Reviewerly/exHarmony huggingface dataset:
Graph Signal Compression by Joint Quantization and Sampling

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and transmitted. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work, we study the joint design of graph signal sampling along with quantization, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Furthermore, we show how the proposed approach can be realized using graph filters combining elements corresponding the neighbouring nodes of the graph, thus facilitating distributed implementation at reduced complexity. Our numerical evaluations on both synthetic and real world data shows that the joint sampling and quantization method yields a compact finite bit representation of high-dimensional graph signals, which allows reconstruction of the original signal with accuracy within a small gap of that achievable with infinite resolution quantizers.
Several classes of PcN power functions over finite fields

Recently, a new concept called multiplicative differential cryptanalysis and the corresponding c-differential uniformity were introduced by Ellingsen et al. (2020), and then some low differential uniformity functions were constructed. In this paper, we further study the constructions of perfect c-nonlinear (PcN) power functions. First, we give a conjecture on all power functions to be PcN over GF(2m). Second, several classes of PcN power functions are obtained over finite fields of odd characteristic for c=−1 and our theorems generalize some results in Bartoli and Timpanella (2020), Hasan et al. (2021) and Zha and Hu (2021). Finally, the c-differential spectrum of a class of almost perfect c-nonlinear (APcN) power functions is determined.
A Novel Trademark Image Retrieval System Based on Multi-Feature Extraction and Deep Networks

Graphical Search Engines are conceptually used in many development areas surrounding information retrieval systems that aim to provide a visual representation of results, typically associated with retrieving images relevant to one or more input images. Since the 1990s, efforts have been made to improve the result quality, be it through improved processing speeds or more efficient graphical processing techniques that generate accurate representations of images for comparison. While many systems achieve timely results by combining high-level features, they still struggle when dealing with large datasets and abstract images. Image datasets regarding industrial property are an example of an hurdle for typical image retrieval systems where the dimensions and characteristics of images make adequate comparison a difficult task. In this paper, we introduce an image retrieval system based on a multi-phase implementation of different deep learning and image processing techniques, designed to deliver highly accurate results regardless of dataset complexity and size. The proposed approach uses image signatures to provide a near exact representation of an image, with abstraction levels that allow the comparison with other signatures as a means to achieve a fully capable image comparison process. To overcome performance disadvantages related to multiple image searches due to the high complexity of image signatures, the proposed system incorporates a parallel processing block responsible for dealing with multi-image search scenarios. The system achieves the image retrieval through the use of a new similarity compound formula that accounts for all components of an image signature. The results shows that the developed approach performs image retrieval with high accuracy, showing that combining multiple image assets allows for more accurate comparisons across a broad spectrum of image typologies. The use of deep convolutional networks for feature extraction as a means of semantically describing more commonly encountered objects allows for the system to perform research with a degree of abstraction.
Lexicase Selection for Multi-Task Evolutionary Robotics

In Evolutionary Robotics, Lexicase selection has proven effective when a single task is broken down into many individual parameterizations. Evolved individuals have generalized across unique configurations of an overarching task. Here, we investigate the ability of Lexicase selection to generalize across multiple tasks, with each task again broken down into many instances. There are three objectives: to determine the feasibility of introducing additional tasks to the existing platform; to investigate any consequential effects of introducing these additional tasks during evolutionary adaptation; and to explore whether the schedule of presentation of the additional tasks over evolutionary time affects the final outcome. To address these aims we use a quadruped animat controlled by a feed-forward neural network with joint-angle, bearing-to-target, and spontaneous sinusoidal inputs. Weights in this network are trained using evolution with Lexicase-based parent selection. Simultaneous adaptation in a wall crossing task (labelled wall-cross) is explored when one of two different alternative tasks is also present: turn-and-seek or cargo-carry. Each task is parameterized into 100 distinct variants, and these variants are used as environments for evaluation and selection with Lexicase. We use performance in a single-task wall-cross environment as a baseline against which to examine the multi-task configurations. In addition, the objective sampling strategy (the manner in which tasks are presented over evolutionary time) is varied, and so data for treatments implementing uniform sampling, even sampling, or degrees of generational sampling are also presented. The Lexicase mechanism successfully integrates evolution of both turn-and-seek and cargo-carry with wall-cross, though there is a performance penalty compared to single task evolution. The size of the penalty depends on the similarity of the tasks. Complementary tasks (wallcross/turn-and-seek) show better performance than antagonistic tasks (wall-cross/cargo-carry). In complementary tasks performance is not affected by the sampling strategy. Where tasks are antagonistic, uniform and even sampling strategies yield significantly better performance than generational sampling. In all cases the generational sampling requires more evaluations and consequently more computational resources. The results indicate that Lexicase is a viable mechanism for multitask evolution of animat neurocontrollers, though the degree of interference between tasks is a key consideration. The results also support the conclusion that the naive, uniform random sampling strategy is the best choice when considering final task performance, simplicity of implementation, and computational efficiency.
Better Bang for the Buck? Generalizing Trust in Online Drug Markets

Abstract Past research into illicit online markets suggests that trust is produced by governance, reputation systems and the formation of social ties. In this paper, we draw on accounts of abstract and institutional trust, examining whether using a market is associated with general positive beliefs about product quality. Using data from the 2018 Global Drug Survey (n = 25,471) we utilize propensity score matching and multilevel linear regression to examine the association between having purchased drugs online and general expectations about product quality in value, weight, purity and price. We find strong evidence of a positive association between general beliefs and individual experience. This suggests that trust in illicit online markets can extend beyond interpersonal relations and towards an abstract market.
Dual attention mechanism network for lung cancer images super-resolution

Currently, the morbidity and mortality of lung cancer rank first among malignant tumors worldwide. Improving the resolution of thin-slice CT of the lung is particularly important for the early diagnosis of lung cancer screening.Aiming at the problems of network training difficulty and low utilization of feature information caused by the deepening of network layers in super-resolution (SR) reconstruction technology, we propose the dual attention mechanism network for single image super-resolution (SISR). Firstly, the feature of a low-resolution image is extracted directly to retain the feature information. Secondly, several independent dual attention mechanism modules are constructed to extract high-frequency details. The introduction of residual connections can effectively solve the gradient disappearance caused by network deepening, and long and short skip connections can effectively enhance the data features. Furthermore, a hybrid loss function speeds up the network's convergence and improves image SR restoration ability. Finally, through the upsampling operation, the reconstructed high-resolution image is obtained.The results on the Set5 dataset for 4 × enlargement show that compared with traditional SR methods such as Bicubic, VDSR, and DRRN, the average PSNR/SSIM is increased by 3.33 dB / 0.079, 0.41 dB / 0.007 and 0.22 dB / 0.006 respectively. The experimental data fully show that DAMN can better restore the image contour features, obtain higher PSNR, SSIM, and better visual effect.Through the DAMN reconstruction method, the image quality can be improved without increasing radiation exposure and scanning time. Radiologists can enhance their confidence in diagnosing early lung cancer, provide a basis for clinical experts to choose treatment plans, formulate follow-up strategies, and benefit patients in the early stage.
Quantum search on noisy intermediate-scale quantum devices

Abstract Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Therefore, Grover's algorithm can be improved for NISQ devices. In this paper, we demonstrate how to implement quantum search algorithms better on NISQ devices. We present detailed benchmarks of the five-qubit quantum search algorithm on different quantum processors, including IBMQ, IonQ, and Honeywell quantum devices. We report the highest success probability of the five-qubit search algorithm compared to previous works. Our results show that designing the error-aware quantum search algorithms is possible, which can maximally harness the power of NISQ computers.
Ising Machines for Diophantine Problems in Physics

Diophantine problems arise frequently in physics, in for example anomaly cancellation conditions, string consistency conditions and so forth. We present methods to solve such problems to high order on annealers that are based on the quadratic Ising Model. This is the intrinsic framework for both quantum annealing and for common forms of classical simulated annealing. We demonstrate the method on so-called Taxicab numbers (discovering some apparently new ones), and on the realistic problem of anomaly cancellation in $U(1)$ extensions of the Standard Model.
Detection of a new crescent moon using the Maximally Stable Extremal Regions (MSER) technique

Visibility of a new crescent moon is crucial for Muslims when declaring the beginning of the Islamic calendar. For many years, astronomers had observed the visibility of the new crescent moon using various techniques, such as the naked eye and optical instruments. In line with technological developments, astronomers have changed their observation technique to using astronomical imaging with the aid of a digital camera attached to a telescope. This helps to capture and save the images of the new crescent moon. However, the new crescent moon image has presented difficulties to astronomers as it visibility in the image is difficult to detect due to a low contrast with the sky. Hence, the captured image could not be presented as evidence of its visibility. This study proposes the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement. Then, the bilateral filter for image denoising was applied and lastly, the Maximally Stable Extremal Regions (MSER) detector was used to detect the new crescent moon region in the image. The result shows that this proposed method has acceptable practical results. The new crescent moon images can be enhanced successfully using CLAHE and the bilateral filter technique even when the new crescent moon is not visible to the naked eye. The new crescent moon region can be well detected with the MSER detector although MSER does not perform well on the blurry region of new crescent moon corner. This technique can help astronomers detect the new crescent moon’s visibility for declaring the beginning month of the Islamic calendar.
Diversified branch fusion for self-knowledge distillation

Knowledge distillation improves the performance of a compact student network by adding supervision from a pre-trained cumbersome teacher network during training. To avoid the resource consumption of acquiring an extra teacher network, the self-knowledge distillation designs a multi-branch network architecture with shared layers for teacher and student models, which are trained collaboratively in a one-stage manner. However, this method ignores the knowledge of shallow branches and rarely provides diverse knowledge for effective collaboration of different branches. To solve these two shortcomings, this paper proposes a novel Diversified Branch Fusion approach for Self-Knowledge Distillation (DBFSKD). Firstly, we design lightweight networks for adding to the middle layers of the backbone. They capture discriminative information by global–local attention. Then we introduce a diversity loss between different branches to explore diverse knowledge. Moreover, the diverse knowledge is further integrated to form two knowledge sources by a Selective Feature Fusion (SFF) and a Dynamic Logits Fusion (DLF). Thus, the significant knowledge of shallow branches is efficiently utilized and all branches learn from each other through the fused knowledge sources. Extensive experiments with various backbone structures on four public datasets (CIFAR100, Tiny-ImageNet200, ImageNet, and RAF-DB) show superior performance of the proposed method over other methods. More importantly, the DBFSKD achieves even better performance with fewer resource consumption than the baseline.
Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand

Abstract Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made.
Patient-Centric Multichain Healthcare Record

In this paper, the authors have amplified the concept that EHRs need to be patient-centric and patient-driven, that is the patient should be the real owner as well as the manager of his medical records. The authors propose patient-centric multichain healthcare record (PCMHR) that implements health records using smart contracts on ethereum blockchain and also utilizes the multichain framework - Polygon. PCMHR can concurrently implement blockchain functionality while addressing the concerns of interoperability among authorized hospitals and patient health information confidentiality that damages our healthcare system. The authors propose a solution to fully decentralize the current medical healthcare system by storing PCMHR on IPFS (InterPlanetary File System) to resolve the limitation of blockchain-based applications in scalability and high cost. The authors have depicted the cost and time analysis of transactions on the polygon framework to give a clear view of this multichain framework and its advantages over the ethereum blockchain.
Forward/Backward and Content Private DSSE for Spatial Keyword Queries

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, <inline-formula><tex-math notation="LaTeX">$\mathsf {DSSE}_{\mathsf {SKQ}}$</tex-math></inline-formula> ) and extend the DSSE leakage functions to capture the leakages in <inline-formula><tex-math notation="LaTeX">$\mathsf {DSSE}_{\mathsf {SKQ}}$</tex-math></inline-formula> . Then, we present a practical <inline-formula><tex-math notation="LaTeX">$\mathsf {DSSE}_{\mathsf {SKQ}}$</tex-math></inline-formula> construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis proves that our construction can achieve not only forward/backward privacy but content privacy as well, which can resist the most existing leakage-abuse attacks. Evaluation results using real-world datasets demonstrate the efficiency and feasibility of our construction. Comparative analysis reveals that our construction outperforms state-of-the-art schemes in terms of privacy and performance, e.g., our construction is <inline-formula><tex-math notation="LaTeX">$175\times$</tex-math></inline-formula> faster than existing schemes with only 51% server storage cost.
Making You Only Look Once Faster: Toward Real-Time Intelligent Transportation Detection

We present in this article a simple yet efficient algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">you only look once: dynamic and stem</i> (YOLO-DS), which can better complete real-time intelligent transportation detection. YOLO-DS is accomplished based on YOLOv5s through the following primary modifications. First, we apply a dynamic mechanism in the backbone. The dynamic mechanism enables the model to be a multibranch model during training and a single-path model during inference and deployment. Hence, our model can enjoy the benefits of fast speed and economical memory while maintaining excellent performance. Second, the original focus module in the backbone is inefficient. Accordingly, we employ the stem module (built entirely with standard convolution) instead of the focus module in the backbone. Finally, we modify the width and depth of the model and redesign a new, lighter detection head to improve the model further. Compared to the original YOLOv5s, YOLO-DS improves the mean average precision (AP) by 2.7 and 0.2 on the University at Albany DETection and tRACking and the high-speed train fault dataset. In addition, experiments conducted on various devices show that the speed of YOLO-DS is highly impressive, far exceeding previous lightweight neural networks. Specifically, the inference speed of YOLO-DS is twice that of the original YOLOv5s, up to 7.98 times. Moreover, our YOLO-DS-Tiny processes an image with 640 × 640 resolution averaging barely 35 ms on a cellphone (the Honor V20).
Modeling Perceived Quality on 8K VVC Video Under Various Screen Sizes and Viewing Distances

Perceptual video quality considerably affects the quality of experience (QoE) of watching television (TV) broadcasts.Viewing conditions, such as the screen size and viewing distance, impact the perceived quality.We performed subjective evaluation experiments on 8K (7,680×4,320) ultra-high definition (UHD) compressed videos under seven viewing conditions (combinations of 31.5-,55-, and 85inch displays and 0.75, 1.5, and 3.0 H (times of screen height) of viewing distance).Distorted videos compressed by the versatile video coding (VVC)/H.266were used in four types of encoding resolution, from 2K (1,920×1,080) to 8K, at a wide bitrate settings range of 3-80 Mbps.We derived a simple regression equation predicting the mean opinion score (MOS) using the hierarchical linear model (HLM), investigating the factors influencing subjective video quality.In this equation, MOS is expressed as a linear combination of terms including intercept and bitrate associated with sequence and encoding resolution, screen size, and viewing distance; it indicates that the smaller the screen, or the further the viewing distance, the fewer artifacts are perceived, as following empirical rules.Furthermore, we confirmed that the derived model is accurate as the Pearson linear and Spearman rank order correlation coefficients between predicted and actual MOS values were more than 0.97.
Developing Novel Activation Functions Based Deep Learning LSTM for Classification

This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation function.The LSTM networks have high performance in solving the vanishing gradient problem that is observed in recurrent neural networks.Performance investigations were carried out utilizing three distinct deep learning optimization algorithms to evaluate the efficiency of the proposed state activation functions-based LSTM classifiers for two different classification tasks.The simulation results demonstrate that the proposed classifiers that use the Modified Elliott, Softsign, Sech, Gaussian, Bitanh1, Bitanh2 and Wave as state activation functions trump the tanh-based LSTM classifiers in terms of classification accuracy.The proposed classifiers are encouraged to be utilized and tested for other classification tasks.