Reviewerly/exHarmony huggingface dataset:
CPA/CCA2-secure PKE with squared-exponential DFR from low-noise LPN

LPN (learning parity with noise) problem is a good candidate for post-quantum cryptography which enjoys simplicity and suitability for weak-power devices. Döttling et al. (ASIACRYPT 2012) initiated the first secure public key encryption (PKE) under the low-noise LPN assumption. Kiltz et al. (PKC 2014) proposed a simpler and more efficient scheme using double-trapdoor technique from the same assumption. Both schemes abide the decoding failure rate (DFR) 2−Θ(k) (k is the security parameter) and there exists CPA/CCA2-secure PKE with squared-exponential DFR 2−Θ(k2) from constant-noise LPN (Yu and Zhang, CRYPTO 2016). In this work, we give a positive answer with squared-exponential DFR in the low-noise setting. More precisely, we first introduce a variant (VxLPN) of the low-noise Exact LPN (xLPN, proposed by Jain et al. at ASIACRYPT 2012 and used as building block in commitments and zero-knowledge proofs), where the coefficient matrix A follows the uniform distribution over {0,1}q×n (n=Θ(k2),q=Θ(n)), the secret x is sampled from Bμn (Bμ is the Bernoulli distribution with noise rate μ=Θ(1q)), and the noise e follows a column vector distribution uniform over {z∈{0,1}q:|z|=qμ}. A series of reductions show that VxLPN is at least as hard as the standard LPN for the same noise rate μ. We then construct from the VxLPN CPA/CCA2 secure PKE schemes with squared-exponential DFR 2−Θ(k2) which share the common structure extrinsically with Kiltz et al. and Yu-Zhang schemes. The secret key(s) in our schemes are simply sampled from the Bernoulli distribution, and comparatively, the secret key(s) in Yu-Zhang schemes must be chosen from a tailored version of Bernoulli distribution (along with the coefficient matrix A that follows a distribution Dλn×n=Un×λ⋅Uλ×n induced by multiplying two random matrices in the public key, λ=Θ(log2⁡n)) in order to guarantee the correctness of their schemes. Consider the performance on 128-bit security level, our CCA2-secure scheme only holds 117.79 MB public keys, 67.31 MB secret keys and 10.15 KB ciphertexts, and thus is more efficient than the schemes of Döttling et al. and Kiltz et al. ((14.53 GB, 14.48 GB, 14.06 KB) and (161.78 MB, 92.45 MB, 13.60 KB) respectively).
Learning Automata for Behavior Control in Evolutionary Computation

Global optimization plays a crucial role in different branches of science and engineering. The rapid development of technology gives rise to complex problems where several objectives may need to be satisfied. Therefore, there is a constant need to provide new optimization techniques and improving the existing methods. One of the most popular research topics in the literature on optimization is function optimization. The main reason is that many real-world optimization problems can be modeled as function optimization. Therefore, function optimization problems can be used as a benchmark for comparing the performance of different algorithms. Analytical methods encounter many difficulties when applying to complex optimization problems; thus, it is impossible to apply them for many practical cases. Evolutionary computation (EC) techniques, however, have been proven to work better than analytical methods. Although being effective, EC methods’ performance often depends on the choice of their parameters, and finding the good parameter values for EC methods is a conventional approach for improving their efficiency for numerical optimization. This chapter investigates the usefulness of learning automaton (LA) to control EC methods’ behavior by adjusting their parameters during the run. To this end, we are running the LA and the EC side by side, and the LA controls the EC on-the-fly parameters.
Communicative culture of the future specialist of a technical university and his personal development in modern realities

The important characteristics of a person and activity of future specialist are not only the possession of information, of professional activity technologies, but also of communicative culture, which provides going beyond the regulatory activities, ability to create and transfer values, provide personal development and competitiveness on the labor market. Today, a low level of speech culture leads to a decrease in the general culture of young people and, as a result, is an obstacle to further career development in any field. The problem generally affects such industries as construction, architecture, design, as they are among the most demanded professions on the international labor markets.
“A Crowdsourcing-based QoE Evaluation of an Immersive VR Autonomous Driving Experience”

Due to COVID-19, crowdsourcing has gained momentum as an alternative methodology for continuing research and for Quality of Experience (QoE) assessment. Employing this approach, we remotely evaluated the user perceived QoE of two different visual rendering formats as part of an Autonomous Vehicles (AVs) simulation. The aim was to investigate the participant's QoE when testing AV technology in distinct visual rendering qualities (lowpoly vs high-poly) of an online streamed 360° car riding experience. In addition, a scoring model based on the expected reliability of each level of the remote assessment was designed. Findings suggest that the consumer's preferences towards the adoption of AV technology is highly determined by the system and human effects on Influence Factors (IFs). Moreover, the adequacy of reliability into a mathematical model is highlighted as a potential turning point for QoE assessment, by carrying out the evaluation tasks from the laboratory environment into the internet, particularly relevant in pandemic times.
Amplified Noise Map Guided Network for Low-Light Image Enhancement

Low-light image is easily degraded by real noise, which brings great challenges for image enhancement task because the enhancement process will amplify the noise. To address this problem, we propose an amplified noise map guided network (AMG-Net), which simultaneously achieves the low-light enhancement and noise removal by extracting amplified noise map to guide the network training. Specifically, we build an encoder-decoder network as the basic enhancement model to get a preliminary enhanced image that usually includes amplified noise. Subsequently, we fed the preliminary enhanced image into a noise map estimator to continuously estimating the amplified noise map during the enhancement process by adopting residual connection. Finally, a residual block with adaptive instance normalization (AIN) is used to build a denoising model, which is guided by the noise map estimator to remove the amplified noise. Extensive experimental results demonstrate that the proposed AMG-Net can achieve competitive results compared with the existing state-of-the-art methods.
Research on PLC Electrical Control Simulation Experimental Device Based on Internet of Things Technology

The PLC electrical control technology practice course is an important practice course in the electrical engineering application-oriented talent training plan. It is aimed at the low utilization rate of the laboratory, high cost of experimental consumables, and student learning progress in the traditional PLC electrical control technology practice course. It is difficult to unify and lack of learning resources, etc., and a PLC electrical control simulation experimental device based on the Internet of Things technology is proposed. The experimental device uses the Internet of Things technology to realize the cloud experiment mode of simulation software and local equipment linkage control, so that students are not restricted by time, space and experimental content, and can complete various experiments anytime and anywhere, thereby solving some of the problems of the traditional practical teaching mode, To further improve students' learning enthusiasm and initiative, and to provide a reliable practical teaching platform for cultivating high-quality applied talents.
Forecasting time series data containing outliers with the ARIMA additive outlier method

Abstract ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA.
Case Retrieval with Clustering for a Case-Based Reasoning and Inverse Problem Methodology: An Investigation of Financial Bubbles

This paper proposes an approach for predicting abnormal asset performance in traded securities, often referred to as ‘financial bubbles’. It uses an ensemble technique based on Case-based Reasoning (CBR) and Inverse Problems (IP), which we term IPCBR. More specifically we propose a Machine Learning formative strategy to determine the causes of stock behaviour, rather than to predict future time series points in fuzzy environments. In so doing, our paper contributes to more robust strategies in investigating financial bubbles. The framework uses a geometric pattern description of historical time series and applies clustering techniques to derive a model that generalizes those patterns onto observations. The model constitutes the forward approach to the IPCBR framework; our results demonstrate that, given the target problem, our CBR model provides a computationally inexpensive description of abnormal asset performance.
Online Curriculum Management of Higher Education Under Mobile Platform

The web crawler technology obtains data from experiment such as learner-curriculum interaction data, course content data and knowledge map. After crawling the data from the network, the data cleaning work is carried out, which is to analyse, sort, calculate and edit various raw data. Finally, the processed data is stored as data files, and the collection of relevant basic data sets of curriculum recommendation is completed. In this paper, the online curriculum management system of higher education based on the design of mobile platform adopts the recommendation model of knowledge map enhancement Ripple_mlp and its improved model Rippk_mlP+. Firstly, the common algorithms of curriculum recommendation are analyzed, and the source of ideas for the construction of new model is described. Then, the scenario of curriculum recommendation is defined in abstract, and the overall framework of the model is summarized, and the specific contents of the four parts of the model are described in detail. Then, based on the feature of "one to many" in the course recommendation area, the paper introduces Co-net into Ripple_mlp model to optimize it. Finally, the optimization method of the model is introduced.
ISO 9001:2015 Quality Management System Requirements and Audit Findings Classification Using Support Vector Machine and Long Short-Term Memory Neural Network: An Optimization Method

Generating accurate and timely internal and external audit reports may seem difficult for some auditors due to limited time or expertise in matching the correct clauses of the standard with the textual statement of findings. To overcome this gap, this paper presents the design of text classification models using support vector machine (SVM) and long short-term memory (LSTM) neural network in order to automatically classify audit findings and standard requirements according to text patterns. Specifically, the study explored the optimization of datasets, holdout percentage and vocabulary of learned words called NumWords, then analyzed their capability to predict training accuracy and timeliness performance of the proposed text classification models. The study found that SVM (96.74%) and LSTM (97.54%) were at par with each other in terms of the best training accuracy, although SVM (67.96±17.93 seconds [s]) was found to be significantly faster than LSTM (136.67±96.42 s) in any dataset size. The study proposed optimization formulas that highlight dataset and holdout as predictors of accuracy, while dataset and NumWords as predictors of timeliness. In terms of actual implementation, both classification models were able to accurately classify 20 out of 20 sample audit findings at 1 and 3 s, respectively. Hence, the extent of choosing between the two algorithms depend on the datasets size, learned words, holdout percentage, and workstation speed. This paper is part of a series, which explores the use of Artificial Intelligence (AI) techniques in optimizing the performance of QMS in the context of a state university.
An Efficient Approach for Mining Weighted Frequent Patterns from Data Stream

The discovery of frequent patterns from various datasets is a crucial aspect of modern data science. It helps to understand the relationship, dependence, structure, and diverse nature of data to take future actions like business decision making, catalog design, etc. Sometimes finding only frequent patterns is not enough as some items are less frequent but preserve high importance. For this reason, researchers have introduced the concept of weighted frequent pattern mining. Moreover, the situation becomes more critical when the dataset's size is enormous, and new data batches come at different velocities. Existing methods that have been proposed so far to handle such data streams are time-consuming and memory-hungry. This paper proposes an efficient sliding window-based approach that captures the data stream in a compact tree and mine weighted frequent patterns that overcomes both problems. We have conducted extensive experiments with the proposed method on different datasets. Experimental results exhibit significant improvement regarding both runtime and memory usage compared to existing approaches.
Accelerating Transformer for Neural Machine Translation

Neural Machine Translation (NMT) models based on Transformer achieve promising progress in both translation quality and training speed. Such a strong framework adopts parallel structures that greatly improve the decoding speed without losing quality. However, due to the self-attention network in decoder that cannot maintain the parallelization under the auto-regressive scheme, the Transformer did not enjoy the same speed performance as training when inference. In this work, with simplicity and feasibility in mind, we introduce a gated cumulative attention network to replace the self-attention part in Transformer decoder to maintain the parallelization property in the inference phase. The gated cumulative attention network includes two sub-layers, a gated linearly cumulative layer that creates the relationship between already predicted tokens and current representation, and a feature fusion layer that enhances the representation with a feature fusion operation. The proposed method was evaluated on WMT17 datasets with 12 language pair groups. Experimental results show the effectiveness of the proposed method and also demonstrated that the proposed gated cumulative attention network has adequate ability as an alternative to the self-attention part in the Transformer decoder.
CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening

In this paper real-time people detection is demonstrated in a relatively large indoor industrial robot cell as well as in an outdoor environment.Six depth sensors mounted at the ceiling are used to generate a merged point cloud of the cell.The merged point cloud is segmented into clusters and flattened into gray-scale 2D images in the xy and xz planes.These images are then used as input to a classifier based on convolutional neural networks (CNNs).The final output is the 3D position (x, y, z) and bounding box representing the human.The system is able to detect and track multiple humans in real-time, both indoors and outdoors.The positional accuracy of the proposed method has been verified against several ground truth positions, and was found to be within the point-cloud voxel-size used, i.e. 0.04 m.Tests on outdoor datasets yielded a detection recall of 76.9 % and an F1 score of 0.87.
A neural cryptography approach for digital image security using Vigenère cipher and tree parity machine

Abstract As with other form of digital media, image can also contain private information that should be kept secret. Commonly, symmetric and asymmetric cryptosystem can be used to secure an image. Symmetric-key cryptography is not a best solution as the secret key has to be transmitted through private channel. Asymmetric-key cryptosystem, however, allows transmission of key through public channel, but requires high computational power and long computational time. This paper proposes a neural cryptography approach to protect image by using tree parity machine and Vigenère cipher. By using this approach, the secret key will never be transmitted. Moreover, the whole process does not require high computational power and long computational time as in public-key cryptosystem. To overcome the weakness of Vigenère cipher, change is made to the algorithm by multiplying the key value with y-axis of image. The effectiveness is validated by the value of pearson correlation that is close to 0. Lastly, the result of experiments show neural synchronization time depends on the size of network, initial weights and randomly generated input vector.
Posterior Transfer Learning with Active Sampling

Transfer learning has achieved great success on the challenge of learning with insufficient labeled data, mainly by exploiting the knowledge from some other related domains. Common transfer learning methods require to access the data or pre-trained models from source domains, and thus are less applicable to tasks with privacy or security requirements. In this paper, we propose a novel strategy to reuse the posterior probabilities from source domains without data sharing. On one hand, the distribution of posterior probability is regularized by utilizing the source predications as well as limited target data; on the other hand, a sampling strategy based on distribution difference is designed to actively select the most valuable instances for label querying. Experiments on multiple datasets demonstrate that the proposed method can achieve effective performances and requires less labeled data.
An Efficient Approach to Point-Counting on Elliptic Curves from a Prominent Family over the Prime Field Fp

Here, we elaborate an approach for determining the number of points on elliptic curves from the family Ep={Ea:y2=x3+a(modp),a≠0}, where p is a prime number &gt;3. The essence of this approach consists in combining the well-known Hasse bound with an explicit formula for the quantities of interest-reduced modulo p. It allows to advance an efficient technique to compute the six cardinalities associated with the family Ep, for p≡1(mod3), whose complexity is O˜(log2p), thus improving the best-known algorithmic solution with almost an order of magnitude.
A Practical Blockchain Framework for Securing IoT Applications

The Internet of things (IoT) has become a guiding technology behind automation and intelligent computing. One of the main problems of Internet of things system is the lack of privacy and security protection scheme to control access and ensure data security. Most of the security problems are caused by the centralized architecture of the Internet of things system. Another problem is the lack of appropriate authentication and access control schemes to restrict access to information generated by IOT devices. The IoT applications face many security challenges, which can be solved by blockchain. We have initially designed a general system model, which can gradually refine and form a secure IOT solution based on blockchain.
Improving the Transferability of Adversarial Examples with Image Affine Transformation

Abstract Deep learning is widely regarded as a black-box technology. We all know its performance is very good, but we have limited understanding of why it is so good. At present, there are many researches on the interpretability of deep neural network. By studying adversarial example, we can understand the internal semantics of neural network and find the decision boundary with problems, which in turn helps to improve the robustness and performance of neural network and its interpretability. With the development of adversarial example research, more and more adversarial example generation methods are proposed. Although the attack from adversarial example poses a great security threat to the deep learning system, it can also be used as an effective tool to measure the robustness and reliability of the model, and the attack and defense are two mutually promoting processes. Therefore, how to generate adversarial example with stronger attack ability is worth further study. And this study proposes a method named Affine-Invariant Method, aimed to improve the transferability of adversarial examples in black-box environment.
Mining Similar Words with Similarity Ranking

Word2vec is an effective model for learning a low-dimensional and compact representation of words. However, for mining similar words, the word2vec-based word vectors can lead to the variable similarities between words, thus the unstable similar words. This problem is particularly evident in specific corpus. To this end, a novel similarity ranking algorithm is introduced in this paper. The proposed algorithm constructs a probability transition matrix by calculating the similarities between words, then the stable similar vector of target word is estimated iteratively. The similar words of target word are extracted from the similarity ranking. The experimental results demonstrate that the proposed algorithm can effectively mine the unstable similar words.
Cross-Domain Variational Capsules for Information Extraction

In this paper, we present a characteristic extraction algorithm and the Multi-domain Image Characteristics Dataset of characteristic-tagged images to simulate the way a human brain classifies cross-domain information and generates insight. The intent was to identify prominent characteristics in data and use this identification mechanism to auto-generate insight from data in other unseen domains. An information extraction algorithm is proposed which is a combination of Variational Autoencoders (VAEs) and Capsule Networks. Capsule Networks are used to decompose images into their individual features and VAEs are used to explore variations on these decomposed features. Thus, making the model robust in recognizing characteristics from variations of the data. A noteworthy point is that the algorithm uses efficient hierarchical decoding of data which helps in richer output interpretation. Noticing a dearth in the number of datasets that contain visible characteristics in images belonging to various domains, the Multi-domain Image Characteristics Dataset was created and made publicly available. It consists of thousands of images across three domains. This dataset was created with the intent of introducing a new benchmark for fine-grained characteristic recognition tasks in the future.