Reviewerly/exHarmony huggingface dataset:
Image shift correction, noise analysis, and model fitting of (cathodo-)luminescence hyperspectral maps

Hyperspectral imaging is an important asset of modern spectroscopy. It allows us to perform optical metrology at a high spatial resolution, for example in cathodoluminescence in scanning electron microscopy. However, hyperspectral datasets present added challenges in their analysis compared to individually taken spectra due to their lower signal to noise ratio and specific aberrations. On the other hand, the large volume of information in a hyperspectral dataset allows the application of advanced statistical analysis methods derived from machine-learning. In this article, we present a methodology to perform model fitting on hyperspectral maps, leveraging principal component analysis to perform a thorough noise analysis of the dataset. We explain how to correct the imaging shift artifact, specific to imaging spectroscopy, by directly evaluating it from the data. The impact of goodness-of-fit-indicators and parameter uncertainties is discussed. We provide indications on how to apply this technique to a variety of hyperspectral datasets acquired using other experimental techniques. As a practical example, we provide an implementation of this analysis using the open-source Python library hyperspy, which is implemented using the well established Jupyter Notebook framework in the scientific community.
EnclavePoSt: A Practical Proof of Storage-Time in Cloud via Intel SGX

Data integrity is one of the most critical security concerns for users when using the cloud storage service. However, it is difficult for users to always stay online and frequently interact with storage service providers to ensure continuous data integrity in practice. The existing Proof of Storage-time schemes, enabling verifiable continuous data integrity checking at cost of performance, fail to provide flexible storage period, reliable measurement of storage time, and resistance to the outsourcing attack. In this paper, we propose EnclavePoSt, the first practical Proof of Storage-time via Intel SGX, where the data integrity checking can be automatically executed in a hardware-driven Trusted Execution Environment (TEE), i.e., the enclave, when users are offline. The checking results can be aggregated and efficiently verified by users. Besides, the elapsed time during isolated data integrity checking can be precisely measured, and the storage period is allowed to flexibly change. Lastly, our EnclavePoSt is resistant to the outsourcing attack. The security analysis and evaluations justify that the EnclavePoSt is more practical than previous works.
Development of a Face Prediction System for Missing Children in a Smart City Safety Network

Cases of missing children not being found are rare, but they continue to occur. If the child is not found immediately, the parents may not be able to identify the child’s appearance because they have not seen their child for a long time. Therefore, our purpose is to predict children’s faces when they grow up and help parents search for missing children. DNA paternity testing is the most accurate way to detect whether two people have a blood relation. However, DNA paternity testing for every unidentified child would be costly. Therefore, we propose the development of the Face Prediction System for Missing Children in a Smart City Safety Network. It can predict the faces of missing children at their current age, and parents can quickly confirm the possibility of blood relations with any unidentified child. The advantage is that it can eliminate incorrect matches and narrow down the search at a low cost. Our system combines StyleGAN2 and FaceNet methods to achieve prediction. StyleGAN2 is used to style mix two face images. FaceNet is used to compare the similarity of two face images. Experiments show that the similarity between predicted and expected results is more than 75%. This means that the system can well predict children’s faces when they grow up. Our system has more natural and higher similarity comparison results than Conditional Adversarial Autoencoder (CAAE), High Resolution Face Age Editing (HRFAE) and Identity-Preserved Conditional Generative Adversarial Networks (IPCGAN).
Improving CT-image universal lesion detection with comprehensive data and feature enhancements

As a crucial task in Computer Vision, object detection has substantially improved in recent years, with the aid of deep learning and increasingly abundant datasets. However, compared with natural image detection, medical CT images require more precision due to the obvious clinical implications. Detecting multiple lesions or clusters with relatively few training samples and indistinctive feature representation is extremely problematic. In this paper, we propose comprehensive improvements to the original YOLOv3, such as data augmentation, feature attention enhancement and feature complementarity enhancement to increase general lesion area detection performance. Ablation studies use the open DeepLesion dataset to validate these improvements and confirm the effectiveness of each amendment. Comparisons between state-of-the-art counterparts demonstrated that the proposed lesion object detector has enhanced salient accuracy (under two commonly used metrics) and an exceptional speed-accuracy trade-off. The proposed model achieved 57.5% mAP and 85.07% sensitivity at 4 false positives (FPs) per image, while running at reliable 35.6 frames per second (FPS). These findings indicate that the proposed detector is more practicable than other currently available computer aided diagnostics (CAD).
FPGA acceleration of deep reinforcement learning using on-chip replay management

A major bottleneck in parallelizing deep reinforcement learning (DRL) is in the high latency to perform various operations used to update the Prioritized Replay Buffer on CPU. The low arithmetic intensity of these operations leads to severe under-utilization of the SIMT computation power of GPUs. In this work, we propose a high-throughput on-chip accelerator for Prioritized Replay Buffer and learner that efficient allocates computation and memory resources to saturate the FPGA computation power. Our design features hardware pipelining on FPGA such that the latency of replay operations is completely hidden. Our experimental results show that the performance of the key operations in managing Prioritized Replay Buffer including sampling and priority insertions are improved by factor of 21X ~ 40X compared with the state-of-the-art implementations on CPU and GPU. In addition, our system design leads to up to 4.3X improvement in overall throughput compared with the state-of-the-art CPU-GPU implementations.
cuPSO

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and analyzed to find the best solution. In particular, the reduction-based method is considered as a common approach to handle the data aggregation and analysis for the calculated particle information. Nevertheless, based on our analysis, the reduction-based method would suffer from excessive memory accesses and thread synchronization overheads. In this paper, we propose a novel algorithm to alleviate the above overheads with the atomic functions. The threads within a thread group update the calculated results atomically to the intra-group data queue conditionally, which prevents the frequent accesses to the memory as done by the parallel reduction operations. Furthermore, we develop an enhanced version of the algorithm to alleviate the synchronization barrier among the thread groups, which is achieved by allowing the thread groups to run asynchronously and updating to the global, lock-protected variables occasionally if necessary. Our experimental results show that our proposed algorithm running on the Nvidia GPU is about 200 times faster than the serial version executed by the Intel Xeon CPU. Moreover, the novel algorithm outperforms the state-of-the-art method (the parallel reduction approach) by a factor of 2.2.
Searching for meaning: Local scene semantics guide attention during natural visual search in scenes

Models of visual search in scenes include image salience as a source of attentional guidance. However, because scene meaning is correlated with image salience, it could be that the salience predictor in these models is driven by meaning. To test this proposal, we generated meaning maps that represented the spatial distribution of semantic informativeness in scenes, and salience maps which represented the spatial distribution of conspicuous image features and tested their influence on fixation densities from two object search tasks in real-world scenes. The results showed that meaning accounted for significantly greater variance in fixation densities than image salience, both overall and in early attention across both studies. Here, meaning explained 58% and 63% of the theoretical ceiling of variance in attention across both studies, respectively. Furthermore, both studies demonstrated that fast initial saccades were not more likely to be directed to higher salience regions than slower initial saccades, and initial saccades of all latencies were directed to regions containing higher meaning than salience. Together, these results demonstrated that even though meaning was task-neutral, the visual system still selected meaningful over salient scene regions for attention during search.
A cross frame post-processing strategy for video object detection

Video-based object detection plays an important role in the real world and scientific research. Compared with still images, video detection is more challenging due to occlusion, rare poses, high-speed movement, frames loss, etc. In order to improve the existing video stream detectors widely and with low coupling, a post-processing strategy, CFPP, is proposed in this work. The framework can establish a cross frame link based on deep learning, connect the proposals belonging to the same object, and improve the performance of the detector by optimizing the classification confidence and object coordinates. Furthermore, CFPP can connect the proposals in adjacent and non adjacent frames at the same time, which makes it exploit the context information of video stream more effectively than other post-processing strategies. Experiments shows that CFPP can improve the existing detectors (e.g. we improve the mAP of YOLOv4 on ImageNet VID dataset form 69.24% to 78.15%). In addition, experiments show that the designed framework can achieve better detection effect than other strategies in the case of high-speed moving object and frames loss.
Generation of Dynamically Feasible Window Traversing Quadrotor Trajectories Using Logistic Curve

Abstract This work considers dynamically feasible point-to-point trajectory generation problem for a quadrotor flying through a constrained planner region referred as window (narrow gap). A four parameter logistic (4PL) curve is investigated as a prospective candidate and closed-form conditions are derived on the 4PL design parameters to satisfy the window traversability and vehicle dynamic feasibility constraints. A hierarchical approach first computes a dynamically feasible design parameter set for decoupled trajectory components and then obtains a solution set satisfying 3-D axis-coupled window traversability conditions. Numerical examples with a comparative study are presented to validate the analytical findings that highlight the quick computation of the dynamically feasible window traversing trajectories in complex window scenarios.
Hybrid Recommendation System Based on Collaborative and Content-Based Filtering

A Recommendation System (RS) is a method which filters the information and helped users’ to choose the corresponding target from the huge amount of information obtainable in online. The system recommends useful and satisfactory products (items) such as books, music, jokes, and movies for targeting users based on their interest. The content-based filtering as well as collaborative are different systems used often while designing the RS that predicts the recommended item(s) based upon the user preferences. However, the collaborative filtering algorithm provides poor performance for data sparsity, and it is complex for tracking the change of user interest. Moreover, the hybrid system has combined both the techniques in multiple ways to overcome the shortcomings and optimize the outcomes. Thereby, this article plans to develop a new hybrid recommender system assisting with the optimization concept for optimal recommendation list based on user preference or interest. At first, the feature extraction process takes place, in which the content features and the collaborative features are extracted based on (a) profile construction, (b) content similarity index, (c) Neighbor finder, (d) Items generator, and (e) Items weight generator and variance generator. Consequently, the optimal recommendation is carried out on the basis of features extracted. Further, the developed work plans to carry out the optimal rating of recommendation using a FireFly with Weighted Crow Search Algorithm (FF-WCSA). At last, the outcomes of the developed model is computed to extant approaches in terms of various metrics like accuracy, FDR, MAE, MARE, MSE, MSRE, RMSE, and RMSRE, respectively.
Post-Quantum Cipher Power Analysis in Lightweight Devices

Post-quantum ciphers (PQC) provide cryptographic algorithms for public-key ciphers which are computationally secure against the threats from quantum-computing adversaries. Because the devices in mobile computing are limited in hardware and power, we analyze the PQC power overheads. We implement the new NIST PQCs across a range of device platforms to simulate varying resource capabilities, including multiple Raspberry Pis with different memories, a laptop, and a desktop computer. We compare the power measurements with the idle cases as our baseline and show the PQCs consume considerable power. Our results show that PQC ciphers can be feasible in the resource-constrained devices (simulated with varying Raspberry Pis in our case); while PQCs consume greater power than the classical cipher of RSA for laptop and desktop, they consume comparable power for the Raspberry Pis.
Low-latency implementation of the GIFT cipher on RISC-V architectures

Lightweight cryptography is a viable solution for constrained computational environments that require a secure communication channel. To standardize lightweight primitives, NIST has published a call for algorithms that address needs like compactness, low-latency, low-power/energy, low-energy etc. Among the candidates, the GIFT family of block ciphers was utilized in various NIST candidates due to its high security margin and small gate footprint. As a result of their hardware-oriented design, software implementations of GIFT require additional optimization techniques such as bitslicing and fixslicing in order to achieve optimal performance. Even though, the performance of these methods has been assessed for several ISA families such as x86 and ARM, there is currently a lack of data with regards to their acceleration capabilities for RISC-V. Since this ISA is an important element of the growing open-hardware movement, our goal is to address this knowledge gap. Therefore, we have developed several assembly implementations for both GIFT-64 and GIFT-128, using the RV32I ISA, and performed a quantitative assessment of their performance using a physical board i.e., Hifive1 Rev B. Our study has shown that by using bitslicing the number of clock cycles can be reduced by 69.33% for GIFT-64 and 71.38% for GIFT-128, compared to a naive assembly implementation, while fixslicing can decreases the number of clock cycles by 85.7% (GIFT-64) and 81.28% (GIFT-128). Nonetheless, the preferred technique is fixslicing with key pre-computation, which can achieve a reduction of 88.69% (GIFT-64) and 95.05% (GIFT-128), while maintaining relatively low memory requirements of 938 bytes (GIFT-64) and 1388 bytes (GIFT-128), respectively.
A similarity measure for Straight Line Programs and its application to control diversity in Genetic Programming

Finding a balance between diversity and convergence plays an important role in evolutionary algorithms to avoid premature convergence and to perform a better exploration of the search space. In the case of Genetic Programming, and more specifically for symbolic regression problems, different mechanisms have been devised to control diversity, ranging from novel crossover and/or mutation procedures to the design of distance measures that help genetic operators to increase diversity in the population. In this paper, we start from previous works where Straight Line Programs are used as an alternative representation to expression trees for symbolic regression, and develop a similarity measure based on edit distance in order to determine how different the Straight Line Programs in the population are. This measure is used in combination with the CHC algorithm strategy to control diversity in the population, and therefore to avoid local optima to solve symbolic regression problems. The proposal is first validated in a controlled scenario of benchmark datasets and it is compared with previous approaches to promote diversity in Genetic Programming. After that, the approach is also evaluated in a real world dataset of energy consumption data from a set of buildings of the University of Granada.
Structured encryption for knowledge graphs

We investigate the problem of structured encryption (STE) for knowledge graphs (KGs) where the knowledge of data can be efficiently and privately queried. Presently, the application of natural language processing (NLP) for knowledge-based search is gradually emerging. Compared with the traditional search based only on keywords of documents—symmetric searchable encryption (SSE), the knowledge-based search system transforms the latent knowledge contained in documents into a semantic network as a knowledge base, which greatly improves the accuracy and relevance of search results. In order to develop a knowledge-based search, the contents of documents are analyzed and extracted using KG techniques (e.g. multi-relational graph (MG) and property graph (PG)), and then all encrypted nodes and edges in a KG constitute the entire index table and database. This paper proposes the first STE for KGs with CQA2-security to search on protected knowledge, where KGs include MGs and PGs. In general, the latter is more complex than the former, but it can represent more abundant knowledge. Experimental results show that the index construction time of our schemes is about 1.9s and the query time is about 190 ms. Our sensitivity analysis shows that the performance of our proposed schemes is greatly influenced by the number of edges and nodes, but less by the number of properties.
Discrimination of dephasing channels

The problem of dephasing channel discrimination is addressed for finite-dimensional systems. In particular, the optimization with respect to input states without energy constraint is solved analytically for qubit, qutrit and ququart. Additionally, it is shown that resorting to side entanglement assisted strategy is completely useless in this case.
Feature selection under fairness constraints

Learning from large dimensional data presents major challenges related to the size of the data. Thus, dimensionality reduction techniques such as feature selection are brought in to reduce computation time, improve prediction performance, and better understand the data. However, two problems can occur with current feature selection methods when protected features are considered. The presence of protected features among the selected ones which often lead to unfair results and the presence of redundant features which carry potentially the same information with the protected ones. By protected features we mean features on which it is important to not have bias due to data imbalances. In view of such issues, we introduce a fair feature selection method that takes into account the existence of protected features and their redundant. Our new method finds a set of relevant features with no protected features with the least possible redundancy under prediction quality constraint. This constraint consists of a trade-off between fairness and prediction performance. Below, we outline the strategy of our trade-off approach.
Efficient frame synchronization using a weak coherent state for continuous-variable quantum key distribution

Frame synchronization is a crucial step in quantum key distribution (QKD) and a prerequisite for secure key distillation. Due to the extremely low signal-to-noise ratio (SNR) and fast phase drift in the continuous-variable QKD system, efficient frame synchronization is highly demanded. In this paper, a frame-synchronization scheme based on weak coherent states is proposed. This scheme uses a train of coherent-state pulses encoded by a Zadoff-Chu sequence to achieve frame synchronization. The synchronization performance is analyzed under different transmission distances, transmit powers, frequency offsets, and phase drifts through Monte Carlo simulation. Its performance is also compared with other synchronization sequences, confirming its noise-tolerance capability. An optical experiment is conducted to verify the feasibility of the synchronization scheme under low SNR (as low as $\ensuremath{-}20$ dB), and the applicable SNR range can be further reduced by increasing the sequence length. Due to its efficiency and noise tolerance, this scheme can provide technical support for weak-coherent-state-based QKD and future quantum network construction.
Evaluation of Multimedia Popular Music Teaching Effect Based on Audio Frame Feature Recognition Technology

Music education should pay attention to popular music that exists in students’ real life and deeply affects them. Moreover, it needs to be combined with “popular classical music” to make them happily learn popular music, appreciate popular music artistically, and feel popular music aesthetically. This study combines the audio frame feature recognition technology to evaluate the effect of multimedia popular music teaching and improve the quality of multimedia popular music teaching. Moreover, this study adaptively revises the speech spectrum technology to construct a multimedia pop music system based on audio frame feature recognition technology. Finally, this study verifies the performance of this system through experimental research. According to the results of experimental research, it can be seen that the effect of the system proposed in this study is very good.