Reviewerly/exHarmony huggingface dataset:
Isolation Without Taxation: Near Zero Cost Transitions for SFI

Almost all SFI systems use heavyweight transitions that incur significant performance overhead from saving and restoring registers when context switching between application and sandbox code. We identify a set of zero-cost conditions that characterize when sandboxed code is well-structured enough so that security can be guaranteed via lightweight zero-cost transitions. We show that using WebAssembly (Wasm) as an intermediate representation for low-level code naturally results in a SFI transition system with zero-cost transitions, and modify the Lucet Wasm compiler and its runtime to use zero-cost transitions. Our modifications speed up font and image rendering in Firefox by up to 29.7% and 10% respectively. We also describe a new purpose-built fast SFI system, SegmentZero32, that uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our zero-cost conditions. While this enforcement incurs some runtime cost within the sandboxed code, we find that, on Firefox image and font rendering benchmarks, the time saved per transition allows SegmentZero32 to outperform even an idealized hardware isolation system where memory isolation incurs zero performance overhead but the use of heavyweight transitions is required.
Modulation Pattern Detection Using Complex Convolutions in Deep Learning

Transceivers used for telecommunications transmit and receive specific modulation patterns that are represented as sequences of complex numbers. Classifying modulation patterns is challenging because noise and channel impairments affect the signals in complicated ways such that the received signal bears little resemblance to the transmitted signal. Although deep learning approaches have shown great promise over statistical methods in this problem space, deep learning frameworks continue to lag in support for complex-valued data. To address this gap, we study the implementation and use of complex convolutions in a series of convolutional neural network architectures. Replacement of data structure and convolution operations by their complex generalization in an architecture improves performance, with statistical significance, at recognizing modulation patterns in complex-valued signals with high SNR after being trained on low SNR signals. This suggests complex-valued convolutions enables networks to learn more meaningful representations. We investigate this hypothesis by comparing the features learned in each experiment by visualizing the inputs that results in one-hot modulation pattern classification for each network.
A Large-Scale Interview Study on Information Security in and Attacks against Small and Medium-sized Enterprises

Cybercrime is on the rise. Attacks by hackers, organized crime and nation-state adversaries are an economic threat for companies world-wide. Small and medium-sized enterprises (SMEs) have increasingly become victims of cyberattacks in recent years. SMEs often lack the awareness and resources to deploy extensive information security measures.However, the health of SMEs is critical for society: For example, in Germany, 38.8% of all employees work in SMEs, which contributed 31.9% of the German annual gross domestic product in 2018. Many guidelines and recommendations encourage companies to invest more into their information security measures. However, there is a lack of understanding of the adoption of security measures in SMEs, their risk perception with regards to cybercrime and their experiences with cyberattacks.To address this gap in research, we performed 5,000 computer-assisted telephone-interviews (CATIs) with representatives of SMEs in Germany. We report on their experiences with cybercrime, management of information security and risk perception. We present and discuss empirical results of the adoption of both technical and organizational security measures and risk awareness in SMEs. We find that many technical security measures and basic awareness have been deployed in the majority of companies. We uncover differences in reporting cybercrime incidences for SMEs based on their industry sector, company size and security awareness. We conclude our work with a discussion of recommendations for future research, industry and policy makers.
Design and development of a question generator for learners of English

In this paper, we describe the design and development of the first release of an online question generator. This pedagogic tool enables learners of English to generate open-ended, closed-ended and tag questions for a target sentence. Learners input a sentence (i.e. declarative statement) and select the type or types of questions to generate. Question generation is a non-trivial task involving numerous processes including syntactic transformation and pronoun selection. Syntactic transformation was achieved through the use of rules based on parse trees while the selection of interrogative pronouns was achieved using matching potential question foci with a linguistic knowledge encoded condition set. Lessons learned are detailed to help other researchers avoid or attempt to ameliorate the pitfalls and problems encountered in this study.
Physically Inspired Dense Fusion Networks for Relighting

Image relighting has emerged as a problem of signif?icant research interest inspired by augmented reality ap?plications. Physics-based traditional methods, as well asblack box deep learning models, have been developed. The existing deep networks have exploited training to achieve a new state of the art; however, they may perform poorly when training is limited or does not represent problem phe?nomenology, such as the addition or removal of dense shad?ows. We propose a model which enriches neural networks with physical insight. More precisely, our method gener?ates the relighted image with new illumination settings via two different strategies and subsequently fuses them using a weight map (w). In the first strategy, our model predicts the material reflectance parameters (albedo) and illumina?tion/geometry parameters of the scene (shading) for the re?lit image (we refer to this strategy as intrinsic image de?composition (IID)). The second strategy is solely based on the black box approach, where the model optimizes its weights based on the ground-truth images and the loss terms in the training stage and generates the relit output directly (we refer to this strategy as direct). While our pro?posed method applies to both one-to-one and any-to-any relighting problems, for each case we introduce problem?specific components that enrich the model performance: 1) For one-to-one relighting we incorporate normal vectors of the surfaces in the scene to adjust gloss and shadows ac?cordingly in the image. 2) For any-to-any relighting, we propose an additional multiscale block to the architecture to enhance feature extraction. Experimental results on the VIDIT 2020 and the VIDIT 2021 dataset (used in the NTIRE 2021 relighting challenge) reveals that our proposal can outperform many state-of-the-art methods in terms of well?known fidelity metrics and perceptual loss
Effects of Macroprudential Policy: Evidence from Over 6,000 Estimates

This paper builds a novel database on the effects of macro-prudential policy drawing from 58 empirical studies, comprising over 6,000 results on a wide range of instruments and outcome variables. It encompasses information on statistical significance, standardized magnitudes, and other characteristics of the estimates. Using meta-analysis techniques, the paper estimates average effects to find:i) statistically significant effects on credit, but with considerable heterogeneity across instruments; ii) weaker and more imprecise effects on house prices; iii) quantitatively stronger effects in emerging markets and among studies using micro-level data; and iv) statistically significant evidence of leakages and spillovers. Other findings include relatively stronger impacts for tightening than loosening actions and negative effects on economic activity in the near term.
Evaluation of challenges faced by the teachers and students of two further education insitutions when using the iPad in the teaching and learning of English as a foreign language Fujairah, United Arab Emirates

The aim of the study was to examine the kind of challenges the teachers and students in the Foundations Program of the Fujairah Men’s College and Fujairah Women’s College faced in using the iPad to create tasks and activities designed to construct, support and promote the teaching and learning of English as a foreign language. The study employed a case study design. The research participants were drawn from the teachers and students of the Foundations Program at FWC & FMC. The data for this research were collected by means of two online surveys involving the Foundations Program’s teachers and students, six class observations and five interviews of the teachers who conducted the classes. The study finds that the Foundations Program’s teachers and students faced challenges in using the iPad to create tasks and activities designed to construct, support and promote the teaching and learning of English as a foreign language. The findings of the study will contribute to the area of TESOL and mobile learning by its focus on the rich experience of two higher education colleges using the iPad as a teaching and learning technology in classes of Arab learners and by highlighting the challenges the teachers and students at these two colleges faced in their attempt to integrate the new technology in their context.
Use of Kahoot media and variations of questions on learning achievement with motivation as moderation

This study aims to determine whether there is an influence of the use of Kahoot and the variation of questions on learning achievement with motivation as a moderator. The populations were the tenth-grade students of social sciences at SMA Negeri 1 Sumberejo in the 2019/2020 academic year, with a sample of 81 students determined using a Cochran formula. The study adopted a descriptive verification method with ex-post facto and survey approaches. The collected data were analysed using a linear regression test with path analysis. The results indicate that there is an influence of the use of Kahoot media, the variation of questions, and learning motivation on the economic learning achievement among the tenth-grade students of social sciences at SMA Negeri 1 Sumberejo in the academic year of 2019/2020.
Ruins of Excess: Computer Game Images and the Rendering of Technological Obsolescence

In this article, we describe three layers of ruins related to computer game technology: in a surface layer, we examine the imagery of ruins in digital games, highlighting game design tools for developing in-game ruination. Secondly, we approach the industrial design model of technological obsolescence as an infrastructural layer that intrinsically demands the production of new provisional spaces for material decay. Lastly, through a waste layer, we unfold the geopolitical dimension of technological obsolescence, calling attention to the transcontinental flows of electronic waste, which also underscores a geological stage of ruination. While exploring these different layers of ruins, we wish to perceive how game design models might relate to different forms of contemporary ruination, inquiring what such material traces have to say as strata of the complex deterioration processes of present-day media.
Hyperparameter Transfer Learning with Adaptive Complexity

Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models. In practice, one frequently has to solve similar hyperparameter tuning problems sequentially. For example, one might have to tune a type of neural network learned across a series of different classification problems. Recent work on multi-task BO exploits knowledge gained from previous tuning tasks to speed up a new tuning task. However, previous approaches do not account for the fact that BO is a sequential decision making procedure. Hence, there is in general a mismatch between the number of evaluations collected in the current tuning task compared to the number of evaluations accumulated in all previously completed tasks. In this work, we enable multi-task BO to compensate for this mismatch, such that the transfer learning procedure is able to handle different data regimes in a principled way. We propose a new multi-task BO method that learns a set of ordered, non-linear basis functions of increasing complexity via nested drop-out and automatic relevance determination. Experiments on a variety of hyperparameter tuning problems show that our method improves the sample ef
PCA Embedded Random Forest

This study proposes a new architecture for the random forest algorithm. This architecture attempts to improve Random Forest's ability to recognize feature interdependencies. A performance improvement is achieved by creating a PCA model within each tree. This PCA model creates new additional features containing information from multiple input features. These new features are not used for feature reduction but instead appended to the existing feature vectors. With this approach, the tree can make separations based on information from more than one of its input features. We will outline an approach using PCA feature construction to improve the Random Forest algorithm's performance. The improvement is not universal but instead useful when false positives are more detrimental than false negatives. RF-PCA produces models which favor a high precision with nearly equivalent F1-scores to a traditional random forest algorithm.
Implementation and evaluation of an echo state network for a quality inspection system for laser welding

Laser welding is a widespread process in series production due to its high welding accuracy and speed. However, performing quality controls manually slows down the manufacturing process and causes high personnel costs combined with potential unreliability. In this paper, various Echo State Network (ESN) architectures for seam classification serving the facilitation and acceleration of the manual postproduction quality inspection are presented. In order to improve the performance of the ESN, a data standardization method is proposed, which applies accumulated information from all seams of the training set to a single seam in the test set. Considering that the automotive industry requires a high level of classification reliability, classification probability histograms are introduced. These enable comparing the reliability of models and clearly outperform typical confusion matrix metrics. Based on the evaluation of different tests performed with these histograms, a combined ESN architecture is suggested. This architecture uses two types of readouts, i.e., Multilayer Perceptron and Support Vector Machine, and utilizes the strong sides of both. In this work, a real laser welding dataset from a series production is used to evaluate all proposed methods and architectures. The data represent the records of light emission values during the welding process.
The Effect of Corporate Social Responsibility Activities on Organizational Trust and Job performance

We study confirmed the effect of corporate social responsibility activities on organizational trust and job performance of organizational members and mediating effects of organizational trust among 351 members of the organization in the metropolitan area and Chungcheong area. For this, the SPSS 24.0 and AMOS 24.0 statistical packages were used to produce the following results. First, as a result of analyzing the impact of CSR activities on organizational trust of organizational members, factors of economic responsibility, legal responsibility, ethical responsibility, and charitable responsibility showed significant effects on organizational trust. Second, as a result of analyzing the relationship between the effects of CSR activities on the job performance of members of the organization, it showed a direct effect on job performance, which is a factor of economic responsibility, legal responsibility, ethical responsibility, and charitable responsibility. Third, organizational trust of organization members was analyzed as a positive factor in job performance. Fourth, it showed the mediating effect of organizational trust on the effect of corporate social responsibility activities on job performance. As a result of this study, the organizational performance and job performance of organizational members showed a direct effect on CSR activities. Therefore, the CSR activity is important as it is a key factor to advance the organizational trust and job performance, which is the company's sustainable management system.
Evaluation of Various Workloads in Filebench Suitable for Phase-change Memory

Phase-Change Memory (PCM) is known as the next generation of memory thanks to its outstanding properties, such as fast speed, non-volatility, scalability, and low power consumption. Based on these characteristics, PCM can be used in larger expanded memory or faster storage compared to HDD or NAND flash memory. Therefore, various companies are trying to deploy PCM-based memory products. However, studies on deciding the target system of PCM are still insufficient, which is an obstacle to the commercialization of PCM. In this paper, a file system benchmark, Filebench, is evaluated with various operating options to find the most appropriate workload for PCM as storage. An experiment was conducted in a virtual system by mounting PCM with a PCM-aware file system. The results demonstrate that the PCM-based system performs up to 500 times better than traditional storage if executing workloads with a significant amount of write operations and synchronization operations. A number of applications were tested on various configurations of systems, and workload characteristics suitable for the PCM-based system are presented.
Cyber Security Threats During Covid-19 Pandemic

In 2020, Covid-19 was declared as a pandemic, affecting almost ever part of societies, because of its impacts economy worldwide faced a big loss which included most of the economic sectors. During that time, technology played a very crucial role worldwide in keeping people connected. Educational institutions adopted online learning, employees started working from home, in addition to that, there has been a high demand for different types of businesses like online healthcare, delivery of food and grocery shopping, etc. Although technology helped in many different ways, we also observed a sudden increase in cyber-attacks. Attackers worldwide took the covid-19 situation as a big opportunity to perform various malicious activities and attacks for financial benefits and to promote their evil demands. This paper identifies major threats in cybersecurity that have taken place worldwide during the situation of covid-19. All these attacks played a big role in bringing harm to different sectors of our society. (C) 2021 INT TRANS J ENG MANAG SCI TECH.
On Data Efficiency of Meta-learning

Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks. Motivated by use-cases in personalized federated learning, we study the often overlooked aspect of the modern meta-learning algorithms -- their data efficiency. To shed more light on which methods are more efficient, we use techniques from algorithmic stability to derive bounds on the transfer risk that have important practical implications, indicating how much supervision is needed and how it must be allocated for each method to attain the desired level of generalization. Further, we introduce a new simple framework for evaluating meta-learning methods under a limit on the available supervision, conduct an empirical study of MAML, Reptile, and Protonets, and demonstrate the differences in the behavior of these methods on few-shot and federated learning benchmarks. Finally, we propose active meta-learning, which incorporates active data selection into learning-to-learn, leading to better performance of all methods in the limited supervision regime.
Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers

Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize information using the self-attention and co-attention mechanisms. These attention modules also play a role in other computer vision tasks including object detection and image segmentation. Unlike Transformers that only use self-attention, Transformers with co-attention require to consider multiple attention maps in parallel in order to highlight the information that is relevant to the prediction in the model's input. In this work, we propose the first method to explain prediction by any Transformer-based architecture, including bi-modal Transformers and Transformers with co-attentions. We provide generic solutions and apply these to the three most commonly used of these architectures: (i) pure self-attention, (ii) self-attention combined with co-attention, and (iii) encoder-decoder attention. We show that our method is superior to all existing methods which are adapted from single modality explainability.
Enseñar y aprender en tiempos de pandemia: presente y horizontes

The following essay presents a critical reflection about the place of technologies in teaching and learning practices in the context of COVID-19. It involves a personal and interpretive approach, not exhaustive, that addresses how institutions, educational practices and teachers have been challenged in this complex and highly uncertain context. It recovers some visions that have been circulating through the networks of critical thinkers of recognized trajectory to help us reflect on this exceptional context. It proposes some horizons of possibility that may offer clues for the critical revision of teaching; of the place of technologies and the construction of knowledge in contemporary digital settings. It raises tensions, questions and some certainties to imagine some didactic transformations for the coming years that imply conditions of inclusion in all expressions (political, cultural, epistemological, cognitive, emotional).
Data migration, RFID integration and implementation of Koha: a case study of NIT Rourkela

The present case study discusses the implementation of Koha library management software at Biju Patnaik Central Library (BPCL) of the National Institute of Technology Rourkela. The paper describes the process of data migration from proprietary integrated library management software (ILMS) Libsys, implementation of Koha ILMS, integration of RFID devices, impact on existing library user services, and housekeeping operations. The study explores the issues with proprietary products such as vendor locking while migrating the data. The paper could be useful to libraries migrating from existing ILMS to open source solution Koha.