Reviewerly/exHarmony huggingface dataset:
Optimized active contor segmentation model for medical image compression

• At first, the collected input image is subjected to the image compression phase, which includes the 2 major phases: (a) segmentation and (b) encoding. In the image compression phase, input image is initially segmented by a new OACM. • The weighting factor λ and maximal iteration MA X it of ACM is fine-tuned by a new M−MBO model, which is the conceptual improvement of standard MBO. • At the end of OACM, the original image is split into ROI region and Non-ROI region. The ROI marked field is encoded using ISPIHT, where as the non-ROI area is encoded using DCT. In terms of bit-stream combination, the outcomes from both the ISPIHT algorithm and the DCT model are merged and the compressed image is its output. • Following that, the compressed image is decompressed that will counteract the compression mechanism. It encompasses bit-stream segmentation, which will be processed separately for the non-ROI and ROI regions using both ISPIHT decoded and DCT based decomposition. Finally, the original image is acquired in a more precise manner. Nowadays, medical imaging systems tend to have greatest impact on disease identification, diagnosis, and surgical preparation. To save hardware space and transmission bandwidth, it is important to reduce data redundancy in the image. Compressing images is very important for storage and transmitting purposes as it decreases the amount of bits required while retaining the critical information content encapsulated in the image document. This makes the system more peculiar in this field. The proposed paradigm image segmentation is the first step attained by the Optimized Active Contour Model (OACM). Using a new Modified marriage in honey bees optimization model (MMBO), ACM's weighting factor and maximum iteration are fine-tuned. Thereby, the collected input image is segmented into N -ROI and ROI. The ROI marked field will indeed be encoded using ISPIHT-based lossy compression model, whereas the non-ROI area is encoded using DCT based lossy compression model. In terms of BSC, the outcomes from both ISPIHT algorithm and DCT model are merged and the compressed image is its output. Then, the compressed image will then be subjected to image decompression. This will include bit-stream segregation, which will be processed separately for the ROI and non-ROI regions using ISPIHT decoder and DCT-based decomposition. This process results in the original image. A comparative evaluation is undergone between the proposed and the existing techniques under PSNR, SSIM, and CR. Accordingly, the PSNR of the Proposed model is (∼)0.22, which is 26 %, 50 %, 60 %, and 55 % superior to the conventional methods like the MFO, LA, MBO, and JCF-LA.
Hybrid Meta-heuristic Genetic Algorithm: Differential Evolution Algorithms for Scientific Workflow Scheduling in Heterogeneous Cloud Environment

The gaint cabailities of cloud computing in providing online services via Internet attract the attention of the distributed sector due to its huge abilities that include storage, processing, software, databases, and servers that are shared simultaneously over the Internet by remote users geographically dispersed. Increasing the enormous amount of generating data through big data platforms and the use of IoT devices connected via the network have exploited the computational power of the cloud. However, the high utilization of the cloud leads to a longer execution time for a specific task. This paper proposing the hybrid strategy of scheduling the workflow in cloud computing called Genetic Algorithm with Differential Evolution (GA-DE). This research aims to investigate how heterogeneous cloud computing affects workflow scheduling. This study is aimed at reducing makespan and verifying if the metaheuristic technology is more suitable for the distributed environment by comparing it to existing heuristics, such as HEFT-Downward Rank,HEFT-Upward Rank,HEFT-Level Rank, and meta-heuristic algorithm GA. The proposed algorithm is validated through extensive experiments compared to three scientific workflows (Epigenomics,Cybershake,and Montage). Based on the simulation result GA-DE algorithm proves its superiority against the other comparing algorithms in term of makespan. Furthermore, the conducted experiment proves that montage scientific workflow is more proper for executing workflow scheduling in heterogeneous cloud computing.
Identifying emotions toward an overweight avatar in Virtual Reality: The moderating effects of visuotactile stimulation and drive for thinness

Virtual reality technologies can be used to alter one’s body image by inducing ownership of an overweight virtual body. This illusion can possibly lead to both negative affective responses toward the virtual body as well as perceptual changes of one’s own body size, which are both scarcely examined with regard to the embodiment of an overweight avatar. This study’s fundamental aim is to investigate, among healthy weight participants, whether negative affect toward a virtual body and changes in body-size perception can be evoked when embodying an overweight virtual avatar. Additionally, the study investigates whether applying visuotactile stimulation (which has been identified as a proxy for virtual body ownership by previous studies) and drive for thinness influence the strength of these effects. A 2 (healthy weight vs. overweight virtual body) × 2 (congruent vs. incongruent visuotactile stimulation) between-subjects design was employed, with participants’ drive for thinness measured to test its potential moderating effect. ANOVAs revealed that participants ( N = 114) experienced significantly more negative affect toward the virtual body when embodying one that was overweight compared to a healthy weight virtual body. Visuotactile stimulation did not moderate this effect, even though the manipulation of visuotactile stimulation worked as intended (i.e., the experience of ownership over the virtual body was significantly higher in the congruent compared to incongruent visuotactile stimulation condition). Additionally, participants with a high drive for thinness did not experience significantly more negative affect than participants with a low drive for thinness. Embodiment of an overweight avatar did not affect body-size perceptions, nor did visuotactile stimulation or the drive for thinness moderate this effect. Knowing that embodiment of an overweight avatar results in negative affective responses toward it, future studies could deepen our understanding of the role of negative affect in the transfer of emotions to one’s own body, and the interplay between negative affect toward a virtual body and body-size perceptions.
Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most preferred screening technique, generally uses low-contrast images where identification of some of the mass lesions from surrounding normal tissues is a difficult task even for skilled radiologists. Therefore, to alleviate the issue, this paper introduces an effective computer-aided detection scheme based on a novel contrast enhancement scheme for mammograms to automate the localization procedure of mass cases. Hyperbolic tangent function, a simple yet effective method, is introduced as an enhancement transfer function whose adjustable parameter is optimized by the Tunicate swarm algorithm, a nature-inspired optimization algorithm, via fitness function. To minimize the false positives generated after the detection, the median robust extended local binary pattern, a texture-based descriptor, is introduced, which possesses the ability to capture both micro- and macro-structure information from an image patch. The analysis includes four classifiers: Fisher linear discriminant analysis, K-nearest neighbor, support vector machine, and an ensemble classifier in the feature-based classification for false positive reduction (FPR). The proposed approach of automatic detection of masses when verified using two standard databases, mini-MIAS and Digital Database for Screening Mammography, comprised 68 and 550 mammograms, respectively, achieved sensitivities of 95.3% and 94.1% with 0.36 and 0.40 false positives per image after false positive reduction.
Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients' state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.
Perceived China Threat, Conspiracy Belief, and Public Support for Restrictive Immigration Control During the COVID-19 Pandemic

During the COVID-19 pandemic, there has been an unprecedented increase in the level of political blame attributed to China for the health crisis and other associated xenophobic discourses. Although previous research has revealed the effects of perceived outgroup threats on popular support for policies that control outgroups, threats posed by foreign countries have received little empirical attention. This study advances previous research by focusing on the independent effects of the perceived China threat to public support for restrictive immigration control during the COVID-19 pandemic. Using a sample drawn from the 2020 American National Election Studies, the results showed that the perceived China threat substantially heightened public support for restrictive immigration control. More importantly, demand for restrictive immigration control intensified when individuals had stronger perceptions of threats and conspiracy beliefs about the virus leak from a Chinese lab. Taken together, our findings demonstrate the roles of politically constructed threats and conspiracy beliefs during the COVID-19 pandemic, revealing new mechanisms of popular attitudes toward social control.
Manipulating the quantum steering direction with sequential unsharp measurements

Quantum steering exhibits an inherent asymmetric property that differs from both quantum entanglement and Bell nonlocality, which leads to no-way, one-way, and two-way steering. Flexibly manipulating the direction of quantum steering without reducing the quantum steerability is very important for information processing. In this paper, implementing sequential unsharp measurements and the steering-radius criterion, we show how to manipulate that steering direction among multiple Alices and Bobs. We demonstrate that the change in steering direction depends on the number of measurement settings and the sharpness of measurements. For a given two-qubit asymmetric state, we obtain 13 kinds of steering regions with different steering directions in the case of one Alice and three Bobs and obtain 11 kinds in the case with two Alices and two Bobs. For the case with one Alice and three Bobs, we further find a volume bound to visualize quantum steering. Our manipulation scheme is experimentally easy to achieve and rather assigns the steerability to more observers without reducing it, which offers a direct reference for the practical application of quantum steering.
Faster Born probability estimation via gate merging and frame optimisation

Outcome probability estimation via classical methods is an important task for validating quantum computing devices. Outcome probabilities of any quantum circuit can be estimated using Monte Carlo sampling, where the amount of negativity present in the circuit frame representation quantifies the overhead on the number of samples required to achieve a certain precision. In this paper, we propose two classical sub-routines: circuit gate merging and frame optimisation, which optimise the circuit representation to reduce the sampling overhead. We show that the runtimes of both sub-routines scale polynomially in circuit size and gate depth. Our methods are applicable to general circuits, regardless of generating gate sets, qudit dimensions and the chosen frame representations for the circuit components. We numerically demonstrate that our methods provide improved scaling in the negativity overhead for all tested cases of random circuits with Clifford+$T$ and Haar-random gates, and that the performance of our methods compares favourably with prior quasi-probability simulators as the number of non-Clifford gates increases.
Saliency guided data augmentation strategy for maximally utilizing an object’s visual information

Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that do not contain objects might be included, which degrades performance. In this paper, we propose a novel augmentation method that mixes patches in a non-overlapping manner after they are extracted from the salient regions in an image. The suggested method can make effective use of object characteristics, because the constructed image consists only of visually important regions and is robust to noise. Since the patches do not occlude each other, the semantically meaningful information in the salient regions can be fully utilized. Additionally, our method is more robust to adversarial attack than the conventional augmentation method. In the experimental results, when Wide ResNet was trained on the public datasets, CIFAR-10, CIFAR-100 and STL-10, the top-1 accuracy was 97.26%, 83.99% and 82.40% respectively, which surpasses other augmentation methods.
PANC: Prototype Augmented Neighbor Constraint instance completion in knowledge graphs

Great attention has been devoted to knowledge graph completion techniques with the wide application of knowledge graph. Previous works on knowledge graph completion mainly consider predicting missing one element for given two elements in a triplet, such as (h, r, ?). However, this task lacks of reasonable consideration between entities and relations, such as (Italy, acted in, ?), which may cause the meaningless predictions. Recent attempt solves this problem by redefining a new task-instance completion, which generates and evaluates reasonable relation-tail pairs for given head entity, such as (h, ?, ?). In this work, we propose a novel Prototype Augmented Neighbor Constraint instance completion model called PANC, which consists of two modules-prototype filter and neighbor aggregation grader. A kind of coarse-grained information prototype is utilized in filters to generate more candidate relation-tail pairs and neighbor aggregation is introduced into grader so as to enhance entity embedding and constrain the combination between head entity and candidate pairs. The experiments show that our PANC outperforms the state-of-the-art instance completion techniques on two real-world datasets FB15k and JF17k. And the ablation results verify the effectiveness of the modules in our proposed PANC.
Program representations for predictive compilation: State of affairs in the early 20’s

In the last five years, predictive compilation has advanced with long strides. Contributions in the field include new program embeddings, new learning architectures, and datasets with millions of programs. This paper evaluates 25 state-of-the-art program embeddings, three of them new, plus two learning models from previous work. We have trained this apparatus with three large datasets, and have applied it onto three classification problems. When classifying programs according to the problem that they solve, we reproduced the high-accuracy results seen in previous work. However, we have not been able to repeat these results in the two new classification challenges that we study: namely, determining the depth of the most nested loop in a program and determining the best sequence of optimizations to reduce code size of programs. Negative results emerged, even in spite of the large number of classifiers, 25, that we have evaluated. Surprisingly, using the histogram of instruction opcodes, a very simple program embedding, led to about the same classification accuracy than embeddings like Ir2Vec or Inst2Vec , which were designed to solve stochastic compilation tasks.
Variational Bayes in State Space Models: Inferential and Predictive Accuracy

Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that, in terms of accuracy on fixed parameters, there is a clear hierarchy in terms of the methods, with approaches that adequately approximate the states yielding superior accuracy over methods that do not. We also document numerically that over small out-of-sample evaluation periods the inferential discrepancies between the various methods often yield only small discrepancies in predictive accuracy. Nevertheless, in certain settings, and over a longer out-of-sample period, these predictive discrepancies can become meaningful. This finding indicates that the invariance of predictive results to inferential inaccuracy, which has been an oft-touted point made by practitioners seeking to justify the use of variational inference, is not ubiquitous and must be assessed on a case-by-case basis. Supplementary materials for this article are available online.
An enhanced multiscale generation and depth-perceptual loss-based super-resolution network for prostate ultrasound images

Abstract Computer-aided diagnosis of prostate ultrasound images is gradually being combined with deep learning to help detect and treat diseases. However, ultrasound images of the prostate have problems such as low resolution and unbalanced categories. In addition, the current image classification algorithms have difficulty with classification network performance due to insufficient data volume. To solve these problems, inspired by CycleGAN, we propose an enhanced multiscale generation and depth-perceptual loss-based super-resolution (SR) network for prostate ultrasound images (EGDL-CycleGAN). We study and improve the generative network and perceptual loss of CycleGAN. In this work we achieve multiscale feature extraction through an improved generator, and utilize full-scale skip connections between encoder and decoder to capture fine-grained details and coarse-grained semantics at full scale. This effectively improves the performance of the generative network and makes the reconstruction effect better. We also use the residual structure for deep extraction of features to obtain perceptual loss, and add this to the network loss function for training the model. This enables the model to learn the global and local differences between the real and generated images. This approach pays more attention to the edge information and spatial information of the image, and provides relevant spatial information feedback to the generator to improve the generator’s ability to perceive consistent super-resolution. The method can enhance the prostate ultrasound image dataset and provide rich images for the next step in intelligence-assisted classification and diagnosis of prostate cancer ultrasound images. The evaluation of peak signal-to-noise ratio/structural similarity and visual effects against the benchmark of our datasets illustrates that our proposed approach is effective and superior to the bicubic classic image SR reconstruction algorithm, the SRGAN perception-driven method and the CycleGAN method applied to ultrasound images. In addition, the method of using the original dataset combined with the SR reconstruction image dataset can effectively improve the accuracy of the classification network in intelligence-assisted classification diagnosis of prostate cancer ultrasound images. In EfficientNetV2 the accuracy is improved from 0.843 to 0.867 and in Swin Transformer the accuracy is improved from 0.893 to 0.917.
Orientation-aware People Detection and Counting Method based on Overhead Fisheye Camera

The rise of intelligent vision-based people detection and counting methods will have a significant impact on the future security and space management of intelligent buildings. The current deep learning-based people detection algorithm achieves state-of-the-art performance in images collected by standard cameras. Nevertheless, standard vision approaches do not perform well on fisheye cameras because they are not suitable for fisheye images with radial geometry and barrel distortion. Overhead fisheye cameras can provide a larger field of view compared to standard cameras in people detection and counting tasks. In this paper, we propose an orientation-aware people detection and counting method based on an overhead fisheye camera. Specifically, an orientation-aware deep convolutional neural network with simultaneous attention refinement module (SARM) is introduced for people detection in arbitrary directions. Based on the attention mechanism, SARM can suppress the noise feature and highlight the object feature to improve the context focusing ability of the network on the people with different poses and orientations. Following the collection of detection results, an Internet of Things (IoT) system based on Real Time Streaming Protocol (RTSP) is constructed to output results to different devices. Experiments on three common fisheye image datasets show that under low light conditions, our method has high generalization ability and outperforms the state-of-the-art methods.
Efficient Sampling Algorithm for Electric Machine Design Calculations incorporating Empirical Knowledge

In order to meet the increasing demand for electric vehicles, automotive suppliers such as ZF Friedrichshafen AG are trying to develop modular electric motor platforms. In order to find the optimal platform and to be able to use machine learning techniques, a large amount of data is required. This article shows an overall concept that includes a central database. With this it is possible to build a continuously growing data lake to support the electric machine design with a data-driven solution. The efficient sampling algorithm developed makes it possible to incorporate empirical knowledge into the sampling. In addition, it is possible to reuse calculated designs and no design is calculated more than once. This achieves the efficient generation of large amounts of data, which enable the use of machine learning.