Reviewerly/exHarmony huggingface dataset:
Quantum state transfer between two photons with polarization and orbital angular momentum via quantum teleportation technology

Quantum teleportation is a useful quantum information technology to transmit quantum states between different degrees of freedom. Here we report a quantum state transfer experiment in the linear optical system, transferring a single-photon state in the polarization degree of freedom (DoF) to another photon in the orbital angular momentum (OAM) quantum state via a biphoton OAM entangled channel. Our experimental method is based on quantum teleportation technology. The differences between ours and the original teleportation scheme is that the transfer state is known in ours, and our method is for different particles with different DoFs, while the original one is for different particles with the same DoF. In addition, our present experiment is implemented with a high Bell efficiency since each of the four hybrid-entangled Bell states can be discriminated. We use six states of poles of the Bloch sphere to test our experiment, and the fidelity of the quantum state transfer is $91.8\ifmmode\pm\else\textpm\fi{}1.3%$.
PMENet: phase map enhancement for Fourier transform profilometry using deep learning

Fringe projection profilometry (FPP) is a three-dimensional (3D) shape measurement method that involves projecting fringe patterns onto the object. A phase map retrieved from these fringe images is used for reconstructing the 3D surface of the object. Fourier transform and phase-shifting are two of the widely used fringe analysis techniques for performing 3D shape measurement using FPP. Fourier transform profilometry (FTP) has the advantage of performing high-speed measurement due to its single-shot nature. However, the reconstructed 3D surface has artifacts and high noise, specifically on the edges. On the other hand, phase-shifting profilometry (PSP) has the advantage of higher accuracy (relatively less noise level) but compromises on measurement speed. In this research, we propose a deep learning method to enhance the quality of the FTP phase maps using an efficient deep learning model called phase map enhancement net (PMENet). PMENet takes an FTP phase map as input and predicts a high-quality phase map in a supervised manner by using the phase maps obtained from 18-step PSP as ground truth. The training dataset was generated using a virtual FPP system. Validations were conducted on both the simulated data (generated by virtual FPP system) and the real-world data. The experimental results demonstrate that the trained neural network model can successfully improve the quality of the 3D geometric reconstruction with FTP and reduced the mean and root-mean-square error by 66% and 43%, respectively.
SRAGL-AWCL: A two-step multi-view clustering via sparse representation and adaptive weighted cooperative learning

Sparse representation and cooperative learning are two representative technologies in the field of multi-view spectral clustering. The former can effectively extract features of multiple views by the removal of redundant information contained in each view. The latter can incorporate the diversity of each view. However, traditional sparse representation and cooperative learning algorithms are inadequate in preserving the internal geometric features of data by manifold regularization. In fact, general approaches rarely consider the similarities between the internal graph structures of individual views. Moreover, to achieve the optimal global feature learning, we present a novel two-step multi-view spectral clustering strategy, which combines the proposed sparse representation by adaptive graph learning with adaptive weighted cooperative learning. In the first step, the proposed matrix factorization by manifold regularization can strengthen the sparse features clustering discrimination of samples of each view. Specifically, the synchronization optimization method by introducing adaptive graph learning can better retain its internal complete structure of each view. This ensures the structure correlation of views through the usage of the sparse matrix and the optimal graph similarity matrix. In the second step, the adaptive weighted cooperative learning is performed on each view to get a global optimized matrix. In order to ensure that the global matrix is associated with various view features, graph learning is also performed on the global matrix. Experiment results on several multi-view datasets and single-view datasets show that the proposed method significantly outperformed the state-of-the-art algorithms.
Optimization-Based On-Road Path Planning for Articulated Vehicles

Maneuvering an articulated vehicle on narrow road stretches is often a challenging task for a human driver. Unless the vehicle is accurately steered, parts of the vehicle's bodies may exceed its assigned drive lane, resulting in an increased risk of collision with surrounding traffic. In this work, an optimization-based path-planning algorithm is proposed targeting on-road driving scenarios for articulated vehicles composed of a tractor and a trailer. To this end, we model the tractor-trailer vehicle in a road-aligned coordinate frame suited for on-road planning. Based on driving heuristics, a set of different optimization objectives is proposed, with the overall goal of designing a path planner that computes paths which minimize the off-track of the vehicle bodies swept area, while remaining on the road and avoiding collision with obstacles. The proposed optimization-based path-planning algorithm, together with the different optimization objectives, is evaluated and analyzed in simulations on a set of complicated and practically relevant on-road planning scenarios using the most challenging tractor-trailer dimensions.
What do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis

Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple visual concepts. In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. Salient image areas identified by local interpretability methods are annotated with semantic concepts, which are then aggregated into a tabular representation of images to facilitate automatic statistical analysis of model behavior. We show that this approach answers interpretability needs for both model validation and exploration, and provides semantically more diverse, informative, and relevant explanations while still allowing for scalable and cost-efficient execution.
Interactive Knowledge Distillation for image classification

Knowledge distillation (KD) is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained, large teacher network. As an effective teaching strategy, interactive teaching has been widely employed at school to motivate students, in which teachers not only provide knowledge, but also give constructive feedback to students upon their responses, to improving their learning performance. In this work, we propose Interactive Knowledge Distillation (IAKD) to leverage the interactive teaching strategy for efficient knowledge distillation. In the distillation process, the interaction between the teacher network and the student one is implemented by swapping-in operations: randomly replacing the blocks in the student network with the corresponding blocks in the teacher network. In this way, we directly involve the teacher’s powerful feature transformation ability for largely boosting the performance of the student network. Experiments with typical settings of teacher-student networks demonstrate that the student networks trained by our IAKD achieve better performance than those trained by conventional knowledge distillation methods on diverse image classification datasets.
A multi-strategy improved sparrow search algorithm

Abstract As a novel algorithm, the sparrow search algorithm has better optimization performance than other intelligent optimization algorithms. However, in complex problems, there is still the possibility of falling into a local optimum and relying on the initial population stage. In response to these shortcomings, a multi-strategy improved sparrow search algorithm (KLSSA) is proposed. First, in the initial population stage, K-means clustering method is used to cluster and differentiate the individual positions of sparrows, which speeds up the work efficiency of the population and gets rid of the influence of randomness. Then, the levy flight mechanism and adaptive local search strategy are respectively introduced in the calculation of the location update of the discoverer and the follower, so that the discoverer can conduct a wide range of searches more flexibly, and the follower has a more detailed search method. Through the 10 standard test functions, it can be seen that the multi-strategy improved sparrow search algorithm has stronger optimization ability and better optimization speed.
Identifying tampering operations in image operator chains based on decision fusion

There has been great interest in image forensics in recent years. However, most of the existing research focuses on detecting a certain tampering operation, which means that the introduced features usually depend on the investigated operation and only binary classification is considered. Given the case where the image tampering process involves diverse processing operations, we propose a decision fusion method for identifying tampering operations in operator chains in this work. The proposed method permits the integration of knowledge provided by available image forensic algorithms. Under this method, a similarity coefficient function is introduced to assign the weight of the output of each forensic classifier. Then, we utilize a combination rule based on local conflict management to merge these outputs. Comparison with the previous works shows an improvement in operations identification accuracy when an image has experienced multiple falsifications.
LegalDB: Long DistilBERT for Legal Document Classification

Transformers have caused a paradigm shift in tasks related to natural language. From text summarization to classification, these models have established new state-of-the-art results on various general and closed domain tasks. Having said that, most of the popular transformer based models (BERT - Bidirectional Encoder Representations from Transformers, DistilBERT, and RoBERTa) face a limitation in terms of the content length they can accept. This is because the self-attention used by these models scales quadratically with the context sequence length. While this works well for short passages and questions, for longer documents this method fails to effectively capture both the local and global contexts. This shortcoming is underscored further for closed domain tasks like Legal Document classification, where the length of the documents can extend up to a couple of pages and which differ in their vocabulary from general English substantially. In this paper, a new architecture is proposed, where the concept of "long" attention is applied to a distilled BERT and then the model is pre-trained on legal-domain specific corpora. This helps combine a local windowed attention with task-motivated global attention, making the model contextually-aware for longer sequences. The proposed model is also able to outperform fine-tuned BERT and other transformer-based models at the task of legal document classification while also being faster.
Using Selective Memoization to Defeat Regular Expression Denial of Service (ReDoS)

Regular expressions (regexes) are a denial of service vector in most mainstream programming languages. Recent empirical work has demonstrated that up to 10% of regexes have super-linear worst-case behavior in typical regex engines. It is therefore not surprising that many web services are reportedly vulnerable to regex denial of service (ReDoS). If the time complexity of a regex engine can be reduced transparently, ReDoS vulnerabilities can be eliminated at no cost to application developers. Unfortunately, existing ReDoS defenses — replacing the regex engine, optimizing it, or replacing regexes piecemeal — struggle with soundness and compatibility. Full memoization is sound and compatible, but its space costs are too high. No effective ReDoS defense has been adopted in practice. We present techniques to provably eliminate super-linear regex behavior with low space costs for typical regexes. We propose selective memoization schemes with varying space/time tradeoffs. We then describe an encoding scheme that leverages insights about regex engine semantics to further reduce the space cost of memoization. We also consider how to safely handle extended regex features. We implemented our proposals and evaluated them on a corpus of real-world regexes. We found that selective memoization lowers the space cost of memoization by an order of magnitude for the median regex, and that run-length encoding further lowers the space cost to constant for 90% of regexes. "Those who cannot remember the past are condemned to repeat it." –George Santayana
Accelerated grey wolf optimization for global optimization problems

Grey Wolf Optimizer (GWO) belongs to the family of swarm intelligence and population-based algorithm which mimics the social behaviors of gray wolf packs. Due to its ease and viability, various researchers from different domains applied GWO to solve their research problems. However, GWO ought to have solid yet adjusted strengthening and broadening procedure to upgrade its execution. This paper introduces a novel SI algorithm, namely Accelerated gray Wolf Optimization (AGWO) which incorporates the enhanced hierarchy into GWO technique. Firstly, we introduce a mathematical model to strengthen the local and global search process. Then, we propose a diversity measure to eradicate the local confinement and to keep perfect balance between intensification and diversification process. Further, parameter tuned strategy is incorporated to speed up the convergence rate. The proposed methodology is experimented using MATLAB and tested with twenty-three mathematical benchmark problems (such as unimodal, multimodal and fixed dimensional multimodal functions). The search capability of the AGWO is compared with the generic GWO and various state-of-art swarm intelligence algorithms. Empirical results expose that AGWO provides better performance as per the standard deviation, best value and convergence curve compared to other algorithms. The sensitivity study of the proposed AGWO algorithm provides better results for 19 benchmark functions out of 23 benchmark functions in terms of minimal computation time and better convergence rate.
Histopathological image classification based on cross-domain deep transferred feature fusion

Transfer learning-based methods for breast cancer histopathological images classification are widely used for computer-aided cancer diagnosis. However, most existing methods directly migrate the model pre-trained on natural images to medical images with little consideration of the differences in the distribution of source and target domain data features. And combining multi-layer features can obtain more discriminative features which is helpful for improving the classification performance. In this paper, a novel histopathological images classification method based on cross-domain transfer learning and multi-stage feature fusion (Cd-dtffNet) is proposed. First, a novel network with residual learning is designed which can extract features from multiple levels. Then, the ability of the network to extract local features is learned by migrating from the source domain to the bridge domain, and the ability to extract global features is learned by migrating from the bridge domain to the target domain. Moreover, a feature fusion strategy with the L2 regularization term is utilized to fuse the extracted local and global features. The proposed method Cd-dtffNet was tested on the breast cancer histopathological image dataset. Experimental results (normal vs malignant: 99.09 %, normal vs uninvolved: 97.71 %, normal vs malignant + uninvolved: 98.27 %) demonstrate effectiveness of the proposed method for breast cancer classification in clinical diagnosis.
Artificial neural network-based virtual machine allocation in cloud computing

The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and electricity. With the help of virtualization, cloud computing helps in transparently sharing of data and services between the cloud users and provides access to more than thousands of computers as a single instance. The virtual machine allocation is a challenging task in virtualization which is regulated as a crucial part of the virtual machine (VM) migration. It is done to discover the best assignment of VMs on physical machines (PMs) because it has explicit effects on resource utilization, energy efficiency, the performance of running applications, etc. So, there is a need to design an effective method for the VM placement problem. The research paper presents a virtual machine allocation technique implementing Enhanced-Modified Best Fit Decreasing (E-MBFD) Algorithm. The outcome of the proposed technique is the cross-validation of allocated virtual machines on physical machines using Artificial Neural Network (ANN). Also, the proposed technique is implemented with the merits of finding the false allocations which happen due to inefficient utilization of resources and it helps in re-allocation of these VMs. The empirical result shows that the E-MBFD technique outperforms in terms of minimized power consumption and a lesser number of service-level agreement (SLA) violations in contrast with the conventional technique.
Enabling Fast Public Auditing and Data Dynamics in Cloud Services

Public auditing enables efficient integrity checks of data assigned to cloud servers. In this article, we revisit the public auditing for encrypted data, in which a major concern is how to effectively support data dynamics, i.e., data modification, insertion, and deletion. We first determine which factor in existing auditing schemes most limits data dynamics from a cost perspective. We then propose a novel public auditing scheme that provides data dynamics that are orders of magnitude faster than previous methods. Our auditing challenge-response protocol reduces the computation cost of the third-party auditor (TPA) significantly, thus increasing the verification speed for the auditing results. Performance and security analysis demonstrates that the proposed scheme generates minimal computation costs while guaranteeing data integrity and privacy against an untrusted cloud.
How to Meet Ternary LWE Keys

The LWE problem with its ring variants is today the most prominent candidate for building efficient public key cryptosystems resistant to quantum computers. NTRU-type cryptosystems use an LWE-type variant with small max-norm secrets, usually with ternary coefficients from the set $$\{-1,0,1\}$$ . The presumably best attack on these schemes is a hybrid attack that combines lattice reduction techniques with Odlyzko's Meet-in-the-Middle approach. Odlyzko's algorithm is a classical combinatorial attack that for key space size $$\mathcal{S}$$ runs in time $$\mathcal{S}^{0.5}$$ . We substantially improve on this Meet-in-the-Middle approach, using the representation technique developed for subset sum algorithms. Asymptotically, our heuristic Meet-in-the-Middle attack runs in time roughly $$\mathcal{S}^{0.25}$$ , which also beats the $$\mathcal{S}^{\frac{1}{3}}$$ complexity of the best known quantum algorithm. For the round-3 NIST post-quantum encryptions NTRU and NTRU Prime we obtain non-asymptotic instantiations of our attack with complexity roughly $$\mathcal{S}^{0.3}$$ . As opposed to other combinatorial attacks, our attack benefits from larger LWE field sizes q, as they are often used in modern lattice-based signatures. For example, for BLISS and GLP signatures we obtain non-asymptotic combinatorial attacks around $$\mathcal{S}^{0.28}$$ . Our attacks do not invalidate the security claims of the aforementioned schemes. However, they establish improved combinatorial upper bounds for their security. We leave it is an open question whether our new Meet-in-the-Middle attack in combination with lattice reduction can be used to speed up the hybrid attack.
Leveraging developer information for efficient effort-aware bug prediction

Software bug prediction techniques can provide informative guidance in software engineering practices. Over the past 15 years, developer information has been intensively used in bug prediction as features or basic data source to construct other useful models. Further leverage developer information from a new and straightforward perspective to improve effort-aware bug prediction. We propose to investigate the direct relations between the number of developers and the probability for a file to be buggy. Based on an empirical study on nine open-source Java systems with 32 versions, we observe a widely-existed and interesting tendency: when there are more developers working on a source file, there will be a stronger possibility for this file to be buggy. Based on the observed tendency, we propose an unsupervised algorithm and a supervised equation both called top-dev to improve effort-aware bug prediction. The key idea is to prioritize the ranking of files, whose number of developers is large, in the suspicious file list generated by effort-aware models. Experimental results show that the proposed top-dev algorithm and equation significantly outperform the unsupervised and supervised baseline models (ManualUp, Rad, Rdd, Ree, CBS+, and top-core). Moreover, the unsupervised top-dev algorithm is comparable or superior to existing supervised baseline models. The proposed approaches are very useful in effort-aware bug prediction practices. Practitioners can use the top-dev algorithm to generate a high-quality and informative suspicious file list without training complex machine learning classifiers. On the other hand, when building supervised bug prediction model, the best practice is to combine existing models with the top-dev equation.