Reviewerly/exHarmony huggingface dataset:
Strategies for scientific popularization in the digital media environment

The proposed article is devoted to the analysis of the characteristic features of popular science communication in the digital media environment. The current models of scientific communication are analyzed, the key trends of modern society are identified — mediatization and digitalization, which set the specifics of scientific popularization today. The mediatized forms of science communication testify to the unfolding process of mediatization of science — the growing involvement of media communications in the implementation of scientific activities. The mediatization of science also has its own specifics, which, in particular, is manifested in the growing convergence of the spheres of science and politics. Such politicization of science sets the features of mediatization, which covers the field of scientific activity very unevenly. Those areas of scientific knowledge that are politically in demand are subjected to mediatization to a greater extent. The politicization of science, which is unfolding along with mediatization, makes it possible to identify a similarity between political and scientific communications. The digital media environment of science communications is largely built in a similar way with the space of political communications. It is characterized by polarization and differentiation of the audience. The acceptance of scientific communication is largely determined not by the quality of scientific content, but by the cognitive and value attitudes of users. The principal feature of digital media architecture is its dual nature, which allows it to be equally used both for the dissemination of scientific information and its refutation and approval of false information. Under these conditions, strategies for effective science communication may include measures aimed at increasing the media literacy of the audience, the flexible use of media framing of scientific knowledge, aimed at expanding the audience of science communication and overcoming prejudices regarding new scientific achievements.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
Machine Learning for Pharmacokinetic/Pharmacodynamic Modeling

A variety of new recurrent neural networks (RNNs) including the ODE-LSTM, Phased LSTM, CTGRU and GRU-D, were evaluated on modeling irregularly sampled PK/PD data with 6 or 12 time points/day and predicting PD data of unseen dosing regimens and dosing levels. The one-compartment absorption PK model and the Indirect PK/PD model I was used to simulate the PK/PD with inter-individual variabilities in volume of distribution and residual errors in PD measurement. The four RNNs were able to successfully model daily dose (QD) PK/PD and extrapolate to twice daily (BID) dose PD based on BID PK. The RNNs not only captured the additional fluctuations in the BID regimen but also the return phase to the baseline PD. However, extrapolating to unseen dose levels outside of the dose range for training proved to be challenging for all the RNNs tested. Only the GRUD demonstrated reasonable prediction results when extrapolating to unseen doses that were 3 or 10-fold outside the training doses. Overall, these new RNNs were able to overcome some limitations of previous RNNs evaluated and showed promise of integrating neural networks in PK/PD.
Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference

nnU-Net serves as a good baseline for many medical image segmentation challenges in recent years. It works pretty well for fully-supervised segmentation tasks. However, it is less efficient for inference and cannot effectively make full use of unlabeled data, both of which are vital in real clinical scenarios. To this end, we revisit nnU-Net and find the trade-off between efficiency and accuracy in this framework. Based on the default nnU-Net settings, we design a co-training framework consisting of two strategies to generate high-quality pseudo labels and make efficient inference respectively. Specifically, we first design a resource-intensive nnU-Net to iteratively generate high-quality pseudo labels for unlabeled data. Then we train another light-weight 3D nnU-Net using labeled data and selected unlabeled data, with high-quality pseudo labels used for the latter to achieve efficient segmentation. We conduct experiments on the FLARE22 challenge. Our resource-intensive nnU-Net achieves the mean DSC of 0.9064 on 13 abdominal organ segmentation tasks and ranks first on the validation leaderboard. Our light-weight nnU-Net shows the mean DSC of 0.8773 on the validation leaderboard but it makes a better trade-off between accuracy and efficiency. On the test set, it shows the mean DSC of 0.8864, the mean NSD of 0.9465, and the average inference time of 14.59s and wins the championship of the FLARE22 challenge. Our code is publicly available at https://github.com/Ziyan-Huang/FLARE22 .
An Ultra-Low Power TinyML System for Real-Time Visual Processing at Edge

Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models for various visual tasks. Then, a specially designed neural co-processor (NCP) is interconnected with MCU to build an ultra-low power TinyML system, which stores all features and weights on chip and completely removes both of latency and power consumption in off-chip memory access. Moreover, an application specific instruction-set is further presented for realizing agile development and rapid deployment. Extensive experiments demonstrate that the proposed TinyML system based on our tiny model, NCP and instruction set yields considerable accuracy and achieves a record ultra-low power of 160mW while implementing object detection and recognition at 30FPS. The demo video is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/watch?v=mIZPxtJ-9EY</uri> .
Deep-Learning-Aided RF Fingerprinting for NFC Relay Attack Detection

Near field communication (NFC) has been a widely used radiofrequency identification (RFID) technology, credited to its convenience and security features. However, the transmitted signals can be easily eavesdropped or relayed in an open wireless channel. One of the challenges is relay attack, where an attacker simply relays the signal and bypasses encryption or other means in the application layer. Prior works on relay attack countermeasures have focused on distance-bounding protocols or ambient-based solutions. This paper focuses on ISO/IEC 14443-A and proposes an NFC relay detection method based on RF fingerprinting of transmitted wireless signals in the physical layer. To this end, we first designed and implemented two realizations of NFC relay attacks, wired and wireless relays, and built an SDR-based testbed. We collected the normal and relayed signals of four NFC tags, and the answer to request type A (ATQA) segments were selected for RF fingerprinting. The created dataset comprised 66,366 samples, with four tags’ normal and wired relayed signals and the wireless relayed signals. The dataset was then fed into a deep CNN for training. Finally, our experiment results showed that the method effectively distinguished normal and relayed signals with a high accuracy of 99%, confirming that RF fingerprinting can be a promising countermeasure to NFC relay attacks.
An Object-Oriented Agent Framework for SCADA

The future of factory automation is certainly Industry 4.0, where SCADA software is its essential element. A more advanced SCADA software, namely “intelligent SCADA,” is needed to be developed to support Industry 4.0. In this paper, we shall investigate an intelligent SCADA software by proposing an object-oriented agent framework for it. The proposed framework is modeled entirely using the object-oriented approach; with its merit, we can represent and reason about machines’ state and change, and we can also model and reason about object language and metalanguage of what we call “a cyber-physical system.” Moreover, we gain a great deal of benefit from object-oriented concepts for this intelligent SCADA, such as inheritance, polymorphism, persistence, object-oriented design patterns and so on. We shall argue that our approach captures the notion of a cyber-physical system and a digital twin naturally as well as embraces logic fundamentally. The framework was developed, as a web-based SCADA application, using Python on the server side and JavaScript together with HTML on the client side.
Log-$$\mathcal {S}$$-unit Lattices Using Explicit Stickelberger Generators to Solve Approx Ideal-SVP

In 2020, Bernard and Roux-Langlois introduced the Twisted-PHS algorithm to solve Approx-Svp for ideal lattices on any number field, based on the PHS algorithm by Pellet-Mary, Hanrot and Stehlé. They performed experiments for prime conductors cyclotomic fields of degrees at most 70, one of the main bottlenecks being the computation of a log- $$\mathcal {S}$$ -unit lattice which requires subexponential time. Our main contribution is to extend these experiments to cyclotomic fields of degree up to 210 for most conductors m. Building upon new results from Bernard and Kučera on the Stickelberger ideal, we use explicit generators to construct full-rank log- $$\mathcal {S}$$ -unit sublattices fulfilling the role of approximating the full Twisted-PHS lattice. In our best approximate regime, our results show that the Twisted-PHS algorithm outperforms, over our experimental range, the CDW algorithm by Cramer, Ducas and Wesolowski, and sometimes beats its asymptotic volumetric lower bound. Additionally, we use these explicit Stickelberger generators to remove almost all quantum steps in the CDW algorithm, under the mild restriction that the plus part of the class number verifies $$h_{m}^{+}\le O(\sqrt{m})$$ .
Compact and Tightly Selective-Opening Secure Public-key Encryption Schemes

We propose four public-key encryption schemes with tight simulation-based selective-opening security against chosen-ciphertext attacks (SIM-SO-CCA) in the random oracle model. Our schemes only consist of small constant amounts of group elements in the ciphertext, ignoring smaller contributions from symmetric-key encryption, namely, they have compact ciphertexts. Furthermore, three of our schemes have compact public keys as well. Known (almost) tightly SIM-SO-CCA secure PKE schemes are due to the work of Lyu et al. (PKC 2018) and Libert et al. (Crypto 2017). They have either linear-size ciphertexts or linear-size public keys. Moreover, they only achieve almost tightness, namely, with security loss depending on the security parameters. Different to them, our schemes are the first ones achieving both tight SIM-SO-CCA security and compactness. Our schemes can be divided into two families:
BLOOM: Bimodal Lattice One-out-of-Many Proofs and Applications

We give a construction of an efficient one-out-of-many proof system, in which a prover shows that he knows the pre-image for one element in a set, based on the hardness of lattice problems. The construction employs the recent zero-knowledge framework of Lyubashevsky et al. (Crypto 2022) together with an improved, over prior lattice-based one-out-of-many proofs, recursive procedure, and a novel rejection sampling proof that allows to use the efficient bimodal rejection sampling throughout the protocol. Using these new primitives and techniques, we give instantiations of the most compact lattice-based ring and group signatures schemes. The improvement in signature sizes over prior works ranges between $$25\%$$ and 2X. Perhaps of even more significance, the size of the user public keys, which need to be stored somewhere publicly accessible in order for ring signatures to be meaningful, is reduced by factors ranging from 7X to 15X. In what could be of independent interest, we also provide noticeably improved proofs for integer relations which, together with one-out-of-many proofs are key components of confidential payment systems.
Prevention of GAN-Based Privacy Inferring Attacks Towards Federated Learning

With the increasing amount of data, data privacy has drawn great concern in machine learning among the public. Federated Learning, which is a new kind of distributed learning framework, enables data providers to train models locally to protect privacy. It solves the problem of privacy leakage of data by enabling multiple parties, each with their training dataset, to share the model instead of exchanging private data with the server side. However, there are still threats of data privacy leakage in federated learning. In this work, we are motivated to prevent GAN-based privacy inferring attacks in federated learning. For the GAN-based privacy inferring attacks, inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the privacy information of victims while maintaining the accuracy of the global model. Comprehensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks.
Fault detection method based on adversarial reinforcement learning

Fault detection is an essential task for large-scale industrial maintenance. However, in practical applications, due to the possible harm caused by the collection of fault data, the fault samples that lead to the labeling are usually very few. Most existing methods consider training unsupervised models with a large amount of unlabeled data while ignoring the rich knowledge that existed in a small amount of labeled data. To make full use of this prior knowledge, this article proposes a reinforcement learning model, namely, adversarial reinforcement learning in weakly supervised (WS-ARL), which performs significantly better by jointly learning small labeled anomaly data and large unlabeled data. We use an agent of the reinforcement learning model as a fault detector and add a new environment agent as a sample selector, by providing an opposite reward for two agents, and they learn in an adversarial environment. The feasibility and effectiveness of the model are verified by experimental analysis and compared the performance of the model with five state-of-the-art weakly/un-supervised methods in the hydraulic press fault detection task.
Wheeled Mobile Robot Path Planning and Path Tracking in a static environment using TLBO AND PID- TLBO control

the autonomous mobile robot has gained an increasing interest, namely its application in several fields, especially transportation. A robot may transport a hazardous materials in an environment cluttered with an obstacles hence the design of a safest path is an urgent requirement for the success of mission. Our study aims to introduce Teaching Learning based optimization (TLBO) to solve path planning of robot mobile. The proposed approach is based on meta-heuristic optimization which address the length and safety objectives. Furthermore the applied algorithm is devoted to find out the PID parameters for the path tracking of the obtained trajectory. The obtained results of simulation proves the efficiently of the implemented algorithm for finding the optimal path and the best parameters of PID controller for Path Tracking.
Path Planning of Robots Based on an Improved A-star Algorithm

In the path planning of mobile robot moving from its own position to the target point, the traditional A-star algorithm is widely used as the most popular path planning algorithm in robots. However, there are many redundant inflection points in the path planned by the traditional algorithm, and the path length is long. The large peak path at the inflection point is not smooth enough, which leads to the problem of path safety. To solve the above problems, an improved A-star algorithm is proposed. Firstly, the evaluation function of the traditional algorithm is improved, and the weights of different regions are added, so that the path planned by the improved algorithm is shorter and the efficiency is higher. Secondly, the redundant points in the generated path are optimized to further shorten the path length. Finally, the tangent method is used to make the overall path smoother at the inflection point, which improves the safety and stability. Finally, the simulation experiment is carried out in matlab to verify the effectiveness of the algorithm. The simulation results show that the improved algorithm shortens the path length by 4.68 %, reduces the number of inflection points, and the path at the inflection point is smoother. Optimized the path.
Transformer-based comparative multi-view illegal transaction detection

In recent years, as the Ether platform has grown by leaps and bounds. Numerous unscrupulous individuals have used illegal transaction to defraud large sums of money, causing billions of dollars of losses to investors worldwide. Facing the endless stream of the illegal transaction based on Ether smart contracts problems, such as illegal transaction, money laundering, financial fraud, phishing. Currently, illegal transaction are only detected by a single view of the smart contract’s contract code view feature and account transaction view feature, which is not only incomplete, but also not fully representative of the smart contract’s features. More importantly, the single view detection model cannot accurately capture the global structure and semantic features between the Tokens of the view features. In this case, it is particularly important that all view features are shared among themselves. In this paper, we investigate a Transformer-based model for contrasting illegal transaction detection networks under multiple views (TranMulti-View Net). The model in this paper is based on Transformer to learn a multi-view fusion representation, which aims to maximise the fusion of the interaction information of different view features under the same condition. In this model we first use the Transformer model to learn global structure and semantic features from a sequence of Tokens tokenised by a view, capturing the remote dependencies of Tokens in the view features, and then we share the contract code view features and the account transaction view features across all views to learn important semantic information between views from each other. In addition, we find that the approach of semi-supervised training of multi-view features using contrast learning outperforms the scheme of prediction based on direct fusion of different view features, resulting in stronger correlation between view features. As a result, the underlying semantic information can be captured more accurately, leading to more accurate predictions of illegal transaction. The experimental results show that our proposed TranMulti-View Net obtains good detection results with a Precision score of 98%.
Combining OCR Methods to Improve Handwritten Text Recognition with Low System Technical Requirements

Using a combination of CRNN (Convolutional Recurrent Neural Network), LSTM (long short-term memory) and CTC (Connectionist temporal classification) in order to build an ensemble of methods, the results of which are on a par with popular modern methods, while maintaining the simplicity of the algorithm. The methods are described in detail, and a brief analysis of the IAM dataset is performed. The results of other authors are shown to determine the efficiency of using an ensemble of these technologies. The effectiveness of using CRNN in comparison with the separate use of CNN/RNN and popular ERN/HMM/LeNet methods has been proven (accuracy is ~15% better). This set of methods is capable of solving the problems of finding and deciphering handwritten texts, and at the same time, the ensemble does not require high power, which allows it to be used on fairly simple machines.
Rivain-Prouff on Steroids: Faster and Stronger Masking of the AES

At CHES 2010, Rivain and Prouff (RP) introduced an elegant masking technique to protect the Advanced Encryption Standard (AES) against power analysis attacks. RP masking is provable secure in the probing model, but this solid theoretical underpinning comes at the cost of a massive increase in execution time. In this paper, we describe software optimization methods to accelerate the low-level arithmetic in the field $$\mathbb {F}_{2^8}$$ , which has a significant impact on the overall performance of a masked implementation of the AES. Among these optimizations is an improved technique for table-based multiplication in $$\mathbb {F}_{2^8}$$ that allows one to avoid the special treatment of 0-values, thereby speeding up the multiplication of masked operands. Furthermore, we introduce a novel exponentiation-based algorithm for inversion in $$\mathbb {F}_{2^8}$$ , which reduces the overall number of table look-ups and the amount of randomness needed for the refreshing of masks compared to the original RP inversion. This new inversion provides some advanced (theoretical) security properties for the composition of gadgets, e.g. Strong Non-Interference (SNI) and Probe Isolating Non-Interference (PINI). We also describe a prototype implementation of a first-order masked inversion and AES encryption in ARMv7-M Assembly language. According to our simulation results, the first-order masked AES has an execution time of about 25k clock cycles per block when using a generic Cortex-M3 as target platform, which is roughly twice as fast as the RP-masked AES Assembly implementation presented at EUROCRYPT 2017 by Goudarzi and Rivain.