Reviewerly/exHarmony huggingface dataset:
A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi – LSTM model for semantic text similarity identification

The conventional semantic text-similarity methods requires high amount of trained labeled data and also human interventions. Generally, it neglects the contextual-information and word-orders information resulted in data sparseness problem and latitudinal-explosion issue. Recently, deep-learning methods are used for determining text-similarity. Hence, this study investigates NLP application tasks usage in detecting text-similarity of question pairs or documents and explores the similarity score predictions. A new hybridized approach using Weighted Fine-Tuned BERT Feature extraction with Siamese Bi-LSTM model is implemented. The technique is employed for determining question pair sets using Semantic-text-similarity from Quora dataset. The text features are extracted using BERT process, followed by words embedding with weights. The features along with weight values, are represented as embedded vectors, are subjected to various layers of Siamese Networks. The embedded vectors of input text features were trained by using Deep Siamese Bi-LSTM model, in various layers. Finally, similarity scores are determined for each sentence, and the semantic text-similarity is learned. The performance evaluation of proposed-framework is established with respect to accuracy rate, precision value, F1 score data and Recall values parameters compared with other existing text-similarity detection methods. The proposed-framework exhibited higher efficiency rate with 91% in accuracy level in determining semantic-text-similarity compared with other existing algorithms.
Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools

Writing a computer program is a complex cognitive task, especially for a new person in the field. In this research an eye-tracking system was developed and applied, which allows the observation of eye movement parameters during programming as a complex, cognitive process, and the conclusions can be drawn from the results. The aim of the paper is to examine whether the flowchart or Nassi–Shneiderman diagram is a more efficient algorithm descripting tool for describing cognitive load by recording and evaluating eye movement parameters. The results show that the case of the interpreting flowchart has significantly longer fixation duration, more number of fixations, and larger pupil diameter than the case of the Nassi–Shneiderman diagram interpreting. Based on the results of the study, it is clear how important it is to choose the right programming tools for efficient and lower cost application development.
Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation

Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method’s effectiveness. Several benchmark images are used to validate the proposed algorithm’s performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.
Eavesdropping on the Decohering Environment: Quantum Darwinism, Amplification, and the Origin of Objective Classical Reality

"How much information about a system S can one extract from a fragment F of the environment E that decohered it?" is the central question of Quantum Darwinism. To date, most answers relied on the quantum mutual information of SF, or on the Holevo bound on the channel capacity of F to communicate the classical information encoded in S. These are reasonable upper bounds on what is really needed but much harder to calculate-the accessible information in the fragment F about S. We consider a model based on imperfect c-not gates where all the above can be computed, and discuss its implications for the emergence of objective classical reality. We find that all relevant quantities, such as the quantum mutual information as well as various bounds on the accessible information exhibit similar behavior. In the regime relevant for the emergence of objective classical reality this includes scaling independent of the quality of the imperfect c-not gates or the size of E, and even nearly independent of the initial state of S.
KAT: A Knowledge Augmented Transformer for Vision-and-Language

Liangke Gui, Borui Wang, Qiuyuan Huang, Alexander Hauptmann, Yonatan Bisk, Jianfeng Gao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Protecting against Statistical Ineffective Fault Attacks

Statistical Ineffective Fault Attacks (SIFA) pose a threat for many practical implementations of symmetric primitives. Countermeasures against both power analysis and fault attacks typically do not prevent straightforward SIFA attacks, which require only very limited knowledge about the concrete implementation. Therefore, the exploration of countermeasures against SIFA that do not rely on protocols or physical protection mechanisms is of great interest. In this paper, we describe different countermeasure strategies against SIFA. First, we introduce an abstraction layer between the algorithmic specification of a cipher and its implementation in hardware or software to study and describe resistance against SIFA. We then show that by basing the masked implementation on permutations as building blocks, we can build circuits that withstand single-fault SIFA and DPA attacks. We show how this approach can be applied to 3-bit, 4-bit, and 5-bit S-boxes and the AES S-box. Additionally, we present a strategy based on fine-grained fault detection suitable for protecting any circuit against SIFA attacks. Although this approach may lead to a higher implementation cost due to the fine-grained detection needed, it can be used to protect arbitrary circuits and can be generalized to cover multi-fault SIFA. For single-fault SIFA protection, our countermeasures only have a small computational overhead compared to a simple combination of masking and duplication.
Enhancing quantum cryptography with quantum dot single-photon sources

Abstract Quantum cryptography harnesses quantum light, in particular single photons, to provide security guarantees that cannot be reached by classical means. For each cryptographic task, the security feature of interest is directly related to the photons’ non-classical properties. Quantum dot-based single-photon sources are remarkable candidates, as they can in principle emit deterministically, with high brightness and low multiphoton contribution. Here, we show that these sources provide additional security benefits, thanks to the tunability of coherence in the emitted photon-number states. We identify the optimal optical pumping scheme for the main quantum-cryptographic primitives, and benchmark their performance with respect to Poisson-distributed sources such as attenuated laser states and down-conversion sources. In particular, we elaborate on the advantage of using phonon-assisted and two-photon excitation rather than resonant excitation for quantum key distribution and other primitives. The presented results will guide future developments in solid-state and quantum information science for photon sources that are tailored to quantum communication tasks.
Blockchain Technology toward Smart Construction: Review and Future Directions

The construction industry has been criticized for low productivity, lack of collaboration and information sharing, poor contract administration, and the like due to its decentralized and fragmented structure as well as sequential and chain-resembling nature. Recently, blockchain technology and its benefits have received wide attention and interest. This research synthesizes the research trends and needs of this growing area by means of a bibliometric-qualitative review method. Scopus and Web of Science were selected as the literature databases to retrieve relevant academic publications. Through a systematic literature search and screening, 181 related articles were identified for bibliometric analysis, and 149 publications were critically discussed in a qualitative review. The bibliometric results indicated the recent research regarding blockchain in construction is primarily directed into several clusters, such as “smart contract,” “Building Information Modeling (BIM),” “supply chain management,” “construction contract,” “construction and project management,” “digital twin,” and “smart city.” These clusters were further synthesized for a qualitative review revealing deep insight into research challenges and gaps. Both quantitative and qualitative review results were then mapped to the future directions. It was noted that future research needs to focus on (1) quantifying the cost-benefits of the blockchain applications in construction, e.g., return on investment, practitioners training, and improving industry readiness, (2) integration of blockchain with different project delivery systems, and (3) technology fusion with blockchain for construction management.
Revealing hidden quantum steerability using local filtering operations

Nonlocal quantum correlation is at the heart of bizarre nature of quantum physics. While there are various classes of nonlocal quantum correlation, steerability of a quantum state by local measurements provides unique operational features. Here, we theoretically and experimentally investigate the hidden quantum steerability. In particular, we find that there are initially unsteerable states which can reveal the steerability by using local filters on individual quantum systems. It is remarkable that a certain set of local filters are more effective on revealing steerability than Bell nonlocality whereas there exists another set of filters that is more effective on revealing Bell nonlocality than steerability. Finally, we present a counter-intuitive result that mixed states originating from non-maximally pure entangled states can have hidden steerability while the mixed state from a maximally pure entangled state fails to show steerability.
Image processing operations identification via convolutional neural network

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.
Hierarchy Visualization for Group Recommender Systems

Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.
Experimental Pauli-frame randomization on a superconducting qubit

The promise of quantum computing with imperfect qubits relies on the ability of a quantum computing system to scale cheaply through error correction and fault tolerance. While fault tolerance requires relatively mild assumptions about the nature of qubit errors, the overhead associated with coherent and non-Markovian errors can be orders of magnitude larger than the overhead associated with purely stochastic Markovian errors. One proposal to address this challenge is to randomize the circuits of interest, shaping the errors to be stochastic Pauli errors but leaving the aggregate computation unaffected. The randomization technique can also suppress couplings to slow degrees of freedom associated with non-Markovian evolution. Here, we demonstrate the implementation of Pauli-frame randomization in a superconducting circuit system, exploiting a flexible programming and control infrastructure to achieve this with low effort. We use high-accuracy gate-set tomography to characterize in detail the properties of the circuit error, with and without the randomization procedure, which allows us to make rigorous statements about Markovianity as well as the nature of the observed errors. We demonstrate that randomization suppresses signatures of non-Markovian evolution to statistically insignificant levels, from a Markovian model violation ranging from $43\ensuremath{\sigma}$ to $1987\ensuremath{\sigma}$, down to violations between $0.3\ensuremath{\sigma}$ and $2.7\ensuremath{\sigma}$ under randomization. Moreover, we demonstrate that, under randomization, the experimental errors are well described by a Pauli error model, with model violations that are similarly insignificant (between $0.8\ensuremath{\sigma}$ and $2.7\ensuremath{\sigma}$). Importantly, all these improvements in the model accuracy were obtained without degradation to fidelity, and with some improvements to error rates as quantified by the diamond norm. This demonstrates the ability of Pauli-frame randomization to shape noise into forms that are more benign for quantum error correction and fault tolerance.
Spectral Clustering of Customer Transaction Data With a Two-Level Subspace Weighting Method

Finding customer groups from transaction data is very important for retail and e-commerce companies. Recently, a “Purchase Tree” data structure is proposed to compress the customer transaction data and a local PurTree spectral clustering method is proposed to cluster the customer transaction data. However, in the PurTree distance, the node weights for the children nodes of a parent node are set as equal and the differences between different nodes are not distinguished. In this paper, we propose a two-level subspace weighting spectral clustering (TSW) algorithm for customer transaction data. In the new method, a PurTree subspace metric is proposed to measure the dissimilarity between two customers represented by two purchase trees, in which a set of level weights are introduced to distinguish the importance of different tree levels and a set of sparse node weights are introduced to distinguish the importance of different tree nodes in a purchase tree. TSW learns an adaptive similarity matrix from the local distances in order to better uncover the cluster structure buried in the customer transaction data. Simultaneously, it learns a set of level weights and a set of sparse node weights in the PurTree subspace distance. An iterative optimization algorithm is proposed to optimize the proposed model. We also present an efficient method to compute a regularization parameter in TSW. TSW was compared with six clustering algorithms on ten benchmark data sets and the experimental results show the superiority of the new method.
On causal discovery with an equal-variance assumption

Summary Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.
Augmented Reality Games as a New Class of Physical Activity Interventions? The Impact of Pokémon Go Use and Gaming Intensity on Physical Activity

Objective: To assess the health impact of augmented reality games by examining the association between Pokémon Go and physical activity among university students. Materials and Methods: This pilot study included 65 medical students who were iPhone (Apple, Inc., Cupertino, CA) users with the built-in accelerometer and Health app. Main outcome measures were the change in daily walking distance before and after the release of Pokémon Go (Niantic, Inc., San Francisco, CA). Results: Twenty-four (36.9%) medical students were active Pokémon Go players. When compared with nonplayers, Pokémon Go players on average walked 1.5, 1.2, 0.9, and 0.6 km more daily on the third, fourth, fifth, and sixth day of the game, respectively (P < 0.05). Physical activity differences were not detected beyond the first week. Among Pokémon Go players, higher intensity of gaming was associated with increased distance walked 50 days after the release of the game compared to previously (P < 0.001). Conclusions: In this pilot study, Pokémon Go was associated with a transient increase in physical activity in the first week. Augmented reality games need to demonstrate a sustained positive health impact to be promoted as a new class of physical activity interventions.
Rethinking the Effective Sample Size

Summary The effective sample size (ESS) is widely used in sample‐based simulation methods for assessing the quality of a Monte Carlo approximation of a given distribution and of related integrals. In this paper, we revisit the approximation of the ESS in the specific context of importance sampling. The derivation of this approximation, that we will denote as , is partially available in a 1992 foundational technical report of Augustine Kong. This approximation has been widely used in the last 25 years due to its simplicity as a practical rule of thumb in a wide variety of importance sampling methods. However, we show that the multiple assumptions and approximations in the derivation of make it difficult to be considered even as a reasonable approximation of the ESS. We extend the discussion of the in the multiple importance sampling setting, we display numerical examples and we discuss several avenues for developing alternative metrics. This paper does not cover the use of ESS for Markov chain Monte Carlo algorithms.
Cryptocurrency Investing Examined

In this work we examine the largest 100 cryptocurrency returns ranging from 2015 to early 2018.We concentrate our analysis on daily returns and find several interesting stylized facts.First, principal components analysis reveals a complex daily return generating process.As we examine data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation.Second, similar to hedge fund returns, cryptocurrency returns suffer from the "beta-in-the-tails" hidden risk.Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per crypto-currency.Fourth, lower volatile cryptocurrencies are slightly more predictable than more volatile ones.Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms.Finally, short-term predictability is very tenuous, which suggests that near-term cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging.
A robust phase unwrapping algorithm based on reliability mask and weighted minimum least-squares method

A robust phase unwrapping algorithm based on reliability mask and weighted minimum least-squares method is proposed. The reliability mask is a 0–1 mask generated according to the reliability map of a wrapped phase map, which is calculated based on the second differences of each pixel. This mask not only ensures the reliability of weighting coefficients but also retains the ability of the 0–1 mask to prevent the noise values from influencing the results. The principle and realization of the proposed algorithm are described in detail. Experiments involving both simulated and actual wrapped phase images are performed to verify the feasibility and effectiveness of the proposed algorithm under various noise conditions. The results show that the proposed method can provide a more reliable and more accurate unwrapped phase, which indicates that our algorithm is more suitable for high-precision phase unwrapping of the actual wrapped phases containing various and complex noises.
Centralized and decentralized bitcoin markets: Euro vs USD vs GBP

In this study, I compared the euro, U.S. dollar, and British pound sterling (GBP) centralized and decentralized bitcoin cryptocurrency markets in terms of return volatility and interdependency. This comparison showed the decentralized bitcoin market has higher volatility and the centralized markets have higher tail dependence regarding returns. The volatility analysis results are contrary to the established leverage reasons that market drops cause volatility. The results demonstrate a higher left tail dependence is in line with the general pattern in “traditional” financial markets which more extreme dependent in downturns. It was also shown trade volume increases as prices decrease, demonstrating participants’ lack of confidence and consensus in a price-jump period.