Reviewerly/exHarmony huggingface dataset:
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks

Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to using these sensors with current powerful deep neural networks is the lack of large labeled training datasets. This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames. The self-supervised training uses only synchronously-recorded intensity frames and novel sensor data to maximize feature similarity between the pretrained network and the grafted network. We show that the enhanced grafted network reaches competitive average precision (AP50) scores to the pretrained network on an object detection task using thermal and event camera datasets, with no increase in inference costs. Particularly, the grafted network driven by thermal frames showed a relative improvement of 49.11% over the use of intensity frames. The grafted front end has only 5--8% of the total parameters and can be trained in a few hours on a single GPU equivalent to 5% of the time that would be needed to train the entire object detector from labeled data. NGA allows new vision sensors to capitalize on previously pretrained powerful deep models, saving on training cost and widening a range of applications for novel sensors.
ezLDA: Efficient and Scalable LDA on GPUs

LDA is a statistical approach for topic modeling with a wide range of applications. However, there exist very few attempts to accelerate LDA on GPUs which come with exceptional computing and memory throughput capabilities. To this end, we introduce EZLDA which achieves efficient and scalable LDA training on GPUs with the following three contributions: First, EZLDA introduces three-branch sampling method which takes advantage of the convergence heterogeneity of various tokens to reduce the redundant sampling task. Second, to enable sparsity-aware format for both D and W on GPUs with fast sampling and updating, we introduce hybrid format for W along with corresponding token partition to T and inverted index designs. Third, we design a hierarchical workload balancing solution to address the extremely skewed workload imbalance problem on GPU and scaleEZLDA across multiple GPUs. Taken together, EZLDA achieves superior performance over the state-of-the-art attempts with lower memory consumption.
Preschool teachers’ attitudes to the use of information and communication technology in preschool institutions

The relevance of this paper refers to the importance of developing future teachers’ professional competences and ability to integrate the information and communication technology in their own work. The aim of this paper is to examine the preschool teachers’ attitudes of the use of information and communication technology (ICT) in preschool institutions. The research was based on quantitative (descriptive analysis and factor analysis) and qualitative research methodology which included the analysis of relevant literature and a survey conducted among the preschool teacher at the preschool institution in Pula (Croatia). The results have shown that there is no statistically significant difference in the positive attitude to the use of ICT in preschool institutions between preschool teachers younger and older than 35, i.e. both show a positive attitude to the application of ICT in preschool institutions. The paper materials may be useful in the preschool institution for integrating digital knowledge into teacher’s work to contribute to the quality of preschool children’s education.
Context-Aware RCNN: A Baseline for Action Detection in Videos

Video action detection approaches usually conduct actor-centric action recognition over RoI-pooled features following the standard pipeline of Faster-RCNN. In this work, we first empirically find the recognition accuracy is highly correlated with the bounding box size of an actor, and thus higher resolution of actors contributes to better performance. However, video models require dense sampling in time to achieve accurate recognition. To fit in GPU memory, the frames to backbone network must be kept low-resolution, resulting in a coarse feature map in RoI-Pooling layer. Thus, we revisit RCNN for actor-centric action recognition via cropping and resizing image patches around actors before feature extraction with I3D deep network. Moreover, we found that expanding actor bounding boxes slightly and fusing the context features can further boost the performance. Consequently, we develop a surpringly effective baseline (Context-Aware RCNN) and it achieves new state-of-the-art results on two challenging action detection benchmarks of AVA and JHMDB. Our observations challenge the conventional wisdom of RoI-Pooling based pipeline and encourage researchers rethink the importance of resolution in actor-centric action recognition. Our approach can serve as a strong baseline for video action detection and is expected to inspire new ideas for this filed. The code is available at \url{https://github.com/MCG-NJU/CRCNN-Action}.
FAME: Fibre optic cables: an Alternative tool for Monitoring volcanic Events

&amp;lt;p&amp;gt;In the framework of EUROVOLCs Trans-national grants, we propose the FAME project aiming at validating Distributed Acoustic Sensing (DAS) technology as a complementary and alternative tool for monitoring volcanic and seismic activity at Etna volcano. DAS technology provides records of strain signals with unprecedented spatial and temporal resolution.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We deployed a fibre optic cable connected to an iDAS (Silixa) interrogator set-up at the Observatory Pizzi Deneri in the summit area. To allow for a continuously recording of the iDAS, a solar panel power system was designed using battery back-up and inverter to supply 200 W at 220 V/AC. An internet connection was set up for a full remote control capability. The iDAS interrogated a 1.5 km long fibre cable, buried at a depth of about 30 cm by digging a trench in Piano delle Concazze area. The DAS measurements were validated with conventional measurements from 26 broadband seismometers and 3 arrays of 3 infrasound sensors from the Geophysical Instrument Pool Potsdam (GIPP). We deployed instruments along the fibre optic cable, covering an area of about 0.1 km2. The DAS and conventional sensors acquired data from 4 July to 23 September 2019 without major interruptions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we show key features of this the extraordinary multidisciplinary dataset. Thanks to the high spatial resolution (2 m), we could find locations of hypothesized faults in Piano delle Concazze area. Thanks to the long acquisition period, we continuously tracked Etna activity, marked by several eruptive episodes, including ash emissions, strombolian and effusive activities from the summit craters. The most intense and sustained eruptive events occurred in 18-20 July, 27-28 July and 9-13 September. We investigate the application of well-established analysis techniques in volcano-seismology to DAS dataset in order to assess the performance of the system in detecting and characterizing volcanic events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our findings demonstrate that DAS technology can record on a long term basis volcanic activity, which suggests DAS technology can be integrated to volcanic monitoring systems.&amp;lt;/p&amp;gt;
Odyssey: Creation, Analysis and Detection of Trojan Models

Along with the success of deep neural network (DNN) models in solving variousreal world problems, rise the threats to these models that aim to degrade theirintegrity. Trojan attack is one of the recent variant of data poisoning attacks thatinvolves manipulation or modification of the model to act balefully. This can occurwhen an attacker interferes with the training pipeline by inserting triggers into someof the training samples and trains the model to act maliciously only for samplesthat are stamped with trigger. Since the knowledge of such triggers is only privy to the attacker, detection of Trojan behaviour is a challenge task. Unlike any of the existing Trojan detectors, a robust detector should not rely on any assumption about Trojan attack. In this paper, we develop a detector based upon the analysis of intrinsic properties of DNN that could get affected by a Trojan attack. To have a comprehensive study, we propose, Odysseus, the largest Trojan dataset with over 3,000 trained DNN models, both clean and Trojan. It covers a large spectrum of attacks; generated by leveraging the versatility in designing a trigger and mapping (source to target class) type. Our findings reveal that Trojan attacks affect the classifier margin and shape of decision boundary around the manifold of the clean data. Combining these two factors leads to an efficient Trojan detector; operates irrespective of any knowledge of the Trojan attack; that sets the first baseline for this task with accuracy above 83%.
SBI -- A toolkit for simulation-based inference

Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging. Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. Importantly, SBI does not seek to recover a single 'best' data-compatible parameter set, but rather to identify all high probability regions of parameter space that explain observed data, and thereby to quantify parameter uncertainty. In Bayesian terminology, SBI aims to retrieve the posterior distribution over the parameters of interest. In contrast to conventional Bayesian inference, SBI is also applicable when one can run model simulations, but no formula or algorithm exists for evaluating the probability of data given parameters, i.e. the likelihood. We present $\texttt{sbi}$, a PyTorch-based package that implements SBI algorithms based on neural networks. $\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
On the Formation of a Common Vision of the Use of Information and Communication Technologies in the Classroom

The formation of a vision common to all participants in the educational process is considered as one of the indicators of a school’s development. The article presents data on how members of the school community form a common vision of the use of information and communication technologies in the educational process. This data was obtained during the piloting of the SELFIE school self-assessment tool. It was found that all groups of participants in the educational process agree that the use of information communication technologies is important and desirable for their school. However, most schools do not apply any focused efforts to develop a common vision that is shared by members of their school.
Remote Access Communications Security: Analysis of User Authentication Roles in Organizations

Remote access is a means of accessing resources outside one's immediate physical location.This has made employee mobility more effective and productive for most organizations.Remote access can be achieved via various channels of remote communication, the most common being Virtual Private Networks (VPNs).The demand for remote access is on the rise, especially during the Covid-19 pandemic, and will continue to increase as most organizations are re-structuring to make telecommuting a permanent part of their mode of operation.Employee mobility, while presenting organizations with some advantages, comes with the associated risk of exposing corporate cyber assets to attackers.The remote user and the remote connectivity technology present some vulnerabilities which can be exploited by any threat agent to violate the confidentiality, integrity and availability (CIA) dimensions of these cyber assets.So, how are users and remote devices authenticated?To what extent is the established connection secured?With employee mobility on the rise, it is necessary to analyze the user authentication role since the mobile employee is not under the monitoring radar of the organization, and the environment from which the mobile employee connects may be vulnerable.In this study, an experiment was setup to ascertain the user authentication roles.The experiment showed the process of 2FA in user authentication and it proved to be an effective means of improving user authentication during remote access.This was depicted via the use of what the user has (mobile phone/soft-token) as a second factor in addition to what the user knows, i.e. password.This authentication method overcomes the security weaknesses inherent in single-factor user authentication via the use of password only.However, the results also showed that though 2FA user authentication ensures security, the remote devices could exhibit further vulnerabilities and pose serious risks to the organization.Thus, a varied implementation was
Attentive Graph Neural Networks for Few-Shot Learning

Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few-shot learning tasks. Despite its powerful capacity to learn and generalize the model from few samples, GNN usually suffers from severe over-fitting and over-smoothing as the model becomes deep, which limit the scalability. In this work, we propose a novel Attentive GNN to tackle these challenges, by incorporating a triple-attention mechanism, i.e. node self-attention, neighborhood attention, and layer memory attention. We explain why the proposed attentive modules can improve GNN for few-shot learning with theoretical analysis and illustrations. Extensive experiments show that the proposed Attentive GNN model achieves the promising results, comparing to the state-of-the-art GNN- and CNN-based methods for few-shot learning tasks, over the mini-ImageNet and tiered-ImageNet benchmarks, under ConvNet-4 and ResNet-based backbone with both inductive and transductive settings. The codes will be made publicly available.
Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances

Determining the borders of the objects in the images and detection of the edges is an important step for image processing applications. In this study, the areas of different objects were calculated using edge detection methods. The edges of real-time images taken from the camera were detected with traditionally used Sobel, Prewitt and Canny filters. In addition, these filters were applied to the images obtained using Basic Global Thresholding (BGT) pre-processing and Mean Thresholding (MT) pre-processing to detect the edges in the image. After eliminating the fake edges in the edge images, the areas of the obtained images were calculated. The methods used in this paper were compared according to the fake edge and the area calculation performances.
Data Sampling on MDS-resistant 10th Generation Intel Core (Ice Lake)

Microarchitectural Data Sampling (MDS) is a set of hardware vulnerabilities in Intel CPUs that allows an attacker to leak bytes of data from memory loads and stores across various security boundaries. On affected CPUs, some of these vulnerabilities were patched via microcode updates. Additionally, Intel announced that the newest microarchitectures, namely Cascade Lake and Ice Lake, were not affected by MDS. While Cascade Lake turned out to be vulnerable to the ZombieLoad v2 MDS attack (also known as TAA), Ice Lake was not affected by this attack. In this technical report, we show a variant of MSBDS (CVE2018-12126), an MDS attack, also known as Fallout, that works on Ice Lake CPUs. This variant was automatically synthesized using Transynther, a tool to find new variants of Meltdown-type attacks. Based on the findings of Transynther, we analyze different microcodes regarding this issue, showing that only microcode versions after January 2020 prevent exploitation of the vulnerability. These results show that Transynther is a valuable tool to find new variants, and also to test for regressions possibly introduced with microcode updates.
On cosets weight distributions of the doubly-extended Reed-Solomon codes of codimension 4

We consider the $[q+1,q-3,5]_q3$ generalized doubly-extended Reed-Solomon code of codimension $4$ as the code associated with the twisted cubic in the projective space $\mathrm{PG}(3,q)$. Basing on the point-plane incidence matrix of $\mathrm{PG}(3,q)$, we obtain the number of weight 3 vectors in all the cosets of the considered code. This allows us to classify the cosets by their weight distributions and to obtain these distributions. The weight of a coset is the smallest Hamming weight of any vector in the coset. For the cosets of equal weight having distinct weight distributions, we prove that the difference between the $w$-th components, $3
Retraction Note: Exploring the quantum speed limit with computer games

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Manajemen Keuangan Pendidikan Anak Usia Dini Perspektif Wealth Management: Studi di TK Ceria Demangan Baru Yogyakarta

Financial management constitutes a crucial element in the performance of the entire programs and activities within an institution. This research was conducted to study financial management practices in Early Childhood Education institution namely TK Ceria Demanagn Baru Yogyakarta through the perspective of wealth management. This research is a descriptive qualitative in which data were collected through interviews and documentations. The results showed that, 1) TK Ceria Demangan Baru managed its financial resources by setting aside students’ contributions for savings funds. This will be used for purposes outside of planned financial arrangement, 2) analysed through the perspective of wealth management, the institution’s financial Management is situated in "Sd" (Student Donation) or equivalent to "E" (employees) within the Cashflow Quadrant. The actual financial management is carried out by first, conducting annual financial planning done at the beginning of each year, implementing the financial planning in all the institution’s programs and activities, recording the implementation, reporting the implementation to the foundation board, and evaluating the implementation. In addition, the implementation of the financial management is carried out by considering the principles of transparency, public accountability, effectiveness and efficiency. Meanwhile, institutional fund obtained merely from students’ contribution reduces the quantity of institutional financial income.
A DLP Module Design Based on Plug-in for MS Word

Inadvertent Data leakage by insiders is considered a serious problem for many organizations. Organizations are increasingly implementing Data Leakage/Loss Prevention solutions also know as (DLP), to protect the confidentiality of their data. Currently, DLP solutions have difficulties to identify confidential data as well as lack the ability to allow users to distinguish confidential from non-confidential data. Moreover, they are limited to work outside organizations. In order to solve this problem, it is important to introduce a DLP-Plugins model where the data owners can identify the privacy of the files during their entire lifecycle (creating, editing, etc.) by classifying them. This model uses security measures such as data encryption and access control to prevent accidental leakage of the classified files by the insiders. The proposed model guarantees that the right user will have access to the correct files according to their security access privilege inside or outside the organization. By always keeping classified files encrypted this will protect them all the time and everywhere. The DLP-Plugins model guarantees the usability for the users, all that will be required is to simply open and close the file as they do normally. As an example of the DLP-Plugins model, we have built a DLP-Plugin for Microsoft Word.
Activities of the Bibliographic Commission of the USSR Academy of Sciences during the Great Patriotic War

On the eve of anniversary of the Victory in the Great Patriotic War, the authors turned to the previously unexplored subject, namely, the work of bibliographers of the USSR Academy of Sciences for the defence of the country. Before, the professional work of bibliographers during the War was not studied in this aspect that determines the relevance and scientific novelty of the topic under consideration. The purpose of the article is to show that during the War bibliography turned from ideological tool of the Party to form a new type of person into the only reliable information tool for solving the most important warfare and national economic problems in the wartime conditions. The article describes the work of the Commission for compilation of bibliography by countries, which was formed under the Section of the special libraries network of the USSR Academy of Sciences in May 1942. Based on the unique archival documents, the authors show for the first time how bibliographers of the libraries of the USSR Academy of Sciences in cooperation with colleagues from the V.I. Lenin State Library of the USSR, the Moscow State University named after M.V. Lomonosov, the Central Polytechnic library and other major libraries of the country managed to provide the military leadership, scientists and specialists of the national economy with reliable timely information on problems that arose in wartime, up to making strategic decisions in the field of planning defence, offensives and radical reorganization of industry and agriculture. The authors conclude that the decent work of bibliographers in the war years, providing information for any level of decision-making, played its role in approaching the Victory. This is the basis for understanding the current role of library specialists, their professionalism and responsibility in the information support for the development of Russia.
Comprehensive Image Captioning via Scene Graph Decomposition

We address the challenging problem of image captioning by revisiting the representation of image scene graph. At the core of our method lies the decomposition of a scene graph into a set of sub-graphs, with each sub-graph capturing a semantic component of the input image. We design a deep model to select important sub-graphs, and to decode each selected sub-graph into a single target sentence. By using sub-graphs, our model is able to attend to different components of the image. Our method thus accounts for accurate, diverse, grounded and controllable captioning at the same time. We present extensive experiments to demonstrate the benefits of our comprehensive captioning model. Our method establishes new state-of-the-art results in caption diversity, grounding, and controllability, and compares favourably to latest methods in caption quality. Our project website can be found at http://pages.cs.wisc.edu/~yiwuzhong/Sub-GC.html.