Reviewerly/exHarmony huggingface dataset:
Quantum Markov Chains: Recurrence, Schur Functions and Splitting Rules

In this work, we study the recurrence problem for quantum Markov chains, which are quantum versions of classical Markov chains introduced by S. Gudder and described in terms of completely positive maps. A notion of monitored recurrence for quantum Markov chains is examined in association with Schur functions, which codify information on the first return to some given state or subspace. Such objects possess important factorization and decomposition properties which allow us to obtain probabilistic results based solely on those parts of the graph where the dynamics takes place, the so-called splitting rules. These rules also yield an alternative to the folding trick to transform a doubly infinite system into a semi-infinite one which doubles the number of internal degrees of freedom. The generalization of Schur functions—so-called FR-functions—to the general context of closed operators in Banach spaces is the key for the present applications to open quantum systems. An important class of examples included in this setting are the open quantum random walks, as described by S. Attal et al., but we will state results in terms of general completely positive trace-preserving maps. We also take the opportunity to discuss basic results on recurrence of finite- dimensional iterated quantum channels and quantum versions of Kac's Lemma, in close association with recent results on the subject.
AN EXPLICIT TWO‐SOURCE EXTRACTOR WITH MIN‐ENTROPY RATE NEAR

MathematikaVolume 65, Issue 4 p. 950-957 Research Article AN EXPLICIT TWO-SOURCE EXTRACTOR WITH MIN-ENTROPY RATE NEAR Mark Lewko, Mark Lewko New York, NY, 10017 USASearch for more papers by this author Mark Lewko, Mark Lewko New York, NY, 10017 USASearch for more papers by this author First published: 24 July 2019 https://doi.org/10.1112/S0025579319000238Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract In 2005 Bourgain gave the first explicit construction of a two-source extractor family with min-entropy rate less than 1/2. His approach combined Fourier analysis with innovative but inefficient tools from arithmetic combinatorics and yielded an unspecified min-entropy rate which was greater than 0.499. This remained essentially the state of the art until a 2015 breakthrough by Chattopadhyay and Zuckerman in which they gave an alternative approach which produced extractors with arbitrarily small min-entropy rate. In the current work, we revisit the Fourier analytic approach. We give an improved analysis of one of Bourgain's extractors which shows that it in fact extracts from sources with min-entropy rate near Moreover, we construct a variant of this extractor which we show extracts from sources with min-entropy rate near While this min-entropy rate is inferior to Chattopadhyay and Zuckerman's construction, our extractors have the advantage of exponential small error, which is important in some applications. The key ingredient in these arguments is recent progress connected to the restriction theory of the finite field paraboloid by Rudnev and Shkredov. This in turn relies on a Rudnev's point–plane incidence estimate, which in turn relies on Kollár's generalization of the Guth–Katz incidence theorem. Citing Literature Volume65, Issue42019Pages 950-957 RelatedInformation
Saliency-Driven Active Contour Model for Image Segmentation

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.
Translating cuneiform symbols using artificial neural network

Cuneiform language is an old language that was invented by the people of Sumerian nation. It is an essential language for many archeologists. Especially who are interested in studying and investigating the old nations of Iraq. Dealing with this type of language usually requires specialist to translate its symbols, which are basically forms of nail shapes. This study presents a new approach to translate the cuneiform writing by employing artificial neural network (ANN) technique. Effectively, multi-layer perceptron (MLP) neural network has been adapted for translating the Sumerian cuneiform symbol images to their corresponding English letters. This work has been successfully established and it attained 100%.
DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system.
Side-Channel Analysis of the Xilinx Zynq UltraScale+ Encryption Engine

The Xilinx Zynq UltraScale+ (ZU+) is a powerful and flexible System-on- Chip (SoC) computing platform for next generation applications such as autonomous driving or industrial Internet-of-Things (IoT) based on 16 nm production technology. The devices are equipped with a secure boot mechanism in order to provide confidentiality, integrity, and authenticity of the configuration files that are loaded during power-up. This includes a dedicated encryption engine which features a protocol-based countermeasure against passive Side-Channel Attacks (SCAs) called key rolling. The mechanism ensures that the same key is used only for a certain number of data blocks that has to be defined by the user. However, a suitable choice for the key rolling parameter depends on the power leakage behavior of the chip and is not published by the manufacturer. To close this gap, this paper presents the first publicly known side-channel analysis of the ZU+ encryption unit. We conduct a black-box reverse engineering of the internal hardware architecture of the encryption engine using Electromagnetic (EM) measurements from a decoupling capacitor of the power supply. Then, we illustrate a sophisticated methodology that involves the first five rounds of an AES encryption to attack the 256-bit secret key. We apply the elaborated attack strategy using several new Deep Learning (DL)-based evaluation methods for cryptographic implementations. Even though we are unable to recover all bytes of the secret key, the experimental results still allow us to provide concrete recommendations for the key rolling parameter under realistic conditions. This eventually helps to configure the secure boot mechanism of the ZU+ and similar devices appropriately.
Universal Constraint for Relaxation Rates for Quantum Dynamical Semigroup

A conjecture for the universal constraints for relaxation rates of a quantum dynamical semigroup is proposed. It is shown that it holds for several interesting classes of semigroups, e.g. unital semigroups and semigroups derived in the weak coupling limit from the proper microscopic model. Moreover, proposed conjecture is supported by numerical analysis. This conjecture has several important implications: it allows to provide universal constraints for spectra of quantum channels and provides necessary condition to decide whether a given channel is consistent with Markovian evolution.
Explainable AI: Using Shapley Value to Explain Complex Anomaly Detection ML-Based Systems

Generally, Artificial Intelligence (AI) algorithms are unable to account for the logic of each decision they take during the course of arriving at a solution. This “black box” problem limits the usefulness of AI in military, medical, and financial security applications, among others, where the price for a mistake is great and the decision-maker must be able to monitor and understand each step along the process. In our research, we focus on the application of Explainable AI for log anomaly detection systems of a different kind. In particular, we use the Shapley value approach from cooperative game theory to explain the outcome or solution of two anomaly-detection algorithms: Decision tree and DeepLog. Both algorithms come from the machine learning-based log analysis toolkit for the automated anomaly detection “Loglizer”. The novelty of our research is that by using the Shapley value and special coding techniques we managed to evaluate or explain the contribution of both a single event and a grouped sequence of events of the Log for the purposes of anomaly detection. We explain how each event and sequence of events influences the solution, or the result, of an anomaly detection system.
The effect of transformation leadership on government employee job satisfaction during Covid-19

The recent study aims to examine the impact of transformational leadership on government employees’ job satisfaction in Jordan. The researchers selected a sample of n= 52 Human Resource employees from public sector organization by using a convenient sampling method. Structural equational modeling also helped to validate the proposed relationship between study variables. Findings revealed a strong positive relationship between transformational leadership and job satisfaction among the HR employees. As found, despite Covid-19, transformational leaders help employees work effectively and increase employee’s job satisfaction and organizational commitment. By keeping in view, the results of the present investigation consider transformational leadership as highly capable tool for dealing with organizational crisis and management. The researchers therefore suggest further studies to illustrate the role of leadership in strategically following administrative procedures during major social and health crisis situations.
Content-Based Image Copy Detection Using Convolutional Neural Network

With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.
Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.
Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision

Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance. All the previous works model and predict the trajectory with a single resolution, which is relatively ineffective and difficult to simultaneously exploit the long-range information (e.g., the destination of the trajectory), and the short-range information (e.g., the walking direction and speed at a certain time) of the motion behavior. In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. Our hierarchical framework builds a feature pyramid with increasingly richer temporal information from top to bottom, which can better capture the motion behavior at various tempos. Furthermore, we propose a coarse-to-fine fusion strategy with multi-supervision. By progressively merging the top coarse features of global context to the bottom fine features of rich local context, our method can fully exploit both the long-range and short-range information of the trajectory. Experimental results on two benchmarks demonstrate the superiority of our method. Our code and models will be available upon acceptance.
Remote study process during Covid-19

The higher education in Latvia became remote in the spring of 2020 due to the pandemic of Covid-19. Bearing in mind the probability of reoccurrence of a similar crisis, the aim of the study is to establish the self-evaluation of digital communication and collaboration skills of Latvian higher education students, and to detect the difficulties experienced during the remote study process. Data was collected through an online survey. Study shows that although the self-assessed level of competence of digital communication and collaboration skills is high, the use of skills is not as frequent, indicating that remote study process is not equivalent to on-site education. The academic staff is advised to vary teaching methods to stimulate use of different skills. Keywords: Collaboration skills; Communication skills; Digital skills; Higher education; Remote studying Keywords: POE, high school students, states of matters, worksheets.
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks

Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training. In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms. In this paper, we define a robust, reproducible and systematic experimental procedure to compare the performance of various model-free algorithms at solving this task. The policies are trained in simulation and are then transferred to a physical robotic manipulator. It is shown that augmenting the reward signal with the Hindsight Experience Replay exploration technique increases the average return of off-policy agents between 7 and 9 folds when the target position is initialised randomly at the beginning of each episode.
A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field

As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification.However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text's context and blindness to the topic of the corpus.This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis.A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered.To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study.The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates.
Penerapan Metode Pembelajaran Daring (Online) melalui Aplikasi Berbasis Android saat Pandemi Global

This study is based on the global pandemic experienced throughout the world, especially Indonesia. The presence of the Covid-19 Virus pandemic has an impact on various fields, one of which is education. In Indonesian education, for 14 days, even more, the whole teaching and learning activities were suspended, and the implementation of the National Examination was also postponed. Changes in teaching methods must also be made online. Many social media applications that can be used, but not all of these applications have a feature and appropriate facilities and attractive to be used as teaching materials to the students. This study used quantitative and qualitative methods. 30 students were chosen randomly at one of the private universities in Jakarta. Collecting and analyzing data directly from respondents through the questionnaire is a quantitative method that the author used. While, qualitative methods used was library research techniques to support the results of collecting data through questionnaires on the use of applications based on Android. This study is expected to present a selection of the proper application to assist the learning process so that it has implications on improving the students' understanding and activities.
Classification of Imbalanced Datasets using One-Class SVM, k-Nearest Neighbors and CART Algorithm

In this paper a new algorithm, OKC classifier is proposed that is a hybrid of One-Class SVM, k-Nearest Neighbours and CART algorithms.The performance of most of the classification algorithms is significantly influenced by certain characteristics of datasets on which these are modeled such as imbalance in class distribution, class overlapping, lack of density, etc.The proposed algorithm can perform the classification task on imbalanced datasets without re-sampling.This algorithm is compared against a few well known classification algorithms and on datasets having varying degrees of class imbalance and class overlap.The experimental results demonstrate that the proposed algorithm has performed better than a number of standard classification algorithms.
C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer

Human video motion transfer (HVMT) aims to synthesize videos that one person imitates other persons' actions. Although existing GAN-based HVMT methods have achieved great success, they either fail to preserve appearance details due to the loss of spatial consistency between synthesized and exemplary images, or generate incoherent video results due to the lack of temporal consistency among video frames. In this paper, we propose Coarse-to-Fine Flow Warping Network (C2F-FWN) for spatial-temporal consistent HVMT. Particularly, C2F-FWN utilizes coarse-to-fine flow warping and Layout-Constrained Deformable Convolution (LC-DConv) to improve spatial consistency, and employs Flow Temporal Consistency (FTC) Loss to enhance temporal consistency. In addition, provided with multi-source appearance inputs, C2F-FWN can support appearance attribute editing with great flexibility and efficiency. Besides public datasets, we also collected a large-scale HVMT dataset named SoloDance for evaluation. Extensive experiments conducted on our SoloDance dataset and the iPER dataset show that our approach outperforms state-of-art HVMT methods in terms of both spatial and temporal consistency. Source code and the SoloDance dataset are available at https://github.com/wswdx/C2F-FWN.
A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

In the current era, the implementation of automated security video surveillance systems is particularly needy in terms of human violence recognition. Nevertheless, the latter encounters various interlinked difficulties which require efficient solutions as well as feasible methods that provide a relevant distinction between normal human actions and abnormal ones. In this paper, we present an overview of these issues and a literature review of the related works and current research on-going efforts on this field and suggests a novel prediction model for violence recognition, based on a preliminary spatio-temporal features extraction using the material derivative which describes the rate of change of a particle while in motion with respect to time. The classification algorithm is then carried out using a deep learning LSTM technique to classify generated features into eight specified violent and non-violent categories and a prediction value for each class of action is calculated. The whole model is trained on a public dataset and its classification capacity is evaluated on a confusion matrix which assembles all the predictions made by the system with their actual labels. The obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.