content of the model of the process of formation and development of environmental competence of future teachers will be considered. As a result of the research, the following main directions of the formation and development of environmental competence have been identified: retraining and professional development, methodological and information support, creation and support of a digital-developmental environment. The principles and organizational and pedagogical conditions of the formation of environmental competence have been determined.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their everyday practice. Such tools typically process these large images by slicing them into tiles that can then be encoded and utilized for different clinical models. In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field. In particular, we developed a progressively grown generative model with the objective of visual field expansion. Thus trained, our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints, all in a self-supervised way. To evaluate the performance of our model, we conducted classification experiments on CAMELYON17 and CRC benchmark datasets, comparing favorably to other self-supervised and pre-trained strategies that are commonly used in digital pathology. Our code is available at this https URL.
The Influence of Parent’s Education Background on Learning Achievement of Class IV Students Elementary School 1 Mengkowo Academic Year 2019/2020

This research is motivated by the differences in grades of grade IV grade students at Mengkowo. The purpose of this study was to study the educational background of the fourth grade students' learning achievement at Mengkowo Elementary School. This research is a quantitative descriptive research. With conclusions obtained through data analysis and chi-square test. The research sample was all grade IV students at Mengkowo Elementary School which were obtained by 31 students, using saturated sampling techniques. Data collection techniques through questionnaires and documentation. Questionnaire to collect all data on parents' educational level, while collection to collect all student achievement data. The results of the study were among the background studies of parents with the fourth grade students' learning achievements in the Mengkowo State Elementary School. The higher the level of parental education, the higher the learning achievement (in this case the students of Elementary School Mengkowo)
Von Neumann entropy in a dispersive cavity

We study the von Neumann entropy of the partial trace of a system of two two-level atoms (qubits) in a dispersive cavity where the atoms are interacting collectively with a single mode electromagnetic field in the cavity. We make a contrast of this entanglement entropy with the spin squeezing property of the system. We find a close relationship between this von Neumann entropy and the spin squeezing of the system. We observe that this entanglement entropy attains its maximum value when the spin squeezing in the system goes to its maximum and is zero when the spin squeezing in the system vanishes.
Steganography and Steganalysis Using Machine Learning

In the present time, data security is of utmost important, when secret information is communicated over the Internet. Steganography and steganalysis strategies can take care of the arising concerns such as copyright issue, possession, and discovery of malevolent information. Steganography works to conceal mystery information without twisting, whereas steganalysis is to recognize the presence of shrouded information. In this section, steganography utilizing AI and Deep Learning will be explained. Also, steganography and steganalysis strategies will be talked about along with Deep Learning and AI systems to show that these two structures can be utilized to improve the nature of stego pictures and to recognize the mystery information stowing away in picture utilizing steganography calculations.
Expression Tuna – World of Expressive music

Music forms an integral part of human beings and acts as a channel to help an individual to express his/her emotions. It also connects the listener with stimulus related to other emotions. The mood or emotion of the individual is communicated partly through the expressions on the face. Demand therefore exists for a system to assess the emotion or mood of a person and play the music to elicit the emotion. A technical approach includes the system recognizing the captured face expressions in a numeric form and identifying the individual’s emotion determined by the centroid of these features. A musical piece or song is played according to this emotion from the play-list.
STUDENT LEARNING ACHIEVEMENTS REVIEWED FROM PARENTAL ATTENTION, INDEPENDENCE, EMOTIONAL INTELLIGENCE, AND MOTIVATION (Study at SMP Batik Surakarta)

This study aims to determine the simultaneous and partial influence of the variables of parents attention, independence, emotional intelligence and motivation students' learning achievements at SMP Batik Surakarta. This research was conducted at SMP Batik Surakarta with the object of analysis of grade IX students. This research method uses a quantitative descriptive research design with a sample of 80 students. Sampling techniques using purposive sampling techniques. Data collection using questionnaires. Data analysis techniques using statistical analysis are multiple linear regression tests, F tests, t-tests, and determination coefficients. The results showed that parental attention, learning independence, emotional intelligence and motivation influenced the learning achievements of grade IX students of SMP Batik Surakarta by 61.9%. In comparison, 38.1% was influenced by other variables not studied, such as learning interests, environment, peers or teacher competencies and others.
태국 유치원의 유·초 연계교육 현황: 피분밤펜 유치원을 중심으로

This study aims to analyze the curriculum of Piboonbumpen Demonstration Kindergarten Burapha University in Thailand in terms of continuity, sequence, and integration by using Ralph Tyler's curriculum organizational principles. Second, it examines the perceptions of kindergarten teachers about connection between kindergarten and elementary school education in Piboonbumpen kindergarten. Third, it examines the status of early childhood education activities in Piboonbumpen kindergarten.The results of analyzing the curriculum using Tyler's curriculum organizational principles are as follows. First, the curriculum achieved continuity by repeating the same educational content every grade. Second, Piboonbumpen Kindergarten introduces STEM Education to provide education, which is an activity that deepens and expands educational content. The curriculum showed a sequence by not only learning one topic once, but by deepening and expanding on the same topic as the grade went up. Third, by providing integrated education for the development of muscles, emotions, spirit, morality, sociality, thinking ability, vocabulary, imagination, and creativity, physical, emotional, social, and intellectual development can be achieved at the same time. Such a curriculum shows the integration that connects the contents of the curriculum horizontally and broadly between different areas and activities.Piboonbumpen kindergarten teachers said that the curriculum in Thailand is well-connected based on the law, and that they also try to meet them. They said that in order to establish a connection in the actual educational field, they made a book of each child’s personality and health and opened it to the elementary school and their parents. They also said that their efforts were supported, and the connection between kindergarten and elementary school was well established.
Школьный медиацентр как площадка для формирования и развития метапредметных компетенций учащихся

The theses include the justification and goal-setting of the innovative project Implementation of the model of formation of metasubject competences of students in the process of organization and functioning of the media center of the educational institution for the period of implementation from 2019 to 2022.
A Quantitative Quality Assessment Framework for Continuous Raw EEG Data

This paper describes the process of capturing an expert’s knowledge in the form of human understandable rules and then inserting them into a self-organizing map neural network. The Dynamic Cell Structure (DCS) is a form of self-organizing map used for many purposes, including classification and prediction. The DCS is a topology-preserving network that starts with no pre-structure, but assumes a structure once trained. This paper explores applying the DCS to analyse a social science data set that contains some of the many factors that may influence the mortality rate of a region.
Local Augmentation for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. The key idea for GNNs is to obtain informative representation through aggregating information from local neighborhoods. However, it remains an open question whether the neighborhood information is adequately aggregated for learning representations of nodes with few neighbors. To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features. Local augmentation is a general framework that can be applied to any GNN model in a plug-and-play manner. It samples feature vectors associated with each node from the learned conditional distribution as additional input for the backbone model at each training iteration. Extensive experiments and analyses show that local augmentation consistently yields performance improvement when applied to various GNN architectures across a diverse set of benchmarks. For example, experiments show that plugging in local augmentation to GCN and GAT improves by an average of 3.4\% and 1.6\% in terms of test accuracy on Cora, Citeseer, and Pubmed. Besides, our experimental results on large graphs (OGB) show that our model consistently improves performance over backbones. Code is available at https://github.com/SongtaoLiu0823/LAGNN.
Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair Semantics

In this paper, we address the problem of handling inconsistent data in Temporal Description Logic (TDL) knowledge bases. Considering the data part of the Knowledge Base as the source of inconsistency over time, we propose an ABox repair approach. This is the first work handling the repair in TDL Knowledge bases. To do so, our goal is twofold: 1) detect temporal inconsistencies and 2) propose a data temporal reparation. For the inconsistency detection, we propose a reduction approach from TDL to DL which allows to provide a tight NP-complete upper bound for TDL concept satisfiability and to use highly optimised DL reasoners that can bring precise explanation (the set of inconsistent data assertions). Thereafter, from the obtained explanation, we propose a method for automatically computing the best repair in the temporal setting based on the allowed rigid predicates and the time order of assertions.
LA ENTONACIÓN DE LAS ORACIONES INTERROGATIVAS EN HABLANTES NORMOTÍPICOS Y PACIENTES AFÁSICOS NO FLUENTES: PRIMER ACERCAMIENTO

The purpose of this paper is to analyze and describe intonational patterns in interrogatives in the Spanish spoken in the urban areas of the Biobío Region of Chile.Spoken samples of normal speakers and individuals diagnosed with 2 variants of non-fluent aphasia were investigated for the current description.The spoken samples were taken from the Spanish Questionnaire (Central Peninsular Spanish Version), adapted for Spanish by Estebas and Prieto (2008) and some unplanned interviews.The sample includes 342 interrogative statements produced by 20 native speakers:10 of them diagnosed with 2 different variants of non-fluent aphasia and 10 individuals with no neurological damage.All of them signed the informed consent.The basis of analysis is the Autosegmental Metrical Theory, known as AM and the intonational transcription system ToBI (Pierrehumbert, 1980;Estebas y Prieto, 2008;Prieto y Roseano, 2010;Hualde y Prieto, 2015).We found that though many intonational patterns are similar to those observed in Santiago de Chile and elsewhere in the Spanish-speaking world, there are some patterns which differ between them.Such differences are considered to be dialectal in nature, as they do not change the meaning of the statement.In the case of non-fluent aphasia patients, large differences were noted concerning the interrogative sentences produced by normal speakers, those that coincide with those already on file from previous studies.
MT4ImgRec:A Metamorphic Testing Tool for Image Recognition Software

Although data-driven image recognition software has widely emerged in various fields, they suffer from quality issues.Metamorphic testing has been successfully applied to AI software for alleviating test oracle problems.Nevertheless, metamorphic testing still relies on manual methods in most cases, which is timeconsuming.To improve test efficiency, a testing tool called MT4ImgRec is designed to automatically perform metamorphic testing for image recognition software.