Reviewerly/exHarmony huggingface dataset:
A Secure and Formally Verified Linux KVM Hypervisor

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.
A Systematic Literature Review on the use of Mobile-assisted Language Learning (MALL) for Enhancing Speaking Skills among ESL and EFL Learners

Mastering all the four language skills has been the topmost priority among both ESL and EFL learners all over the world.Apparently, speaking skills remains to be the most challenging among other skills in which many learners are still struggling to speak proficiently.Past studies suggest that Mobile-assisted language learning (MALL) could be applied as a potential instructional approach in the pursuit of helping learners to gain speaking proficiency.However, there are limited reviews done on current studies which indicate the effectiveness of MALL for speaking particularly in ESL and EFL contexts.Hence, this paper sought to fulfill the gap by presenting a systematic review of relevant published studies done in MALL environments to enhance ESL and EFL learners' speaking skills from the year 2016 to 2020.The findings of the review suggest that the application of MALL propagates the concepts of the constructivism theory, promotes stressfree environment, supports situated learning, and provides ease of use.Further analysis indicate that mobile devices' general built-in features and mobile applications could be used as possible tools in helping learners to enhance their speaking skills in MALL environments.However, most of the reviewed studies' focus on tertiary education has created a literature gap.Thus, there is a need to study the implementation of MALL in both primary and secondary schools so that it could be benefited by ESL and EFL learners of all ages particularly in developing speaking skills.
A Secure Decentralized Spatial Crowdsourcing Scheme for 6G-Enabled Network in Box

Due to the safe portability of the device in sixth generation (6G) enabled network in box (NIB) and the mobility of locations, users equipped with the device can have a better experience in spatial crowdsourcing. The integration of NIB with 6G results in a decentralized spatial crowdsourcing in industrial automation, but threatens the security of tasks and answers and also leads to the leakage of sensing nodes’ locations. To address these problems, in this article, we propose a secure decentralized spatial crowdsourcing scheme for 6G-enabled NIB (DSC-NIB). Using DSC-NIB, the control station and sensing nodes can gather and transmit information on the blockchain using NIB, without depending on the third party. The control station shares encrypted location strategy parameters set to negotiate session keys and a group key with sensing nodes whose locations satisfy the location strategy, while ensuring the privacy of sensing nodes’ locations. For the security of tasks and answers, we leverage the Counter with CBC-MAC authenticated encryption mechanism to provide confidentiality and integrity. Furthermore, we analyze the security of the proposed DSC-NIB. Compared with existing approaches, the performance is evaluated and improved by 30--50%. To further optimize the performance, two optimized schemes without sacrificing security are presented and our results demonstrate that performance is improved greatly.
A place to share teaching resources: Speech and language resource bank

One of the major difficulties of teaching is accessing the vast amounts of material/resources already available to instructors. The Speech and Language Resource Bank (SLRB) is a platform and central repository for sharing, managing, and growing community resources for teaching and research in the speech and language sciences.
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation

Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-curriculum learning for domain adaptation in NMT. During meta-training, the NMT first learns the similar curricula from each domain to avoid falling into a bad local optimum early, and finally learns the curricula of individualities to improve the model robustness for learning domain-specific knowledge. Experimental results on 10 different low-resource domains show that meta-curriculum learning can improve the translation performance of both familiar and unfamiliar domains. All the codes and data are freely available at https://github.com/NLP2CT/Meta-Curriculum.
Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.
Chosen Ciphertext k-Trace Attacks on Masked CCA2 Secure Kyber

Single-trace attacks are a considerable threat to implementations of classic public-key schemes, and their implications on newer lattice-based schemes are still not well understood. Two recent works have presented successful single-trace attacks targeting the Number Theoretic Transform (NTT), which is at the heart of many lattice-based schemes. However, these attacks either require a quite powerful side-channel adversary or are restricted to specific scenarios such as the encryption of ephemeral secrets. It is still an open question if such attacks can be performed by simpler adversaries while targeting more common public-key scenarios. In this paper, we answer this question positively. First, we present a method for crafting ring/module-LWE ciphertexts that result in sparse polynomials at the input of inverse NTT computations, independent of the used private key. We then demonstrate how this sparseness can be incorporated into a side-channel attack, thereby significantly improving noise resistance of the attack compared to previous works. The effectiveness of our attack is shown on the use-case of CCA2 secure Kyber k-module-LWE, where k ∈ {2, 3, 4}. Our k-trace attack on the long-term secret can handle noise up to a σ ≤ 1.2 in the noisy Hamming weight leakage model, also for masked implementations. A 2k-trace variant for Kyber1024 even allows noise σ ≤ 2.2 also in the masked case, with more traces allowing us to recover keys up to σ ≤ 2.7. Single-trace attack variants have a noise tolerance depending on the Kyber parameter set, ranging from σ ≤ 0.5 to σ ≤ 0.7. As a comparison, similar previous attacks in the masked setting were only successful with σ ≤ 0.5.
Transformer Based Language Identification for Malayalam-English Code-Mixed Text

Social media users have the proclivity to write majority of the data for under resourced languages in code-mixed format. Code-mixing is defined as mixing of two or more languages in a single sentence. Research in code-mixed text helps apprehend security threats, prevalent on social media platforms. In such instances, language identification is an imperative task of code-mixed text. The focus of this paper is to carry out a word-level language identification (WLLI) of Malayalam-English code-mixed data, from social media platforms like YouTube. This study was centered around BERT, a transformer model, along with its variants - CamemBERT, DistilBERT - for intuitive perception of the language at the word-level. The propounded approach entails tagging Malayalam-English code-mixed data set with six labels: Malayalam (mal), English (eng), acronyms (acr), universal (univ), mixed (mix) and undefined (undef). Newly developed corpus of Malayalam-English was deployed for appraisal of the effectiveness of state-of-the-art models like BERT. Evaluation of the proffered approach, accomplished with other code-mixed language such as Hindi-English, notched a 9% increase in the F1-score.
Neat, Simple, and Wrong: Debunking Electrostatic Fallacies Regarding Noncovalent Interactions

Multipole moments such as charge, dipole, and quadrupole are often invoked to rationalize intermolecular phenomena, but a low-order multipole expansion is rarely a valid description of electrostatics at the length scales that characterize nonbonded interactions. This is illustrated by examining several common misunderstandings rooted in erroneous electrostatic arguments. First, the notion that steric repulsion originates in Coulomb interactions is easily disproved by dissecting the interaction potential for Ar2. Second, the Hunter-Sanders model of π–π interactions, which is based on quadrupolar electrostatics, is shown to have no basis in accurate calculations. Third, curved "buckybowls" exhibit unusually large dipole moments, but these are ancillary to the forces that control their intermolecular interactions, as illustrated by two examples involving corannulene. Finally, the assumption that interactions between water and small anions are dictated by the dipole moment of H2O is shown to be false in the case of binary halide–water complexes. These examples present a compelling case that electrostatic explanations based on low-order multipole moments are very often counterfactual for nonbonded interactions at close range and should not be taken seriously in the absence of additional justification.
A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.
Hybridized Methods for Quantum Simulation in the Interaction Picture

Conventional methods of quantum simulation involve trade-offs that limit their applicability to specific contexts where their use is optimal. In particular, the interaction picture simulation has been found to provide substantial asymptotic advantages for some Hamiltonians, but incurs prohibitive constant factors and is incompatible with methods like qubitization. We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations over known algorithms. These approaches show asymptotic improvements over the individual methods that comprise them and further make interaction picture simulation methods practical in the near term. Physical applications of these hybridized methods yield a gate complexity scaling as<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>log</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>&amp;#x2061;</mml:mo><mml:mi mathvariant="normal">&amp;#x039B;</mml:mi></mml:math>in the electric cutoff<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">&amp;#x039B;</mml:mi></mml:math>for the Schwinger Model and independent of the electron density for collective neutrino oscillations, outperforming the scaling for all current algorithms with these parameters. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03BB;</mml:mi></mml:math>used to impose an energy cost on time-evolution into an unphysical subspace.
Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations

Abstract The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described. As the novelty of this technique, insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes. As an additional feature set, an automatic identification of groups, clusters, and outliers is provided over time, and based on the visual outcome of the node-link and matrix visualizations, the repertoire of the supported layout and matrix reordering techniques is extended, and more interaction techniques are provided when considering the dynamics of the graph data. Finally, a small user experiment was conducted to investigate the usability of the proposed approach. The usefulness of the proposed tool is illustrated by applying it to a graph dataset, such as e co-authorships, co-citations, and a Comprehensible Perl Archive Network distribution.
Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems

Adversarial examples are designed to fool the speaker recognition (SR) system by adding a carefully crafted human-imperceptible noise to the speech signals. Posing a severe security threat to state-of-the-art SR systems, it becomes vital to deep-dive and study their vulnerabilities. Moreover, it is of greater importance to propose countermeasures that can protect the systems against these attacks. Addressing these concerns, we first investigated how state-of-the-art x-vector based SR systems are affected by white-box adversarial attacks, i.e., when the adversary has full knowledge of the system. x-Vector based SR systems are evaluated against white-box adversarial attacks common in the literature like fast gradient sign method (FGSM), basic iterative method (BIM)–a.k.a. iterative-FGSM–, projected gradient descent (PGD), and Carlini-Wagner (CW) attack. To mitigate against these attacks, we investigated four pre-processing defenses which do not need adversarial examples during training. The four pre-processing defenses–viz. randomized smoothing, DefenseGAN, variational autoencoder (VAE), and Parallel Wave-GAN vocoder (PWG) are compared against the baseline defense of adversarial training. Performing powerful adaptive white-box adversarial attack (i.e., when the adversary has full knowledge of the system, including the defense), our conclusions indicate that SR systems were extremely vulnerable under BIM, PGD, and CW attacks. Among the proposed pre-processing defenses, PWG combined with randomized smoothing offers the most protection against the attacks, with accuracy averaging 93% compared to 52% in the undefended system and an absolute improvement > 90% for BIM attacks with L∞ > 0.001 and CW attack.
Packet-level prediction of mobile-app traffic using multitask Deep Learning

The prediction of network traffic characteristics helps in understanding this complex phenomenon and enables a number of practical applications, ranging from network planning and provisioning to management, with security implications as well. A significant corpus of work has so far focused on aggregated behavior, e.g., considering traffic volumes observed over a given time interval. Very limited attempts can instead be found tackling prediction at packet-level granularity. This much harder problem (whose solution extends trivially to the aggregated prediction) allows a finer-grained knowledge and wider possibilities of exploitation. The recent investigation and success of sophisticated Deep Learning algorithms is now providing mature tools to face this challenging but promising goal. In this work, we investigate and specialize a set of architectures selected among Convolutional, Recurrent, and Composite Neural Networks, to predict mobile-app traffic at the finest (packet-level) granularity. We discuss and experimentally evaluate the prediction effectiveness of the provided approaches also assessing the benefits of a number of design choices such as memory size or multi-modality, investigating performance trends at packet level focusing on the head and the tail of biflows. We compare the results with both Markovian and classic Machine Learning approaches, showing increased performance with respect to state-of-the-art predictors (high-order Markov chains and Random Forest Regressor). For the sake of reproducibility and relevance to modern traffic, all evaluations are conducted leveraging two real human-generated mobile traffic datasets including different categories of mobile apps. The experimental results witness remarkable variability in prediction performance among different apps categories. The work also provides valuable analysis results and tools to compare different predictors and strike the best balance among the performance measures.
Partial least squares regression residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy

Based on near-infrared (NIR) spectroscopy, a new quantitative calibration algorithm, called “Partial Least Squares Regression Residual Extreme Learning Machine (PLSRR-ELM)”, was proposed for fast determination of research octane number (RON) for blended gasoline. In this algorithm, partial least square (PLS) cooperates with non-linear extreme learning machine (ELM) to separate the relationship information suitable for each other from the raw relationship information (between NIR spectrum and corresponding property) with the unknown degree of non-linearity, with aim of calibrating them respectively. Since the advantages of both PLS and ELM are fully utilized, it is expected that PLSRR-ELM can address the relationship information more effectively and leads to improved calibration performance over PLS and ELM alone. The calibration performance of PLSRR-ELM was evaluated by a set of on-line gasoline blending sample data from a refinery. As a result, it showed an enhanced prediction performance, e.g., about 13% or 11% decrease in the root mean squared error of test (RMSE-T) over PLS or ELM alone, respectively. In method comparison, the model performance of PLRR-ELM exceeds all other methods including PLS, Poly-PLS, KPLS, ELM, and ANN, demonstrating its superiority for fast prediction of gasoline RON.