Reviewerly/exHarmony huggingface dataset:
ModuloNET: Neural Networks Meet Modular Arithmetic for Efficient Hardware Masking

Intellectual Property (IP) thefts of trained machine learning (ML) models through side-channel attacks on inference engines are becoming a major threat. Indeed, several recent works have shown reverse engineering of the model internals using such attacks, but the research on building defenses is largely unexplored. There is a critical need to efficiently and securely transform those defenses from cryptography such as masking to ML frameworks. Existing works, however, revealed that a straightforward adaptation of such defenses either provides partial security or leads to high area overheads. To address those limitations, this work proposes a fundamentally new direction to construct neural networks that are inherently more compatible with masking. The key idea is to use modular arithmetic in neural networks and then efficiently realize masking, in either Boolean or arithmetic fashion, depending on the type of neural network layers. We demonstrate our approach on the edge-computing friendly binarized neural networks (BNN) and show how to modify the training and inference of such a network to work with modular arithmetic without sacrificing accuracy. We then design novel masking gadgets using Domain-Oriented Masking (DOM) to efficiently mask the unique operations of ML such as the activation function and the output layer classification, and we prove their security in the glitch-extended probing model. Finally, we implement fully masked neural networks on an FPGA, quantify that they can achieve a similar latency while reducing the FF and LUT costs over the state-of-the-art protected implementations by 34.2% and 42.6%, respectively, and demonstrate their first-order side-channel security with up to 1M traces.
Hierarchical Transformers Are More Efficient Language Models

Transformer models yield impressive results on many NLP and sequence modeling tasks.Remarkably, Transformers can handle long sequences, which allows them to produce long coherent outputs: entire paragraphs produced by GPT-3 or well-structured images produced by DALL-E.These large language models are impressive but also very inefficient and costly, which limits their applications and accessibility.We postulate that having an explicit hierarchical architecture is the key to Transformers that efficiently handle long sequences.To verify this claim, we first study different ways to downsample and upsample activations in Transformers so as to make them hierarchical.We use the best performing upsampling and downsampling layers to create Hourglass -a hierarchical Transformer language model.Hourglass improves upon the Transformer baseline given the same amount of computation and can yield the same results as Transformers more efficiently.In particular, Hourglass sets new state-of-the-art for Transformer models on the ImageNet32 generation task and improves language modeling efficiency on the widely studied enwik8 benchmark.
Comparing software frameworks of Augmented Reality solutions for manufacturing

Augmented reality (AR) is a technology that allows overlaying of virtual elements on top of the physical environment. This enhances the perception and conveys additional information to the user. With the emergence of industry 4.0 concepts in manufacturing landscape, AR found its way to improve existing Human-Machine Interfaces (HMI) on the shop-floor. The industrial setting has a wide variety of application opportunities from AR, ranging from training and digital work instructions to quality inspection and remote maintenance. Even though its implementation in the industry is rising in popularity, it is still mainly restricted to large companies due the limited availability of resources in Small and Medium Size Enterprises (SME). However, SMEs can benefit from AR solutions in its production processes. Therefore, this research aims to develop and present the results of comparison of two simple and cost-effective AR software frameworks for Hand Held Device (HHD) and a Head Mounted Device (HMD), which can be applied for developing AR applications for manufacturing. Two AR applications are developed using these software frameworks which are presented in the case study section. Android device is chosen as a HHD and HoloLens is the HMD used in the case study. The development structure can be reproduced by a wider range of enterprises with diverse needs and resource availability.
A Survey of Smart Contract Vulnerability Research

Smart contract is a decentralized application that operates on a blockchain platform, providing secure and reliable capabilities to contract participants. Smart contracts play an important role in decentralized application scenarios. They are widely used in many fields, such as equity crowdfunding, games, insurance, and the Internet of Things, making them attractive to attackers. Compared to traditional programs, the security of smart contracts affects not only the fairness of contracts but also the safety of high volume digital assets on the blockchain managed by contracts. Therefore, analyzing the security of smart contracts and associated vulnerabilities is crucial. In this paper, we analyzed the characteristics of smart contracts and new security risks they bring. We propose a three-layer threat model, i.e., threats from high-level languages, virtual machines, and the blockchain, for characterizing smart contract security. We use the world′s largest smart contract platform Ethereum as an example to illustrate 15 types of common vulnerabilities in smart contracts. We then summarize the main challenges and progress of smart contract security research on vulnerability, including automated vulnerability detection, automated exploit generation and mitigations for smart contracts. At the end of this paper, we highlight the future of smart contract security research, and proposed two potential research directions.
A nonlocal enhanced Low-Rank tensor approximation framework for 3D Magnetic Resonance image denoising

Magnetic Resonance Imaging (MRI) has become an increasingly essential tool in clinical detection and diagnosis for its ability to disclose the distinctive information of human anatomic structures in vivo. However, the image intensity in magnitude MRI images is frequently corrupted by Rician noise which is inherent to the acquisition process. This study aimed to propose a denoising model for 3D MR image restoration based on low-rank tensor approximation with a Logarithmic-Sum regularization framework. Nonlocal similarity of images was exploited with grouping and block matching techniques, and tensor decomposition approaches were utilized taking consideration of multi-dimensional structural dependence conservation. The low-rank tensor approximation problem was regulated by introducing a Logarithmic-Sum trace norm, which thresholded the singular value spectrum adaptively with different thresholds. Noise removal was processed through the low-rank approximation, and the denoised tensor blocks were aggregated to restore the clear images. Numerical experiments were conducted under comprehensive noise conditions for 3D MR volumetric datasets. Through denoising comparison, the results demonstrated that the proposed algorithm achieves state-of-the-art performance for Rician noise removal with excellent detail preservation..
Self-Supervised Deep Monocular Depth Estimation With Ambiguity Boosting

We propose a novel two-stage training strategy with ambiguity boosting for the self-supervised learning of single view depths from stereo images. Our proposed two-stage learning strategy first aims to obtain a coarse depth prior by training an auto-encoder network for a stereoscopic view synthesis task. This prior knowledge is then boosted and used to self-supervise the model in the second stage of training in our novel ambiguity boosting loss. Our ambiguity boosting loss is a confidence-guided type of data augmentation loss that improves the accuracy and consistency of generated depth maps under several transformations of the single-image input. To show the benefits of the proposed two-stage training strategy with boosting, our two previous depth estimation (DE) networks, one with t-shaped adaptive kernels and the other with exponential disparity volumes, are extended with our new learning strategy, referred to as DBoosterNet-t and DBoosterNet-e, respectively. Our self-supervised DBoosterNets are competitive, and in some cases even better, compared to the most recent supervised SOTA methods, and are remarkably superior to the previous self-supervised methods for monocular DE on the challenging KITTI dataset. We present intensive experimental results, showing the efficacy of our method for the self-supervised monocular DE task.
GANs and Artificial Facial Expressions in Synthetic Portraits

Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, and buildings, among others. Not only image generation, but also image manipulation is possible with GANs. Generative deep learning models are inherently limited in their creative abilities because of a focus on learning for perfection. We investigated the potential of GAN’s latent spaces to encode human expressions, highlighting creative interest for suboptimal solutions rather than perfect reproductions, in pursuit of the artistic concept. We have trained Deep Convolutional GAN (DCGAN) and StyleGAN using a collection of portraits of detained persons, portraits of dead people who died of violent causes, and people whose portraits were taken during an orgasm. We present results which diverge from standard usage of GANs with the specific intention of producing portraits that may assist us in the representation and recognition of otherness in contemporary identity construction.
Multilingual and Cross-Lingual Intent Detection from Spoken Data

We present a systematic study on multilingual and cross-lingual intent detection (ID) from spoken data.The study leverages a new resource put forth in this work, termed MINDS-14, a first training and evaluation resource for the ID task with spoken data.It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) yield strong intent detectors in the majority of target languages covered in MINDS-14, and offer comparative analyses across different axes: e.g., translation direction, impact of speech recognition, data augmentation from a related domain.We see this work as an important step towards more inclusive development and evaluation of multilingual ID from spoken data, hopefully in a much wider spectrum of languages compared to prior work.
Delegation-Based Personal Data Processing Request Notarization Framework for GDPR Based on Private Blockchain

With the growing awareness regarding the importance of personal data protection, many countries have established laws and regulations to ensure data privacy and are supervising managements to comply with them. Although various studies have suggested compliance methods of the general data protection regulation (GDPR) for personal data, no method exists that can ensure the reliability and integrity of the personal data processing request records of a data subject to enable its utilization as a GDPR compliance audit proof for an auditor. In this paper, we propose a delegation-based personal data processing request notarization framework for GDPR using a private blockchain. The proposed notarization framework allows the data subject to delegate requests to process of personal data; the framework makes the requests to the data controller, which performs the processing. The generated data processing request and processing result data are stored in the blockchain ledger and notarized via a trusted institution of the blockchain network. The Hypderledger Fabric implementation of the framework demonstrates the fulfillment of system requirements and feasibility of implementing a GDPR compliance audit for the processing of personal data. The analysis results with comparisons among the related works indicate that the proposed framework provides better reliability and feasibility for the GDPR audit of personal data processing request than extant methods.
The relationship between the willingness to mobile learning and educational achievements in health-care professional students

The relationship between willingness to mobile learning (m-learning) and educational achievement was examined in health-care professional students.This is a descriptive correlative study that was conducted from January 2020 to February 2020. A total of 295 students in Saveh University of Medical Sciences in Iran were selected through census method. The data were collected using the standard willingness to m-learning questionnaire. The data collected were analyzed statistically using Pearson's correlation coefficient and concurrent regression analysis.The mean score of willingness to m-learning was 165.55 ± 13.4, which is an indicative of a higher willingness level in the health-care professional students for m-learning. There was a positive and significant relationship between willingness for m-learning and educational achievement (r = 0.77, P < 0.01). The predictive variable, i.e., willingness to m-learning, predicted 53.8% of the variance of educational achievement (F = 58.801, P = 0.00). Among the variables of willingness to m-learning, the regression coefficients of perceived ease, attitude, self-management in learning, educational use, and efficiency of m-learning were significant (P < 0.05). This means that these variables are direct predictors of educational achievement.Willingness to m-learning had a positive and significant relationship with educational achievement. Although this study was performed just before the corona outbreak, paying attention to the results of this study can be helpful for students, faculty members, and policymakers in filling the educational gap during the coronavirus disease 2019 outbreak.
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results

In this paper, we introduce the Eval4NLP-2021 shared task on explainable quality estimation.Given a source-translation pair, this shared task requires not only to provide a sentencelevel score indicating the overall quality of the translation, but also to explain this score by identifying the words that negatively impact translation quality.We present the data, annotation guidelines and evaluation setup of the shared task, describe the six participating systems, and analyze the results.To the best of our knowledge, this is the first shared task on explainable NLP evaluation metrics.Datasets and results are available at https://github.com/eval4nlp/SharedTask2021.
Enterprise Platform of Logistics Services Based on a Multi-Agents Mechanism and Blockchains

We present a multi-agent mechanism and blockchains platform dedicated to logistics services and activities, which allows controlling the use of connected “objects” via an Internet of Things structure. The proposed platform allows decentralization through a peer-to-peer communication offered by blockchains capable of smart contracting, as well as centralization with the multi-agent system permitting to provide a more robust global mechanism to secure the Internet of Things network and involved enterprises. Indeed, the decentralized part enhances the efficiency (security, reliability, simplicity, etc.). The centralized part allows facilitating the complete and optimized management of the Internet of Things network in terms of uses, reallocations or modifications but also in terms of economic benefits for users. In this paper, we develop the complete architecture of the proposed platform and connection between multi-agent system, blockchains, cloud system, connected objects and users and detail the first level of relationships between agents. We also explain the links between all elements and the functioning of our proposed enterprise platform.
An Improved Equilibrium Optimizer Algorithm for Tackling Global Optimization Problems

This paper introduces a new, metaheuristic optimization algorithm, named an Improved Metaheuristic Equilibrium Optimizer (IMEO). The algorithm Equilibrium Optimizer (EO), is inspired by its method of estimating both equilibrium and dynamics, based on mass balance models. Studying the EO closely, we find that EO does not have the potential to get closer to the optimal global solution when it solves certain problems. To improve EO efficiency, this paper suggests using an improvement, called an elite opposition learning-based, that increases the speed and accuracy of EO convergence, and helps the algorithm to get a better solution. Falling into local optima is a big problem, EO suffers from the fact that when we look deeply at the standard EO mathematical formula, we found that the algorithm is trying to get out of the local optima, but sometimes it can't, so we're introducing a new mathematical formula based on using cosine trigonometric function. To validate the proposed algorithm efficiency, The IMEO algorithm is evaluated on 23 benchmarks and compared with other common naturalistic heuristic algorithms. The statistical analysis shows that the results of IMEO achieve better performance compared to the standard EO and several well-known algorithms on several benchmark issues.
PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems

Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing policy, which recommends personalized learning material based on the learning experiences of the learner to maximize their learning outcomes. However, limited available information such as cognitive, affective states, and competence levels of the learners ongoing knowledge points servers critical challenges to optimizing individual-specific pedagogical instructions in real-time. Moreover, making such decisions policy for every learner with a unique knowledge profile demands a trade-off between learner current knowledge and curiosity to learn next knowledge point. To address these challenges, this paper proposes a personalized adaptability knowledge extraction strategy (PAKES) using cognitive diagnosis and reinforcement learning (RL). We apply the general diagnostic model to track the current knowledge state of the learners. Subsequently, an RL-based Q-learning algorithm is employed to recommend optimal pedagogical instructions for individuals to meet their learning objectives while maintaining equilibrium among the learner-control and teaching trajectories. The results indicate that the learning analytics of the proposed framework can fairly deliver the optimal pedagogical paths for the learners based upon their learning profiles. A 62% learning progress score was achieved with the pedagogical paths recommended by the PAKES, showing a 20% improvement compared to the baseline model.
Exploratory Data Analysis on Username-Password Dataset

Passwords act as a first line of defense against any malicious or unauthorized access to one's personal information. With the increasing digitization, it has now become even more important to choose strong passwords. In this paper, the authors analyze a 100 million Email-Password Database to perform Exploratory Data Analysis. The analysis provides valuable insights on statistics about the most common passwords being used, character set of passwords, most common domains, average length, password strength, frequencies of letters, numbers, symbols (special characters), most common letter, most common number, most common symbol, the ratio of letters, numbers, symbols in passwords which highlights the general trend that users follow while creating passwords. Using the results of this paper, users can make intelligent decisions while creating passwords for themselves, i.e., not opting for the most common features that will help them create robust and less vulnerable passwords.
Design of English Learning Effectiveness Evaluation System Based on K-Means Clustering Algorithm

English is the universal language of the world. In the context of global economic integration, English learning is not only an essential course for business elites but also a required course for the general public. Currently, in colleges and universities across the world, English is presented as a compulsory first foreign language course. Therefore, how to improve the effect of English performance assessment in the context of smart teaching has become an important part of smart English teaching. Due to the influence of interference factors, human factors, or external factors, the traditional English language teaching evaluation system has the problems of high system sensitivity, long envelope delay jitter time, and short stationary state maintenance time. Therefore, this study develops an English learning effectiveness evaluation system based on a K-means clustering algorithm. The SQL Server 2005 database management software is used to develop the system database; various functional modules of the system are designed using ActiveX, with emphasis on the design of scoring functional modules; and different roles and permissions are given to administrators, teachers, and students. A student English learning effectiveness evaluation model based on BP neural network training and K-means clustering algorithm is designed to optimize the English learning effectiveness evaluation model and achieve effective English learning by solving the consistent estimate of the effectiveness of English learning assessment. The performance test results show that the proposed system has a lower sensitivity coefficient, a shorter envelope delay jitter time, and a longer period of steady-state maintenance, indicating that the system can achieve stable operation.
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations

Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines' ability to understand human language.Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at the prediction.One of the most important interpretation forms is to attribute model decisions to input features.Based on post-hoc interpretation methods, we assess attributions of paragraphs in multiplechoice MRC and improve the model by punishing the illogical attributions.Our method can improve model performance without any external information and model structure change.Furthermore, we also analyze how and why such a self-training method works.