Reviewerly/exHarmony huggingface dataset:
Efficient Robust Training via Backward Smoothing

Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational costs due to the iterative adversarial attacks in each training step. Recent studies show that it is possible to achieve fast Adversarial Training by performing a single-step attack with random initialization. However, such an approach still lags behind state-of-the-art adversarial training algorithms on both stability and model robustness. In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. Following this new perspective, we also propose a new initialization strategy, backward smoothing, to further improve the stability and model robustness over single-step robust training methods. Experiments on multiple benchmarks demonstrate that our method achieves similar model robustness as the original TRADES method while using much less training time (~3x improvement with the same training schedule).
EM-Fuzz: Augmented Firmware Fuzzing via Memory Checking

Embedded systems are increasingly interconnected in the emerging application scenarios. Many of these applications are safety critical, making it a high priority to ensure that the systems are free from malicious attacks. This work aims to detect vulnerabilities, that could be exploited by adversaries to compromise functional correctness, in the embedded firmware, which is challenging especially due to the absence of source code. In particular, we propose EM-Fuzz, a firmware vulnerability detection technique that tightly integrates fuzzing with real-time memory checking. Based on the memory instrumentation, the firmware fuzzing can not only be guided by the traditional branch coverage to generate high-quality seeds to explore hard-to-reach regions but also by the recorded memory sensitive operations to continuously exercise sensitive regions which are prone to being attacked. More importantly, the instrumentation integrates real-time memory checkers to expose memory vulnerabilities, which is not well-supported by existing fuzzers without source code. The experiments on several real-world embedded firmware such as OpenSSL demonstrate that EM-Fuzz significantly improves the performance of state-of-the-art fuzzing tools, such as AFL and AFLFast, with the coverage improvements of 93.98% and 46.89%, respectively. Furthermore, EM-Fuzz exposes a total of 23 vulnerabilities, with an average of about 7-h per vulnerability. AFL and AFLFast together find 10 vulnerabilities, costing about 13 h and 10-h per vulnerability on average, respectively. Out of these 23 vulnerabilities, 16 are previously unknown and have been reported to the upstream product vendors, 7 of which have been assigned with unique CVE identifiers in the U.S. National Vulnerability Database.
Spatial Frequency Bias in Convolutional Generative Adversarial Networks

Understanding the capability of Generative Adversarial Networks (GANs) in learning the full spectrum of spatial frequencies, that is, beyond the low-frequency dominant spectrum of natural images, is critical for assessing the reliability of GAN-generated data in any detail-sensitive application. In this work, we show that the ability of convolutional GANs to learn an image distribution depends on the spatial frequency of the underlying carrier signal, that is, they have a bias against learning high spatial frequencies. Our findings are consistent with the recent observations of high-frequency artifacts in GAN-generated images, but further suggest that such artifacts are the consequence of an underlying bias. We also provide a theoretical explanation for this bias as the manifestation of linear dependencies present in the spectrum of filters of a typical generative Convolutional Neural Network (CNN). Finally, by proposing a proof-of-concept method that can effectively manipulate this bias towards other spatial frequencies, we show that the bias is not fixed and can be exploited to explicitly direct computational resources towards any specific spatial frequency of interest in a dataset, with minimal computational overhead.
Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree

Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on.To mine frequent itemsets, previous algorithms based on a prefix tree structure have to construct many prefix trees, which is very time-consuming.In this paper, we propose a novel frequent itemset mining algorithm called DPT (Dynamic Prefix Tree) which uses only one prefix tree.We first introduce the concept of the post-conditional database of an itemset, and analyze the distribution of an itemset's post-conditional database in a prefix tree representing a database.Subsequently, we illuminate how DPT adjusts the prefix tree to mine frequent itemsets and give three optimization techniques.An interesting advantage of DPT is that the algorithm can directly output a prefix tree representing all frequent itemsets after slight modifications.Using only one dynamic prefix tree, DPT avoids the high cost of constructing many prefix trees and thus gains significant performance improvement.Experimental results show that DPT remarkably outperforms previous algorithms with respect to running time and memory usage, and that a prefix tree representing all frequent itemsets DPT outputs can be used more efficient than a list representing them previous algorithms output.
INVITED: Computation on Sparse Neural Networks and its Implications for Future Hardware

Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are very computationally intensive and reach the hardware limit of the existing server and IoT devices. Thus, finding better model architectures with much less amount of computation while maximally preserving the accuracy is a popular research topic. Among various mechanisms that aim to reduce the computation complexity, identifying the zero values in the model weights and in the activations to avoid computing them is a promising direction. In this paper, we summarize the current status of the research on the computation of sparse neural networks, from the perspective of the sparse algorithms, the software frameworks, and the hardware accelerations. We observe that the search for the sparse structure can be a general methodology for high-quality model explorations, in addition to a strategy for high-efficiency model execution. We discuss the model accuracy influenced by the number of weight parameters and the structure of the model. The corresponding models are called to be located in the weight dominated and structure dominated regions, respectively. We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region. In order to achieve the goal, new approaches are required to search for proper sparse structures, and new sparse training hardware needs to be developed to facilitate fast iterations of sparse models.
Towards a Better Understanding of Agile Mindset by Using Principles of Agile Methods

The right way to agility should start with a proper agile mindset instead of applying Agile methods directly.However, apart from the manifesto, it is unlikely to find a comprehensive set of Agile principles that can serve for an improved agile mindset.Our study intends to fulfill this gap in a systematic way: providing a list of the Agile methods along with their principles within a single source, in the way of providing a better understanding of the concept of agility from a wide and exhaustive perspective.To do so, the collected 105 principles were content-analyzed in order to group them into 32 categories for a higher-level abstraction.These categories then were subsumed into two main categories.The whole grouping process was reviewed by one expert and the list was adjusted accordingly.Then, based on the consolidated list of the categorized principles, analysis and evaluations were made by the authors.As a part of the evaluations, semi-structured interviews with two experts were conducted to evaluate the categorized principles in general, especially in terms of their contribution to agility.
BOREALIS: Building Block for Sealed Bid Auctions on Blockchains

We focus on securely computing the ranks of sealed integers distributed among n parties. For example, we securely compute the largest or smallest integer, the median, or in general the kth-ranked integer. Such computations are a useful building block to securely implement a variety of sealed-bid auctions. Our objective is efficiency, specifically low interactivity between parties to support blockchains or other scenarios where multiple rounds are time-consuming. Hence, we dismiss powerful, yet highly-interactive MPC frameworks and propose BOREALIS, a special-purpose protocol for secure computation of ranks among integers. BOREALIS uses additively homomorphic encryption to implement core comparisons, but computes under distinct keys, chosen by each party to optimize the number of rounds. By carefully combining cryptographic primitives, such as ECC Elgamal encryption, encrypted comparisons, ciphertext blinding, secret sharing, and shuffling, BOREALIS sets up systems of multi-scalar equations which we efficiently prove with Groth-Sahai ZK proofs. Therewith, BOREALIS implements a multi-party computation of pairwise comparisons and rank zero-knowledge proofs secure against malicious adversaries. BOREALIS completes in at most 4 rounds which is constant in both bit length l of integers and the number of parties n. This is not only asymptotically optimal, but surpasses generic constant-round secure multi-party computation protocols, even those based on shared-key fully homomorphic encryption. Furthermore, our implementation shows that BOREALIS is very practical. Its main bottleneck, ZK proof computations, is small in practice. Even for a large number of parties (n=200) and high-precision integers (l=32), computation time of all proofs is less than a single Bitcoin block interval.
Two-photon amplitude interferometry for precision astrometry

Improved quantum sensing of photons from astronomical objects could provide high resolution observations in the optical benefiting numerous fields, including general relativity, dark matter studies, and cosmology.It has been recently proposed that stations in optical interferometers would not require a phase-stable optical link if instead sources of quantum-mechanically entangled pairs could be provided to them, potentially enabling hitherto prohibitively long baselines.A new refinement of this idea is developed, in which two photons from different sources are interfered at two separate and decoupled stations, requiring only a slow classical information link between them.We rigorously calculate the observables and contrast this new interferometric technique with the Hanbury Brown & Twiss intensity interferometry.We argue this technique could allow robust high-precision measurements of the relative astrometry of the two sources.A basic calculation suggests that angular precision on the order of 10 microarcsecond in the relative opening angle could be achieved in a single night's observation of two bright stars.
Intelligent Visual Similarity-Based Phishing Websites Detection

This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.
Combination of deep speaker embeddings for diarisation

Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for clustering speech segments. To extract better-performing and more robust speaker embeddings, this paper proposes a c-vector method by combining multiple sets of complementary d-vectors derived from systems with different NN components. Three structures are used to implement the c-vectors, namely 2D self-attentive, gated additive, and bilinear pooling structures, relying on attention mechanisms, a gating mechanism, and a low-rank bilinear pooling mechanism respectively. Furthermore, a neural-based single-pass speaker diarisation pipeline is also proposed in this paper, which uses NNs to achieve voice activity detection, speaker change point detection, and speaker embedding extraction. Experiments and detailed analyses are conducted on the challenging AMI and NIST RT05 datasets which consist of real meetings with 4--10 speakers and a wide range of acoustic conditions. For systems trained on the AMI training set, relative speaker error rate (SER) reductions of 13% and 29% are obtained by using c-vectors instead of d-vectors on the AMI dev and eval sets respectively, and a relative reduction of 15% in SER is observed on RT05, which shows the robustness of the proposed methods. By incorporating VoxCeleb data into the training set, the best c-vector system achieved 7%, 17% and16% relative SER reduction compared to the d-vector on the AMI dev, eval, and RT05 sets respectively
Creating a Foundation of Well-being for Teachers and Students Starts with SEL Curriculum in Teacher Education Programs

During the COVID-19 crisis, it has become clear how unprepared our educational systems are to provide social and emotional support through distance learning.Despite the demands for teachers to support the social and emotional development of their students, our universities are behind the curve in providing coursework to develop their knowledge and skills in these areas.This paper calls us to imagine teacher education with Social and Emotional Learning (SEL) as a cornerstone in teacher preparation programs.We outline the importance of SEL curriculum in preservice education and suggest a multifaceted approach to teacher preparation.
Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference

We present a novel approach for training deep neural networks in a Bayesian way. Compared to other Bayesian deep learning formulations, our approach allows for quantifying the uncertainty in model parameters while only adding very few additional parameters to be optimized. The proposed approach uses variational inference to approximate the intractable a posteriori distribution on basis of a normal prior. By representing the a posteriori uncertainty of the network parameters per network layer and depending on the estimated parameter expectation values, only very few additional parameters need to be optimized compared to a non-Bayesian network. We compare our approach to classical deep learning, Bernoulli dropout and Bayes by Backprop using the MNIST dataset. Compared to classical deep learning, the test error is reduced by 15%. We also show that the uncertainty information obtained can be used to calculate credible intervals for the network prediction and to optimize network architecture for the dataset at hand. To illustrate that our approach also scales to large networks and input vector sizes, we apply it to the GoogLeNet architecture on a custom dataset, achieving an average accuracy of 0.92. Using 95% credible intervals, all but one wrong classification result can be detected.
Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The objective of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format. We evaluate our approach on the 3D skeletal data provided in the large NTU_RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of the 60 action classes. We also quantitatively evaluate the performance of our model by computing Fréchet inception distances, which shows strong correlation to human judgement. To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.
Digital Literacy and Digital Skills in University Study

Recently, the digitalization phenomenon has been trending upwards globally. This term has occupied all spheres of our lives, including education. Along with global tendencies and calls of the Industrial Revolution, 4.0 national projects outlined by the president in the particular project “Digital economy” have provided many impulses to the Digitalization of education.This research paper is mainly devoted to exploring digital education and digital learning in Russia's realities today. The author utilizes the current situation with lockdown and, therefore, distance education and learning to try to shed light on some aspects of educational Digitalization. The article provides a theoretical discussion of the irreversibility and necessity of Digitalization of education, its components, stages, structure, advantages, and disadvantages; of what has been done and what is to be done in this field. The author also provides empirical data of studying Kazan Federal University students in foreign language classes during distance education and learning period. Remarkably, the article offers some insight into students’ readiness for the digital era, evaluating their digital literacy and digital skills and competencies, their motivation to keep on studying while on distance, their abilities to take responsibility for their learning as well as some issues challenging students during distance learning.
Autochain platform: expert automatic algorithm Blockchain technology for house rental dApp image application model

Abstract This paper deals with the current Blockchain adopted in the various types of trading markets supporting smart contracts that process text-based transaction information only when exchanging cryptocurrencies. Even though the Blockchain itself is decentralized, services can still be provided through a centralized system in order to provide adequate services to the users. It also allows the Autochain to apply Blockchain technology to existing businesses conducted under the Fourth Industrial Revolution to realize higher productivity or competitiveness and improve profitability to a remarkable extent. The Autochain is adaptable to all Blockchain technologies, companies, or research works around the world to overcome the limitations of the existing Blockchain technology. It creates the Blockchain-platform ecology chain and ecosystem, making our lives more convenient while revolutionizing every industry. In addition, it automates all Blockchain-based systems and offers a base for constructing a system wherein everyone can easily provide, operate, and maintain Blockchain services. This paper discusses a number of Blockchain-related technologies and presents a decentralized Application (dApp) exclusively designed for mobile use, taking house rental service as an example. It also presents the existing Blockchain and the Blockchain database storage method and proposes the dApp based on it.
Prediction of Short-Time Cloud Motion Using a Deep-Learning Model

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications.
Fractal Pattern for Multiscale Digital Image Correlation

Digital Image Correlation (DIC) is based on the matching, between reference and deformed state images, of features contained in patterns that are deposited on test sample surfaces. These features are often suitable for a single scale, and there is a current lack of multiscale patterns capable of providing reliable displacement measurements over a wide range of scales. Here, we aim to demonstrate that a pattern based on a fractal (self-affine) surface would make a suitable pattern for multiscale DIC. A method to numerically generate patterns directly from a desired auto-correlation function is introduced. It is then enhanced by a Mean Intensity Gradient (MIG) improvement process based on grey level redistribution. Numerical experiments at multiple scales are performed for two different imposed displacement fields and results for one of the patterns generated are compared with those obtained for a random pattern and a Perlin noise one. The proposed pattern is shown to lead to DIC errors comparable to those found with the two others for the first scales, but has much greater robustness. More importantly, the pattern generated here exhibits stable errors and robustness with respect to the scale whereas these two outputs become significantly degraded for the other two patterns as the scale increases. As a result, scale invariance properties of the pattern based on fractal surfaces correspond to scale invariance in DIC errors as well. This is of great interest regarding the use of such patterns in multiscale DIC.