Reviewerly/exHarmony huggingface dataset:
Non-Asymptotic Entanglement Distillation

Entanglement distillation, an essential quantum information processing task, refers to the conversion from multiple copies of noisy entangled states to a smaller number of highly entangled states. In this paper, we study the non-asymptotic fundamental limits for entanglement distillation. We investigate the optimal tradeoff between the distillation rate, the number of prepared states, and the error tolerance. First, we derive the one-shot distillable entanglement under completely positive partial transpose preserving operations as a semidefinite program and demonstrate an exact characterization via the quantum hypothesis testing relative entropy. Second, we establish efficiently computable second-order estimations of the distillation rate for general quantum states. In particular, we provide explicit as well as approximate evaluations for various quantum states of practical interest, including pure states, mixture of Bell states, maximally correlated states, and isotropic states.
Spectral embedded generalized mean based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e6983" altimg="si267.svg"><mml:mi>k</mml:mi></mml:math>-nearest neighbors clustering with S-distance

The spectral clustering algorithm is extensively employed in different aspects, especially in the field of pattern recognition. However, the efficient construction of the neighborhood graph is the main reason for its promising results. Generally, the similarity matrix relies on the applied similarity measure between two data points, selection of k−nearest neighbors (KNN), and approach for the construction of a neighborhood graph. In this study, we integrate S-distance to spectral clustering, which is capable to find out the complex and non-linear cluster structures. Moreover, generalized mean distance-based KNN is proposed to decrease the sensitiveness towards the value of the k. Also, a symmetry-favored KNN method is applied to construct the neighborhood graph, which reduces the impact of outliers and noisy data points. However, spectral clustering faces scalability and speedup issues in the case of large size datasets. Thus, the proposed spectral clustering algorithm is also executed in distributed environments. Several experiments are performed to validate the proposed clustering algorithm on 20 real-world datasets and 3 large size datasets. Experimental results demonstrate that the proposed clustering algorithm outperforms some of the baseline methods in terms of accuracy and clustering error rates. Finally, we conduct Wilcoxon's Rank-Sum test and illustrate that the proposed spectral clustering algorithm is statistically significant.
Multipartite entanglement analysis from random correlations

Abstract Quantum entanglement is usually revealed via a well aligned, carefully chosen set of measurements. Yet, under a number of experimental conditions, for example in communication within multiparty quantum networks, noise along the channels or fluctuating orientations of reference frames may ruin the quality of the distributed states. Here, we show that even for strong fluctuations one can still gain detailed information about the state and its entanglement using random measurements. Correlations between all or subsets of the measurement outcomes and especially their distributions provide information about the entanglement structure of a state. We analytically derive an entanglement criterion for two-qubit states and provide strong numerical evidence for witnessing genuine multipartite entanglement of three and four qubits. Our methods take the purity of the states into account and are based on only the second moments of measured correlations. Extended features of this theory are demonstrated experimentally with four photonic qubits. As long as the rate of entanglement generation is sufficiently high compared to the speed of the fluctuations, this method overcomes any type and strength of localized unitary noise.
A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

Data clustering is an important data analysis and data mining tool in many fields such as pattern recognition and image processing. The goal of data clustering is to optimally organize similar objects into clusters. Grey wolf optimizer is a newly introduced optimization algorithm with inspiration from the social behavior of gray wolves. In this work, we propose a modified gray wolf optimizer to tackle some of the challenges in meta-heuristic algorithms. These modifications include a balanced approach to the exploration and exploitation stages of the algorithm as well as a local search around the best solution found. The performance of the proposed algorithm is compared to seven other clustering methods on nine data sets from the UCI machine learning laboratory. Experimental results demonstrate the competence of the proposed algorithm in solving data clustering problems. Overall, the intra-cluster distance of the proposed algorithm is lower than other algorithms and gives an average error rate of 11.22% which is the lowest among all.
Experimental few-copy multipartite entanglement detection

Many future quantum technologies rely on the generation of entangled states. Quantum devices will require verification of their operation below some error threshold, but the reliable detection of quantum entanglement remains a considerable challenge for large-scale quantum systems. Well-established techniques for this task rely on the measurement of expectation values of entanglement witnesses; however these require many measurement settings to be extracted. Here, we develop a generic framework for efficient entanglement detection that translates any entanglement witness into a resource-efficient probabilistic scheme, whose confidence grows exponentially with the number of individual detection events, namely copies of the quantum state. To benchmark our findings, we experimentally verify the presence of entanglement in a photonic six-qubit cluster state generated using three single-photon sources operating at telecommunication wavelengths. We find that the presence of entanglement can be certified with at least 99.74% confidence by detecting 20 copies of the quantum state. Additionally, we show that genuine six-qubit entanglement is verified with at least 99% confidence by using 112 copies of the state. Our protocol can be carried out with a remarkably low number of copies and in the presence of experimental imperfections, making it a practical and applicable method to verify large-scale quantum devices. A general scheme for a resource-efficient probabilistic detection of multipartite entanglement is demonstrated on six-partite cluster states.
Predicting market movement direction for bitcoin: A comparison of time series modeling methods

Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement's direction for the next 5-min time frame. Several machine-learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison.
Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information besides triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE significantly outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.
The triptych of conceptual modeling

Abstract We understand this paper as a contribution to the “anatomy” of conceptual models. We propose a signature of conceptual models for their characterization, which allows a clear distinction from other types of models. The motivation for this work arose from the observation that conceptual models are widely discussed in science and practice, especially in computer science, but that their potential is far from being exploited. We combine our proposal of a more transparent explanation of the nature of conceptual models with an approach that classifies conceptual models as a link between the dimension of linguistic terms and the encyclopedic dimension of notions. As a paradigm we use the triptych, whose central tableau represents the model dimension. The effectiveness of this explanatory approach is illustrated by a number of examples. We derive a number of open research questions that should be answered to complete the anatomy of conceptual models.
High-Throughput Portable True Random Number Generator Based on Jitter-Latch Structure

Under the requirement of highly reliable encryption, the design of true random number generators (TRNGs) based on field-programmable gate arrays (FPGAs) is receiving increased attention. Although TRNGs based on ring oscillators (ROs) and phase-locked loops (PLLs) have the advantages of small resource overhead and high throughput, there are problems such as instability of randomness and poor portability. To improve the randomness, portability, and throughput of a random number generator, we design a TRNG whose randomness is generated by the oscillation of self-timed rings (STRs) and accurately extracted by a jitter-latch structure. The portability of the structure is verified by electronic design automation (EDA) tools. Under the condition of 0°C-80°C ambient temperature and 1.0 ± 0.1 V output voltage, the proposed structure is tested many times on Xilinx Spartan-6 and Virtex-6 FPGAs with an automatic routing mode. Theoretical analysis shows that this method can effectively improve the coverage of jitter and reduce the migration phenomenon. Experimental results show excellent performance in randomness, robustness, and portability, and the throughput reaches 100 Mbps.
A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution

How to effectively fuse temporal information from consecutive frames remains to be a non-trivial problem in video super-resolution (SR), since most existing fusion strategies (direct fusion, slow fusion, or 3D convolution) either fail to make full use of temporal information or cost too much calculation. To this end, we propose a novel progressive fusion network for video SR, in which frames are processed in a way of progressive separation and fusion for the thorough utilization of spatio-temporal information. We particularly incorporate multi-scale structure and hybrid convolutions into the network to capture a wide range of dependencies. We further propose a non-local operation to extract long-range spatio-temporal correlations directly, taking place of traditional motion estimation and motion compensation (ME&MC). This design relieves the complicated ME&MC algorithms, but enjoys better performance than various ME&MC schemes. Finally, we improve generative adversarial training for video SR to avoid temporal artifacts such as flickering and ghosting. In particular, we propose a frame variation loss with a single-sequence training method to generate more realistic and temporally consistent videos. Extensive experiments on public datasets show the superiority of our method over state-of-the-art methods in terms of performance and complexity. Our code is available at https://github.com/psychopa4/MSHPFNL.
Decentralized blockchain-based consensus for Optimal Power Flow solutions

We design, implement and analyze a decentralized consensus algorithm based on the blockchain technology for the solution of the Optimal Power Flow problem. The proposed algorithm enables independent power grid nodes, without prior trust on each other, to reach an agreement on the Optimal Power Flow solution. We decompose the Optimal Power Flow problem using a blockchain-based Alternating Direction Method of Multipliers and we comment on efficiency, trust, security and transparency issues. The successive iterants of the solution of the power flow problem are securely stored on the blockchain, removing the need for a central operating authority, while allowing network nodes to verify the validity and optimality of the solution. We illustrate the effectiveness of the consensus algorithm presented through simulation experiments on the 39-bus New England transmission system, and IEEE-57 and IEEE-118 benchmark systems. We systematically compare the effectiveness of the proposed algorithm with well established blockchain consensus algorithms. The results show that our method maintains the convergence characteristics of the Alternating Direction Method of Multipliers iterative scheme and enables independent network nodes to reach consensus.
Feature Normalized Knowledge Distillation for Image Classification

Knowledge Distillation (KD) transfers the knowledge from a cumbersome teacher model to a lightweight student network. Since a single image may reasonably relate to several categories, the one-hot label would inevitably introduce the encoding noise. From this perspective, we systematically analyze the distillation mechanism and demonstrate that the $$L_2$$ -norm of the feature in penultimate layer would be too large under the influence of label noise, and the temperature T in KD could be regarded as a correction factor for $$L_2$$ -norm to suppress the impact of noise. Noticing different samples suffer from varying intensities of label noise, we further propose a simple yet effective feature normalized knowledge distillation which introduces the sample specific correction factor to replace the unified temperature T for better reducing the impact of noise. Extensive experiments show that the proposed method surpasses standard KD as well as self-distillation significantly on Cifar-100, CUB-200-2011 and Stanford Cars datasets. The codes are in https://github.com/aztc/FNKD
Resolving cross-site scripting attacks through genetic algorithm and reinforcement learning

Cross Site Scripting (XSS) is one of the most frequently occurring vulnerability. The impact of XSS can vary from cosmetic to catastrophic damages. However, detection of XSS efficiently is still an open issue. Cross site scripting has been dealt with static and dynamic analysis previously. Both techniques have shortcomings and fail due to frequent variations in XSS payloads. Therefore, in this paper, we have proposed the use of Genetic Algorithm (GA) along with Reinforcement Learning (RL) and threat intelligence to overcome XSS attacks. For validation, the proposed approach is applied on a real dataset of XSS attacks. Results show better performance of our proposed approach when compared to the approaches reported in the literature. In addition to better performance, our method is not only flexible to changes in XSS payloads, but the results are also more understandable to end users. Moreover, our approach shows improvement when the number of attacks is increased.
Layered neural rendering for retiming people in video

We present a method for retiming people in an ordinary, natural video --- manipulating and editing the time in which different motions of individuals in the video occur. We can temporally align different motions, change the speed of certain actions (speeding up/slowing down, or entirely "freezing" people), or "erase" selected people from the video altogether. We achieve these effects computationally via a dedicated learning-based layered video representation, where each frame in the video is decomposed into separate RGBA layers, representing the appearance of different people in the video. A key property of our model is that it not only disentangles the direct motions of each person in the input video, but also correlates each person automatically with the scene changes they generate---e.g., shadows, reflections, and motion of loose clothing. The layers can be individually retimed and recombined into a new video, allowing us to achieve realistic, high-quality renderings of retiming effects for real-world videos depicting complex actions and involving multiple individuals, including dancing, trampoline jumping, or group running.
A novel secure communications scheme based on chaotic modulation, recursive encryption and chaotic masking

The aim of this paper is to propose a new secure communications approach, obtained by combining into one scheme chaotic modulation, recursive encryption and chaotic masking. The objective is achieved by designing control laws to synchronize two hyperchaotic Lorenz systems with encrypted states via a backstepping technique. The encryption process relies on a Unified chaotic system, driven by the hyperchaotic Lorenz system, as well as on a recursive encryption algorithm that exploits an n-fold composition of an invertible function. A novel theorem is proved, which assures that the transmitted message is recovered at the receiver side by virtue of the synchronization of the two Lorenz systems via backstepping design. Numerical simulations are carried out using image and text as transmitted signals, with the aim to show the effectiveness of the conceived chaos-based encryption scheme.
A Unified Framework for Shot Type Classification Based on Subject Centric Lens

Shots are key narrative elements of various videos, e.g. movies, TV series, and user-generated videos that are thriving over the Internet. The types of shots greatly influence how the underlying ideas, emotions, and messages are expressed. The technique to analyze shot types is important to the understanding of videos, which has seen increasing demand in real-world applications in this era. Classifying shot type is challenging due to the additional information required beyond the video content, such as the spatial composition of a frame and camera movement. To address these issues, we propose a learning framework Subject Guidance Network (SGNet) for shot type recognition. SGNet separates the subject and background of a shot into two streams, serving as separate guidance maps for scale and movement type classification respectively. To facilitate shot type analysis and model evaluations, we build a large-scale dataset MovieShots, which contains 46K shots from 7K movie trailers with annotations of their scale and movement types. Experiments show that our framework is able to recognize these two attributes of shot accurately, outperforming all the previous methods.
Novel Triplex Procedure for Ranking the Ability of Software Engineering Students Based on Two levels of AHP and Group TOPSIS Techniques

Ranking the strengths and weaknesses of software engineering students in software development life cycle (SDLC) process level is a challenging task owing to (1) data variation, (2) multievaluation criteria, (3) criterion importance and (4) alternative member importance. According to the existing literature, no specified procedure can rank the ability of software engineering students based on SDLC process levels to figure out the strengths and weaknesses of each student. This study aims to present a novel triplex procedure for ranking the ability of software engineering students to address the literature gap. The methodology of the proposed work is presented on the basis of three phases. In the identification phase, four steps are implemented, namely, processing dataset, identifying the criteria, distributing the courses to the software engineering body of knowledge and proposing the pre-decision matrix (DM). The data comprise the GPA and soft skills from 60 software engineering students who graduated from Universiti Pendidikan Sultan Idris in 2016. In the pre-processing phase, three steps are involved as follows. Analytic hierarchy process (AHP) is first used to assign weights to the courses and then multiply the assigned weight by courses, which is the first procedure in the proposed work. In this phase, the construction of DM is presented based on multimeasurement criteria (GPA and soft skills), with SDLC process levels as alternatives. In the development phase, AHP is used again to weight the multimeasurement criteria, and this is the second procedure. In such case, the coordinator and head of the software engineering department are consulted to obtain subjective judgments for each criterion. Technique for order performance by similarity to ideal solution (TOPSIS) is then used to rank the students, which is the third procedure. In the validation, statistical analysis is performed to validate the results by checking the accuracy of the systematic ranking. Results show that (1) integrating AHP and group TOPSIS is suitable for ranking the ability of students. (2) The 60 students are categorized into five ranking groups based on their strength level: 14 collector requirements, 13 designers, 5 programmers, 13 testers and 15 maintenances. (3) Significant differences are observed between the groups’ scores for each level of SDLC, indicating that the ranking results are identical for all levels.
Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture

(2021). Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture. Computer Methods in Biomechanics and Biomedical Engineering: Vol. 24, No. 8, pp. 864-873.
Hate Speech Detection in the Bengali Language: A Dataset and Its Baseline Evaluation

Social media sites such as YouTube and Facebook have become an integral part of everyone’s life, and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media Web sites faces a variety of challenges including small imbalanced datasets, the finding of an appropriate model and also the choice of feature analysis method. Furthermore, this problem is more severe for the Bengali speaking community due to the lack of gold standard labeled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowdsourcing and verified by expert. All the user comments collected from YouTube and Facebook comment section and classified into seven categories: sports, entertainment, religion, politics, crime, celebrity and TikTok and meme. A total of 50 annotators annotated each comment three times, and the majority vote was taken as the final annotation. Nevertheless, we have conducted baseline experiments and several deep learning models along with extensive pretrained Bengali word embedding such as Word2Vec, FastText and BengFastText on this dataset to facilitate future research opportunities. The experiment illustrated that although all the deep learning model performed well, SVM achieved the best result with 87.5% accuracy. Our core contribution is to make this benchmark dataset available and accessible to facilitate further research in the field of Bengali hate speech detection.