Reviewerly/exHarmony huggingface dataset:
Decision-Guided Weighted Automata Extraction from Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have demonstrated their effectiveness in learning and processing sequential data (e.g., speech and natural language). However, due to the black-box nature of neural networks, understanding the decision logic of RNNs is quite challenging. Some recent progress has been made to approximate the behavior of an RNN by weighted automata. They provide better interpretability, but still suffer from poor scalability. In this paper, we propose a novel approach to extracting weighted automata with the guidance of a target RNN's decision and context information. In particular, we identify the patterns of RNN's step-wise predictive decisions to instruct the formation of automata states. Further, we propose a state composition method to enhance the context-awareness of the extracted model. Our in-depth evaluations on typical RNN tasks, including language model and classification, demonstrate the effectiveness and advantage of our method over the state-of-the-arts. The evaluation results show that our method can achieve accurate approximation of an RNN even on large-scale tasks.
Testing Independence Between Linear Combinations for Causal Discovery

Recently, regression based conditional independence (CI) tests have been employed to solve the problem of causal discovery. These methods provide an alternative way to test for CI by transforming CI to independence between residuals. Generally, it is nontrivial to check for independence when these residuals are linearly uncorrelated. With the ability to represent high-order moments, kernel-based methods are usually used to achieve this goal, but at a cost of considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model (SEM). We show that generally the 1-st to 4-th moments of the two linear combinations contain enough information to infer whether or not they are independent. The proposed method provides a simpler but more effective way to measure CIs, with only calculating the 1-st to 4-th moments of the input variables. When applied to causal discovery, the proposed method outperforms kernel-based methods in terms of both speed and accuracy. which is validated by extensive experiments.
Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models

Watermarking has become the tendency in protecting the intellectual property of DNN models. Recent works, from the adversary's perspective, attempted to subvert watermarking mechanisms by designing watermark removal attacks. However, these attacks mainly adopted sophisticated fine-tuning techniques, which have certain fatal drawbacks or unrealistic assumptions. In this paper, we propose a novel watermark removal attack from a different perspective. Instead of just fine-tuning the watermarked models, we design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations, which can effectively and blindly destroy the memorization of watermarked models to the watermark samples. We also introduce a lightweight fine-tuning strategy to preserve the model performance. Our solution requires much less resource or knowledge about the watermarking scheme than prior works. Extensive experimental results indicate that our attack can bypass state-of-the-art watermarking solutions with very high success rates. Based on our attack, we propose watermark augmentation techniques to enhance the robustness of existing watermarks.
A Relation-Oriented Clustering Method for Open Relation Extraction

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE).However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes.In this work, we propose a relationoriented clustering model and use it to identify the novel relations in the unlabeled data.Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relationoriented representation.We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly.To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data.Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.
Encoding Qubits in Multimode Grid States

Encoding logical quantum information in harmonic oscillator modes is a promising and hardware-efficient approach to the realization of a quantum computer. In this work, we propose to encode logical qubits in grid states of an ensemble of harmonic oscillator modes. We first discuss general results about these multimode bosonic codes; how to design them, how to practically implement them in different experimental platforms and how lattice symmetries can be leveraged to perform logical non-Clifford operations. We then introduce in detail two two-mode grid codes based on the hypercubic and D4 lattices, respectively, showing how to perform a universal set of logical operations. We demonstrate numerically that multimode grid codes have, compared to their single-mode counterpart, increased robustness against propagation of errors from ancillas used for error correction. Finally, we highlight some interesting links between multidimensional lattices and single-mode grid codes concatenated with qubit codes.
Validating and certifying stabilizer states

We propose a measurement scheme that validates the preparation of an $n$-qubit stabilizer state. The scheme involves a measurement of $n$ Pauli observables, a priori determined from the stabilizer state and which can be realized using single-qubit gates. Based on the proposed validation scheme, we derive an explicit expression for the worst-case fidelity, i.e., the minimum fidelity between the stabilizer state and any other state consistent with the measured data. We also show that the worst-case fidelity can be certified, with high probability, using $O({n}^{2})$ copies of the state.
Are we there yet? Manifold identification of gradient-related proximal methods

In machine learning, models that generalize better often generate outputs that lie on a low-dimensional manifold. Recently, several works have separately shown finite-time manifold identification by some proximal methods. In this work we provide a unified view by giving a simple condition under which any proximal method using a constant step size can achieve finite-iteration manifold detection. For several key methods (FISTA, DRS, ADMM, SVRG, SAGA, and RDA) we give an iteration bound, characterized in terms of their variable convergence rate and a problem-dependent constant that indicates problem degeneracy. For popular models, this constant is related to certain data assumptions, which gives intuition as to when lower active set complexity may be expected in practice.
On the interaction between a time-dependent field and a two-level atom

In this paper, we study the interaction between the time-dependent field and a two-level atom with one mode electromagnetic field. We consider that the field of photons is assumed to be coupled with modulated coupling parameter which depends explicitly on time. It is shown that the considered model can be reduced to a well-known form of the time-dependent generalized Jaynes–Cummings model. Under special initial conditions, in which the atom and the field are prepared in the excited and the coherent states, respectively, the explicit time evolution of the wave function of the entire system is analytically obtained. Our proposal has many advantages over the previous optical schemes and can be realized in several multiple experiments, such as trapped ions and quantum electrodynamics cavity. The influence of the time-dependent field parameter on the collapses-revivals, the normal squeezing of the radiation, the anti-bunching of photons and the entanglement phenomena for the considered atomic system is examined. The linear entropy, the von Neumann entropy are used to quantify entanglement in the quantum systems. We noticed that these phenomena are affected by the existence of both the time-dependent coupling field and detuning parameters.
Comparisons of δ-Two-Stream and δ-Four-Stream Radiative Transfer Schemes in RRTMG for Solar Spectra

Five δ-two-stream and δ-four-stream schemes are compared in solar spectra using the Rapid Radiative Transfer Model for General Circulation Models Applications (RRTMG). By calculating the flux and heating rate in various atmosphere, it is found that, in accuracy, the δ-four-stream schemes overwhelmingly outperform the δ-two-stream schemes. The precision of adding algorithm of the δ-four-stream spherical harmonic expansion (δ-4SDA) is comparable to that of adding algorithm of the δ-four-stream discrete ordinates method (δ-4DDA). Furthermore, the accuracy of the adding algorithm of δ-Eddington approximation (δ-2SDA) is close to that of δ-two-stream approximation with Practical Improved Flux Method (δ-PIFM), while adding algorithm of δ-two-stream discrete ordinates method (δ-2DDA) produces the poorest results among the five approximate schemes. For the RRTMG model with radiative transfer calculation, the computational time of δ-4SDA is about 1.5 times that of δ-two-stream schemes, and the computational time of δ-4SDA is about 88% that of δ-4DDA.
Artificial Intelligence for Cognitive Wireless Communications

The 13 articles in this special section focus on artificial intelligence and cognitive wireless communications. With the rapid development of wireless communication and networking technologies, novel information services and applications are booming globally. Advanced communications and networks greatly enhance users’ experience and have had a significant impact on all aspects of people’s lives, including at home, at work, in social exchanges, and economically. Although these advanced techniques have extensively improved users’ quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide area coverage, high-capacity hotspot, low-power massive connections, low latency and high reliability, and other scenarios. Under the new service paradigm, artificial intelligence (AI) is significant for cognitive wireless communications to meet various technical challenges. The application potential includes complicated decision making, wireless network management, resource optimization, and in-depth knowledge discovery in complex wireless networking environments. These articles present a comprehensive view of research challenges and opportunities regarding AI for cognitive wireless communications in order to cope with the extreme demands of user experience, efficiency, and performance in a complex wireless networking environment.
Job Satisfaction and Retention of Nursing Staff in Saudi Hospitals.

The objective of this study is to investigate job satisfaction amongst nurses in Saudi Arabian hospitals. In recent years, there has been considerable growth in the healthcare system in Saudi Arabia, yet little attention has been paid to improving the performance of healthcare professionals, by improving job satisfaction and retention of nursing staff. This paper reviews the research conducted on job satisfaction, and retention of Saudi nursing staff. This is an integrative review of previous studies on job satisfaction and retention of Saudi nursing staff. The electronic databases Google Scholar, CINAHL, PubMed, and Global Health were used to identify peer-reviewed literature published between 2009 and 2018. The literature review showed that the majority of nurses were satisfied in their job. However, there was a shortage of research in retention of nurses. The evidence from this study suggests that the hospitals need to ensure high level of job satisfaction and decent wages of nurses for maximum retention of nurses.&#x0D;
Heat pain modulation with virtual water during a virtual hand illusion

Abstract Immersive virtual reality is a powerful method to modify the environment and thereby influence experience. The present study used a virtual hand illusion and context manipulation in immersive virtual reality to examine top-down modulation of pain. Participants received painful heat stimuli on their forearm and placed an embodied virtual hand (co-located with their real one) under a virtual water tap, which dispensed virtual water under different experimental conditions. We aimed to induce a temperature illusion by a red, blue or white light suggesting warm, cold or no virtual water. In addition, the sense of agency was manipulated by allowing participants to have high or low control over the virtual hand’s movements. Most participants experienced a thermal sensation in response to the virtual water and associated the blue and red light with cool/cold or warm/hot temperatures, respectively. Importantly, the blue light condition reduced and the red light condition increased pain intensity and unpleasantness, both compared to the control condition. The control manipulation influenced the sense of agency, but did not influence pain ratings. The large effects revealed in our study suggest that context effects within an embodied setting in an immersive virtual environment should be considered within VR based pain therapy.
Evolution of the big deals use in the public universities of the Castile and Leon region, Spain

The evolution of the use of electronic scientific journals in the 2007-2018 period by the academic communities of the public universities of Castilla and Leon, members of the Bucle consortium, is studied.Downloads of articles distributed by four broad-spectrum providers are analyzed: Emerald, ScienceDirect, SpringerLink, and Wiley.Unexpectedly, in spite of the new ways of accessing content, this analysis shows that the number of downloads in public universities in Castile and Leon has increased.However, it has been found that despite the growth, a few journals concentrate most of the downloads and that a large number of titles are not used.It has been verified that the universities with a higher number of staff and greater diversity of knowledge areas use a wider range of titles and thus obtain a higher benefit from their contracts.All of the above leads us to question the system of contracting big deals, i.e., bundles of hundreds of multidisciplinary journals and other resources.In a moment of change promoted by Plan S, we consider it more necessary than ever to understand the situation of the provision of serial scientific publications and to find the best strategy to have access to them.
Locally Differentially Private Frequency Estimation with Consistency

Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency oracle (FO) protocols, which estimate frequencies of values. While several FO protocols have been proposed, the design goal does not lead to optimal results for answering many queries. In this paper, we show that adding post-processing steps to FO protocols by exploiting the knowledge that all individual frequencies should be non-negative and they sum up to one can lead to significantly better accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. We consider 10 different methods that exploit this knowledge differently. We establish theoretical relationships between some of them and conducted extensive experimental evaluations to understand which methods should be used for different query tasks.
Synthesis of Flexible Accelerators for Early Adoption of Ring-LWE Post-quantum Cryptography

The advent of the quantum computer makes current public-key infrastructure insecure. Cryptography community is addressing this problem by designing, efficiently implementing, and evaluating novel public-key algorithms capable of withstanding quantum computational power. Governmental agencies, such as NIST, are promoting standardization of quantum-resistant algorithms that is expected to run for 7 years. Several modern applications must maintain permanent data secrecy; therefore, they ultimately require the use of quantum-resistant algorithms. Because algorithms are still under scrutiny for eventual standardization, the deployment of the hardware implementation of quantum-resistant algorithms is still in early stages. In this article, we propose a methodology to design programmable hardware accelerators for lattice-based algorithms, and we use the proposed methodology to implement flexible and energy efficient post-quantum cache-based accelerators for NewHope , Kyber , Dilithium , Key Consensus from Lattice ( KCL ), and R.EMBLEM submissions to the NIST standardization contest. To the best of our knowledge, we propose the first efficient domain-specific, programmable cache-based accelerators for lattice-based algorithms. We design a single accelerator for a common kernel among various schemes with different kernel sizes, i.e., loop count, and data types. This is in contrast to the traditional approach of designing one special purpose accelerators for each scheme. We validate our methodology by integrating our accelerators into an HLS-based SoC infrastructure based on the X86 processor and evaluate overall performance. Our experiments demonstrate the suitability of the approach and allow us to collect insightful information about the performance bottlenecks and the energy efficiency of the explored algorithms. Our results provide guidelines for hardware designers, highlighting the optimization points to address for achieving the highest energy minimization and performance increase. At the same time, our proposed design allows us to specify and execute new variants of lattice-based schemes with superior energy efficiency compared to the main application processor without changing the hardware acceleration platform. For example, we manage to reduce the energy consumption up to 2.1× and energy-delay product (EDP) up to 5.2× and improve the speedup up to 2.5×.
On Quantum Distinguishers for Type-3 Generalized Feistel Network Based on Separability

In this work, we derive a method for constructing quantum distinguishers for GFNs (Generalized Feistel-like schemes with invertible inner functions and XORs), where for simplicity 4 branches are considered. The construction technique is demonstrated on Type-3 GFN, where some other cyclically inequivalent GFNs are considered as examples. Introducing the property of separability, we observe that finding a suitable partition of input blocks implies that some branches can be represented as a sum of functions with almost disjoint variables, which simplifies the application of Simon’s algorithm. However, higher number of rounds in most of the cases have branches which do not satisfy the previous property, and in order to derive a quantum distinguisher for these branches, we employ Simon’s and Grover’s algorithm in combination with a suitable system of equations given in terms of input blocks and inner functions involved in the round function. As a result, we are able to construct a 5-round quantum distinguisher for Type-3 GFNs using only a quantum encryption oracle with query complexity $$2^{N/4}\cdot \mathcal {O}(N/4)$$, where N size of the input block.
FUTURE FOR SCIENTIFIC COMPUTING USING PYTHON

Computational science (scientific computing or scientific computation) is concerned with constructing mathematical models as well as quantitative analysis techniques and using computers to analyze as well as solve scientific problems. In practical use, it is basically the application of computer simulation as well as other forms of computation from numerical analysis and theoretical computer science to problems in different scientific disciplines. The scientific computing approach is to gain understanding, basically through the analysis of mathematical models implemented on computers. Python is frequently used for highperformance scientific applications and widely used in academia as well as scientific projects because it is easy to write and performs well. Due to its high performance nature, scientific computing in Python often utilizes external libraries like NumPy, SciPy and Matplotlib etc.
Efficient image cipher based on the movement of king on the chessboard and chaotic system

A new and an efficient image cipher using chaos theory and the king chess piece is proposed. The given image is being treated as a large chessboard. The king scrambles the pixels of the given image through its random movement on the chessboard. Once the image is scrambled, it is further diffused through the XOR operation. Chaotic data to be used in the cipher have been generated through the Tinkerbell chaotic map. Image characteristics have been directly used to embed the plaintext sensitivity in the cipher to avoid the complicated and lengthy hash codes. The starting address of the king has also been made a key. It increases both the key space and plaintext sensitivity of the proposed image cipher. Both the simulation and comprehensive security analyses exhibit the efficiency, robustness, security, and applicability of the proposed algorithm.
LR-CNN: LOCAL-AWARE REGION CNN FOR VEHICLE DETECTION IN AERIAL IMAGERY

Abstract. State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs’ features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset.