Reviewerly/exHarmony huggingface dataset:
Simulating and Mitigating Crosstalk

We describe an efficient and scalable framework for modeling crosstalk effects on quantum information processors. By applying optimal control techniques, we show how to tuneup arbitrary high-fidelity parallel operations on systems with substantial local and nonlocal crosstalk. Simulations show drastically lower error rates for a 2D square array of 100 superconducting transmon qubits. These results suggest that rather than striving to engineer away undesirable interactions during fabrication, we can largely mitigate their effects through careful characterization and control optimization.
Evolutionary Human-UAV Cooperation for Transmission Network Restoration

Power transmission networks are vulnerable to natural or man-made disasters, and it is of critical importance to efficiently restore damaged power supply in disaster-affected areas. A large-scale damaged transmission network can contain many faults that are initially uninspected/unlocated. Using unmanned aerial vehicles (UAVs) to inspect these faults can significantly improve the efficiency of subsequent restoration performed by human operators. Such a cooperative human-UAV scheduling problem is highly complex due to the correlation between UAV schedules and human-team schedules. In this article, we propose a cooperative evolutionary algorithm that simultaneously evolves two populations, one of UAV scheduling solutions (U-solutions) and the other of human-team scheduling solutions (H-solutions), which cooperate by determining a best matching U-solution for each H-solution and evaluating U-solutions based on a surrogate objective function that is iteratively improved by feedback from H-solutions. Our algorithm exhibits significant performance advantages over the state-of-the-arts on various test instances and an application to transmission network restoration in the 2017 Jiuzhaigou earthquake.
Data Encryption &amp; Decryption Using Steganography

Video steganography is a method that processes secure communication. When we see the history of steganography, it was hidden in many ways such as tablets covered with wax, & written on the stomachs of rabbits. Here in this paper, considering the video steganography methods to perform secure steganography communication. Many methods have been proposed for video steganography but they're no more different types of formats, secured, quality, of the results. So here propose secure steganography methods i.e. Secure base LSB method, Neural Networks & Fuzzy logic, and check their using PSNR and MSE data of the methods. That data-set has collected is from video streams. And the result was seen with the more formats, more security, quality of outputs, & accuracy values of PSNR & MSE which is better than other proposed methods.
A Novel Echo State Network and Its Application in Temperature Prediction of Exhaust Gas From Hot Blast Stove

Hot blast stove (HBS) provides hot air for the blast furnace and temperature prediction for its exhaust gas is of vital importance to control the process. In this article, a novel deep memory echo state network (DMESN) is proposed for temperature prediction. First, data preprocessing is performed, including outlier rejecting, missing data handling and lag time calculating, so that better dynamic characteristics of data set can be obtained. Then, in order to improve the prediction accuracy, an improved hidden layer structure is proposed, which consists of two parts: echo state formation and hidden state formation. Echo state formation is used in network starting phase. Aiming at the problem that information cannot be effectively retained, three gates are designed in the cell to obtain a stable echo state which will be used to initialize the hidden state. In this way, the hidden state can operate continuously and stably. Finally, comparing experiments were carried out to demonstrate the efficiency of the proposed method. The simulation results show that DMESN achieves excellent performance in terms of accuracy, stability, and learning speed.
Neural machine translation of low-resource languages using SMT phrase pair injection

Abstract Neural machine translation (NMT) has recently shown promising results on publicly available benchmark datasets and is being rapidly adopted in various production systems. However, it requires high-quality large-scale parallel corpus, and it is not always possible to have sufficiently large corpus as it requires time, money, and professionals. Hence, many existing large-scale parallel corpus are limited to the specific languages and domains. In this paper, we propose an effective approach to improve an NMT system in low-resource scenario without using any additional data. Our approach aims at augmenting the original training data by means of parallel phrases extracted from the original training data itself using a statistical machine translation (SMT) system. Our proposed approach is based on the gated recurrent unit (GRU) and transformer networks. We choose the Hindi–English, Hindi–Bengali datasets for Health, Tourism, and Judicial (only for Hindi–English) domains. We train our NMT models for 10 translation directions, each using only 5–23k parallel sentences. Experiments show the improvements in the range of 1.38–15.36 BiLingual Evaluation Understudy points over the baseline systems. Experiments show that transformer models perform better than GRU models in low-resource scenarios. In addition to that, we also find that our proposed method outperforms SMT—which is known to work better than the neural models in low-resource scenarios—for some translation directions. In order to further show the effectiveness of our proposed model, we also employ our approach to another interesting NMT task, for example, old-to-modern English translation, using a tiny parallel corpus of only 2.7K sentences. For this task, we use publicly available old-modern English text which is approximately 1000 years old. Evaluation for this task shows significant improvement over the baseline NMT.
A blockchainized privacy-preserving support vector machine classification on mobile crowd sensed data

The voluminous amount of data generated by individuals’ mobile sensors and wearable devices is considered of a great value for the benefits of patients and clinical research. Recent advances incorporating data mining and cloud computing have leveraged the great potential of these data. However, the introduction of such technologies in the process of mobile crowd sensed data mining and analytics could potentially lead to security and privacy concerns. Individuals and organizations are not able to share and collectively run computations on their private data captured by different sensors to infer any processes of common interest. Although solutions such as Secure Multiparty Computation (SMC) were laid decades ago, they are still perceived for theoretical interest only, so far. In this paper, we aim at bridging the gap between privacy-preserving data mining and its practice. To do so, we introduce a blockchain-based privacy-preserving SVM classification (BPPSVC) between mutually distrustful data owners. In BPPSVC, blockchain technology along with smart contracts underlay more realistic assumptions about the adversarial model. Our main focus is on investigating the immutability, security and the bookkeeping properties of the blockchain in preserving the privacy of an SVM classifier over horizontally distributed IoT data. To this end, we first propose the system architecture, adversary model and design goals of BPPSVC, then we describe the design details. Our security analysis indicates that the proposed system is secure and it provides fairness and protection against Denial of Service (DoS) attacks. We finally show the efficiency and feasibility of BPPSVC through rigorous experimental results.
Towards safe reinforcement-learning in industrial grid-warehousing

Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment transition dynamics into a predictive model. The transition dynamics describes the mapping from one state to another, conditioned on an action. If this model is accurate enough, the predictive model is sufficient to train agents for optimal behavior in real environments. This paper presents the Dreaming Variational Autoencoder (DVAE) for safely learning good policies with a significantly lower risk of catastrophes occurring during training. The algorithm combines variational autoencoders, risk-directed exploration, and curiosity to train deep-q networks inside ”dream” states. We introduce a novel environment, ASRS-Lab, for research in the safe learning of autonomous vehicles in grid-based warehousing. The work shows that the proposed algorithm has better sample efficiency with similar performance to novel model-free deep reinforcement learning algorithms while maintaining safety during training.
Post-Quantum Secure Boot

A secure boot protocol is fundamental to ensuring the integrity of the trusted computing base of a secure system. The use of digital signature algorithms (DSAs) based on traditional asymmetric cryptography, particularly for secure boot, leaves such systems vulnerable to the threat of quantum computers. This paper presents the first post-quantum secure boot solution, implemented fully as hardware for reasons of security and performance. In particular, this work uses the eXtended Merkle Signature Scheme (XMSS), a hash-based scheme that has been specified as an IETF RFC. The solution has been integrated into a secure SoC platform around RISC-V cores and evaluated on an FPGA and is shown to be orders of magnitude faster compared to corresponding hardware/software implementations and to compare competitively with a fully hardware elliptic curve DSA based solution.
Detecting Deepfake Videos using Attribution-Based Confidence Metric

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.
Improved students’ performance prediction for multi-class imbalanced problems using hybrid and ensemble approach in educational data mining

Abstract Among the problems raised in the data mining area, the class imbalance is a well-known issue that always occurs. Many researchers studied this issue in several fields using three commonly used techniques: sampling, ensemble, or cost-sensitive learning. However, such studies are still new in education domains. This problem always related to the quality of data that gives the most impact to form an accurate prediction result. Many previous studies focus on binary imbalance classification problems instead of the multi-class imbalance problem in education data. This study used 4413 student instances of two datasets; students’ information system and e-learning from the Faculty of Engineering in a Malaysia university for First Semester 2017/2018. Three sampling categories utilized in this study are oversampling techniques, undersampling techniques, and hybrid techniques. The research empirically analyzes five types of ensemble classifiers and seven sampling techniques. The experimental results show a hybrid technique ROS with AdaBoost produces the most excellent performance compared to the other benchmark techniques. SMOTEENN technique with ensembles classifiers consistently produces high results. This technique has great potential in improving the students’ performance prediction model.
PD otherwise will be pluriversal (or it won't be)

The following text proposes a narrative and argumentative path starting in the wetlands of the Caribbean coastal region of Colombia, where Orlando Fals Borda, more than 50 years ago, found himself in the search for appropriate tools and methods for studying the complex social situation of the communities living there. His development of PAR -a transformative and explicitly political research approach with communities [15]- represented a radical critique and novel proposal for research in the social sciences. Such proposal opens a path for us to explore some examples of knowledge and research (building knowledge) otherwise such as 'systematisation of experiences' 'epistemologies of the south' [8] and 'situated knowledges' [20], as well as some design initiatives, networks and platforms otherwise that have emerged in recent years, such as 'designs from the souths' [19], 'decolonising design' [36], 'depatriarchise design', and 'autonomous design' [12]. We then build on these proposals of knowledge and design otherwise, to explain how they have influenced and informed our intervention in a specific case study of northern-European PD, which we will present as a practical example of such ideas. Our journey will end with a plea for contemporary PD to incorporate the political nature of the origins of PAR and the more contemporary concept of 'pluriverse' [13], so as to better articulate –not only its investigative- but also its transformative qualities.
First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network

Picking the first arrival of microseismic signals, quickly and accurately, is the key for real-time data processing of microseismic monitoring. The traditional method cannot meet the high-accuracy and high-efficiency requirements for the firstarrival microseismic picking, in a low SNR environment. Concentrating on the problem of relatively low microseismic SNR, this paper proposes the Residual Link Nested U-Net Network (RLU-Net), which can not only retain the spatial position information of input signal and profile, but also realize the first arrival picking from end-to-end. The added number of layers with the residual block is important to extract more features and better distinguish the signal and noise in low SNR environments, which improves the accuracy and efficiency of first arrival picking. We also use the improved Wasserstein Generative Adversarial Network (WGAN) generated adversarial sample sets and conducted adversarial training on the network to enhance the generalization ability of the model. The resulting model then accurately and efficiently identified the first arrival of effective signals. The testing of forward modeling microseismic signals and measured microseismic data show that, compared with the Short Term Average/Long Term Average (STA/LTA) algorithm, the proposed algorithm not only has a high accuracy, but also can pick the first arrival time of microseismic signals in a low SNR environment.
Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot’s applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human–robot interactions.
Investing Data with Machine Learning Using Python

Data can be classified into various forms like structured, semi-structured and unstructured. Data is growing rapidly in exponential form. Not all the data is useful; percentage of useful data is to be identified for future use. From useful data, future is to be predicted by analyzing the past. There are various types of algorithms and tools available in market through which we can predict our future [9]. Through this paper, we shall try to analyze and predict stocks and investing data of various companies such as S&P 500 Index, standard and poor’s 500, Yahoo Finance, Google Finance through supervised and unsupervised machine learning algorithms. Machine learning plays vital role in today’s scenario from self-driven cars, Google Assistance and Siri to news recommendation systems. It has been incorporated into mainstream tools, news recommendation engines, sentiment analysis, stock screeners, etc.
Ancient Korean Neural Machine Translation

Translation of the languages of ancient times can serve as a source for the content of various digital media and can be helpful in various fields such as natural phenomena, medicine, and science.Owing to these needs, there has been a global movement to translate ancient languages, but expert minds are required for this purpose.It is difficult to train language experts, and more importantly, manual translation is a slow process.Consequently, the recovery of ancient characters using machine translation has been recently investigated, but there is currently no literature on the machine translation of ancient Korean.This paper proposes the first ancient Korean neural machine translation model using a Transformer.This model can improve the efficiency of a translator by quickly providing a draft translation for a number of untranslated ancient documents.Furthermore, a new subword tokenization method called the Share Vocabulary and Entity Restriction Byte Pair Encoding is proposed based on the characteristics of ancient Korean sentences.This proposed method yields an increase in the performance of the original conventional subword tokenization methods such as byte pair encoding by 5.25 BLEU points.In addition, various decoding strategies such as n-gram blocking and ensemble models further improve the performance by 2.89 BLEU points.The model has been made publicly available as a software application.
New Approach to the Implementation of Post-Quantum Digital Signature Scheme

This article is devoted to the analysis of possibilities of implementation of digital signature schemes based on using error-correcting codes. This approach allows cryptographers to build schemes that are resistant to both classic cryptanalysis and cryptanalysis which uses quantum computing. The article describes the principles of the classic digital signature scheme which is named CFS and built using a Niederreiter-like transform, and also we propose a new approach that enables an implementation of signature according to the McEliece transformations. This approach preserves the advantages of its predecessor and provides additional protection against special attacks. Also a comparative analysis and characterization of the considered schemes according to the criteria of resistance to classic and quantum cryptanalysis, complexity of necessary transformations and length of generated signatures are made.
Optimization of the Match‐Filtering Method for Robust Repeating Earthquake Detection: The Multisegment Cross‐Correlation Approach

Abstract Waveform match‐filtering (MF), based on cross‐correlation between an earthquake pair, is a powerful and widely used tool in seismology. However, its performance can be severely affected by several factors, including the length of the cross‐correlation window, the frequency band of the applied digital filter, and the presence of a large‐amplitude phase(s). To optimize the performance of MF, we first systematically examine the effects of different operational parameters and determine the generic rules for selecting the window length and the optimal frequency passband. To minimize the influence of a large‐amplitude phase(s), we then propose a new approach, namely, MF with multisegment cross‐correlation (MFMC). By equally incorporating the contributions from various segments of the waveforms, this new approach is much more sensitive to small separation between two sources compared to the conventional MF method using the entire waveform template. To compare the reliability and effectiveness of both methods in capturing interevent source separation and identifying repeating earthquakes, we systematically conduct experiments with both synthetic data and real observations. The results demonstrate that the conventional MF method can detect the existence of an event but sometimes lacks the resolution to tell whether the template and detected events are co‐located or not, whereas MFMC works in all cases. The far‐reaching implication from this study is that inferring source separation between an earthquake pair based on the conventional MF method, particularly with data from a single channel/station, may not be reliable.
Spatial-temporal saliency action mask attention network for action recognition

Recently, video action recognition about two-stream network is still a popular research topic in computer vision. However, most of current two-stream-based methods have two redundancy issues, including: inter-frame redundancy and intra-frame redundancy. To solve the above problems, a Spatial-Temporal Saliency Action Mask Attention network (STSAMANet) is built for action recognition. First, this paper introduces a key-frame mechanism to eliminate inter-frame redundancy. This mechanism can compute key frames on each video sequence to get the greatest difference between frames. Then, Mask R-CNN detection technology is introduced to build a saliency attention layer to eliminate intra-frame redundancy. This layer is to focus on the saliency human body and objects for each action class. We experiment on two public video action datasets, i.e., the UCF101 dataset and Penn Action dataset to verify the effectiveness of our method in action recognition.