Reviewerly/exHarmony huggingface dataset:
Near-lossless compression scheme using hamming codes for non-textual important regions in document images

Working at Bell Labs in 1950, irritated with error-prone punched card readers, R W Hamming began working on error-correcting codes, which became the most used error-detecting and correcting approach in the field of channel coding in the future. Using this parity-based coding, two-bit error detection and one-bit error correction was achievable. Channel coding was expanded further to correct burst errors in data. Depending upon the use of the number of data bits ‘d’ and parity bits ‘k’ the code is specified as (n, k) code, here ‘n’ is the total length of the code (d+k). It means that 'k' parity bits are required to protect 'd' data bits, which also means that parity bits are redundant if the code word contains no errors. Due to the framed relationship between data bits and parity bits of the valid codewords, the parity bits can be easily computed, and hence the information represented by 'n' bits can be represented by 'd' bits. By removing these unnecessary bits, it is possible to produce the optimal (i.e., shortest length) representation of the image data. This work proposes a digital image compression technique based on Hamming codes. Lossless and near-lossless compression depending upon need can be achieved using several code specifications as mentioned here. The achieved compression ratio, computational cost, and time complexity of the suggested approach with various specifications are evaluated and compared, along with the quality of decompressed images.
A Novel Shape Descriptor for Object Recognition

In this study a novel shape descriptor for object recognition is proposed. As a preprocessing stage, Canny edge detection [4] is applied to input images. Output of Canny edge detector, namely edge image, is sampled and various number of points are selected. Chosen points are input to the new shape descriptor. Proposed shape descriptor is composed of deviations from average range and average angle. Shape descriptor is used as a feature extractor output of which is fed to linear classifier. Linear classifier is trained using pseudo-inverse and gradient descent techniques. Full MNIST dataset is used to test the system and results are reported.
Multi-agent medical image segmentation: A survey

During the last decades, the healthcare area has increasingly relied on medical imaging for the diagnosis of a growing number of pathologies. The different types of medical images are mostly manually processed by human radiologists for diseases detection and monitoring. However, such a procedure is time-consuming and relies on expert judgment. The latter can be influenced by a variety of factors. One of the most complicated image processing tasks is image segmentation. Medical image segmentation consists of dividing the input image into a set of regions of interest, corresponding to body tissues and organs. Recently, artificial intelligence (AI) techniques brought researchers attention with their promising results for the image segmentation automation. Among AI-based techniques are those that use the Multi-Agent System (MAS) paradigm. This paper presents a comparative study of the multi-agent approaches dedicated to the segmentation of medical images, recently published in the literature.
DIGITALIZATION IN UNIVERSITY EDUCATION: DIDACTIC ASPECTS

Intensively developing digital technologies are transforming the basic processes studied by the didactics of higher education: ways of the teacher-student interrelated activity; the student and the knowledge, skills, and competencies intended to be learnt and gained; the teacher and the methodology of educational material. The learning goals set by the information society shift the focus from mastering the system of knowledge and developing professional skills to developing competencies, the formation of students’ readiness to design their own unique educational trajectories based on a competency-based approach. As higher education becomes more digitalized, there is a shift from coordinating teaching and learning activities to projecting, forming, and mastering individual educational plans. To meet the challenges of modern education, educators are expected to be able to comprehend changes impacting all of the components of didactic theory, including goals, content, forms, technologies, and learning tools. Digitalization processes lead to the dominance of infographic forms of presentation of educational material instead of narrative (text) forms associated with the development of voluminous texts. The variety of forms of organization of educational activities in the digital educational environment is increasing significantly, they are becoming a dynamic open system of educational modules. The educators are being faced with new theoretical tasks due to the active modernization of university educational practice, including the need to find ways to implement continuity between traditional and innovative methods of organizing the educational process while taking into account specific cognitive, motivational, and need-based characteristics of the digital generation. The transition from teaching as the educators’ primary activity to the variety of pedagogical roles they play in the digital educational process actualizes teachers’ mastery of the information technologies and the development of their personal professional teaching strategy.
Joint Correlation and Attention Based Feature Fusion Network for Accurate Visual Tracking

Correlation operation and attention mechanism are two popular feature fusion approaches which play an important role in visual object tracking. However, the correlation-based tracking networks are sensitive to location information but loss some context semantics, while the attention-based tracking networks can make full use of rich semantic information but ignore the position distribution of the tracked object. Therefore, in this paper, we propose a novel tracking framework based on joint correlation and attention networks, termed as JCAT, which can effectively combine the advantages of these two complementary feature fusion approaches. Concretely, the proposed JCAT approach adopts parallel correlation and attention branches to generate position and semantic features. Then the fusion features are obtained by directly adding the location feature and semantic feature. Finally, the fused features are fed into the segmentation network to generate the pixel-wise state estimation of the object. Furthermore, we develop a segmentation memory bank and an online sample filtering mechanism for robust segmentation and tracking. The extensive experimental results on eight challenging visual tracking benchmarks show that the proposed JCAT tracker achieves very promising tracking performance and sets a new state-of-the-art on the VOT2018 benchmark.
Adversarial Algorithm Unrolling Network for Interpretable Mechanical Anomaly Detection

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on artificial neural networks, are frequently constructed as black boxes, resulting in opaque interpretability in architecture and low credibility in results. This article proposes an adversarial algorithm unrolling network (AAU-Net) for interpretable mechanical anomaly detection. AAU-Net is a generative adversarial network (GAN). Its generator, composed of an encoder and a decoder, is mainly produced by algorithm unrolling of a sparse coding model, which is specially designed for feature encoding and decoding of vibration signals. Thus, AAU-Net has a mechanism-driven and interpretable network architecture. In other words, it is ad hoc interpretable. Moreover, a multiscale feature visualization approach for AAU-Net is introduced to verify that meaningful features are encoded by AAU-Net, helping users to trust the detection results. The feature visualization approach enables the results of AAU-Net to be interpretable, i.e., post hoc interpretable. To verify AAU-Net's capability of feature encoding and anomaly detection, we designed and performed simulations and experiments. The results show that AAU-Net can learn signal features that match the dynamic mechanism of the mechanical system. Considering the excellent feature learning ability, unsurprisingly, AAU-Net achieves the best overall anomaly detection performance compared with other algorithms.
Modal-Aware Resource Allocation for Cross-Modal Collaborative Communication in IIoT

With the development of human–machine interactions, users are increasingly evolving toward an immersion experience with multidimensional stimuli. Facing this trend, cross-modal collaborative communication is considered an effective technology in the Industrial Internet of Things (IIoT). In this article, we focus on open issues about resource reuse, pair interactivity, and user assurance in cross-modal collaborative communication to improve Quality of Service (QoS) and users’ satisfaction. Therefore, we propose a novel architecture of modal-aware resource allocation to solve these contradictions. First, taking all the characteristics of multimodal into account, we introduce network slices to visualize resource allocation, which is modeled as a Markov decision process (MDP). Second, we decompose the problem by the transformation of probabilistic constraint and Lyapunov Optimization. Third, we propose a deep reinforcement learning (DRL) decentralized method in the dynamic environment. Meanwhile, a federated DRL framework is provided to overcome the training limitations of local DRL models. Finally, numerical results demonstrate that our proposed method performs better than other decentralized methods and achieves superiority in cross-modal collaborative communications.
Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts

Background Automated breast (AB) US effectively depicts mammographically occult breast cancers in Western women. However, few studies have focused on the outcome of supplemental AB US in Asian women who have denser breasts than Western women. Purpose To evaluate the performance of supplemental AB US on mammography-based breast cancer screening in Asian women with dense breasts and those with nondense breasts. Materials and Methods A retrospective database search identified asymptomatic Korean women who underwent digital mammography (DM) and supplemental AB US screening for breast cancer between January 2018 and December 2019. We excluded women without sufficient follow-up, established final diagnosis, or histopathologic results. Performance measures of DM alone and AB US combined with DM (hereafter AB US plus DM) were compared. The primary outcome was cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed based on mammography density. Results From 2785 screening examinations in 2301 women (mean age, 52 years ± 9 [SD]), 28 cancers were diagnosed (26 screening-detected cancers, two interval cancers). When compared with DM alone, AB US plus DM resulted in a higher CDR of 9.3 per 1000 examinations (95% CI: 7.7, 10.3) versus 6.5 per 1000 examinations (95% CI: 5.2, 7.2; P < .001) and a higher sensitivity of 90.9% (95% CI: 77.3, 100.0) versus 63.6% (95% CI: 40.9, 81.8; P < .001) but a lower specificity of 86.8% (95% CI: 85.2, 88.2) versus 94.6% (95% CI: 93.6, 95.5; P < .001) in women with dense breasts. In women with nondense breasts, AB US plus DM resulted in a higher CDR of 9.5 per 1000 examinations (95% CI: 7.1, 10.6) versus 6.3 per 1000 examinations (95% CI: 3.5, 7.1; P < .001), whereas specificity was lower at 95.2% (95% CI: 93.4, 96.8) versus 97.1% (95% CI: 95.8, 98.4; P < .001). Conclusion In Asian women, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities in both dense and nondense breasts. © RSNA, 2023 Supplemental material is available for this article.
State Preparation of Antisymmetrized Geminal Power on a Quantum Computer without Number Projection

The antisymmetrized geminal power (AGP) is equivalent to the number projected Bardeen-Cooper-Schrieffer (PBCS) wave function. It is also an elementary symmetric polynomial (ESP) state. We generalize previous research on deterministically implementing the Dicke state to a state preparation algorithm for an ESP state, or equivalently AGP, on a quantum computer. Our method is deterministic and has polynomial cost, and it does not rely on number symmetry breaking and restoration. We also show that our circuit is equivalent to a disentangled unitary paired coupled cluster operator and a layer of unitary Jastrow operator acting on a single Slater determinant. The method presented herein highlights the ability of disentangled unitary coupled cluster to capture nontrivial entanglement properties that are hardly accessible with traditional Hartree-Fock based electronic structure methods.
Content-Aware Retargeted Image Quality Assessment

In targeting the low correlation between existing image scaling quality assessment methods and subjective awareness, a content-aware retargeted image quality assessment algorithm is proposed, which is based on the structural similarity index. In this paper, a similarity index, that is, a local structural similarity algorithm, which can measure different sizes of the same image is proposed. The Speed Up Robust Feature (SURF) algorithm is used to extract the local structural similarity and the image content loss degree. The significant area ratio is calculated by extracting the saliency region and the retargeted image quality assessment function is obtained by linear fusion. In the CUHK image database and the MIT RetargetMe database, compared with four representative assessment algorithms and other latest four kinds of retargeted image quality assessment algorithms, the experiment proves that the proposed algorithm has a higher correlation with Mean Opinion Score (MOS) values and corresponds with the result of human subjective assessment.
Accelerating the Assembly of Defect-Free Atomic Arrays with Maximum Parallelisms

Defect-free atomic arrays have been demonstrated as a scalable and fully-controllable platform for quantum simulations and quantum computations. To push the qubit size limit of this platform further, we design an integrated measurement and feedback system, based on field programmable gate array (FPGA), to quickly assemble two-dimensional defect-free atomic array using maximum parallelisms. The total time cost of the rearrangement is first reduced by processing atom detection, atomic occupation analysis, rearrangement strategy formulation, and acousto-optic deflectors (AOD) driving signal generation in parallel in time. Then, by simultaneously moving multiple atoms in the same row (column), we save rearrangement time by parallelism in space. To best utilize these parallelisms, we propose a new algorithm named Tetris algorithm to reassemble atoms to arbitrary target array geometry from two-dimensional stochastically loaded atomic arrays. For an $L \times L$ target array geometry, the number of moves scales as $L$, and the total rearrangement time scales at most as $L^2$. We present the overall performance for different target geometries, and demonstrate a significant reduction in rearrangement time and the potential to scale up defect-free atomic array system to thousands of qubits.
Automated scholarly paper review: Concepts, technologies, and challenges

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in academic publishing. However, criticisms have long been leveled at this mechanism, mostly because of its poor efficiency and low reproducibility. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this paper, we propose the concept and pipeline of automated scholarly paper review (ASPR) and review the relevant literature and technologies of achieving a full-scale computerized review process. On the basis of the review and discussion, we conclude that there is already corresponding research and preliminary implementation at each stage of ASPR. We further look into the challenges in ASPR with the existing technologies. The major difficulties lie in inadequate data, imperfect document parsing and representation, defective human–computer interaction, and flawed deep logical reasoning. Moreover, we point out the future directions and discuss the possible moral and ethical issues of ASPR. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans.
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multilayer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an alternating graph-regularized neural network (AGNN) composed of graph convolutional layer (GCL) and graph embedding layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multilayer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with the state-of-the-art models.
Feedback-based active reset of a spin qubit in silicon

Abstract Feedback control of qubits is a highly demanded technique for advanced quantum information protocols such as fault-tolerant quantum error correction. Here we demonstrate active reset of a silicon spin qubit using feedback control. The active reset is based on quantum non-demolition (QND) readout of the qubit and feedback according to the readout results, which is enabled by hardware data processing and sequencing. We incorporate a cumulative readout technique to the active reset protocol, enhancing initialization fidelity above a limitation imposed by the single-shot QND readout fidelity. An analysis of the reset protocol implies a pathway to achieve the initialization fidelity sufficient for fault-tolerant quantum computation. These results provide a practical approach to high-fidelity qubit operations in realistic devices.
Facial Expression Recognition Model Depending on Optimized Support Vector Machine

In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the performance of the proposed model (FERM), AffectNet is used. AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online. The FERM is composed of three main phases: (i) the Data preparation phase, (ii) Applying grid search for optimization, and (iii) the categorization phase. Linear discriminant analysis (LDA) is used to categorize the data into eight labels (neutral, happy, sad, surprised, fear, disgust, angry, and contempt). Due to using LDA, the performance of categorization via SVM has been obviously enhanced. Grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). The proposed optimized SVM algorithm has achieved an accuracy of 99% and a 98% F1 score.
Website Vulnerability Scanner

In today's world, cyber security has become an important leap in the form for jobs, education.But the reality is the only a few are aware of the major wed vulnerabilities.Some statistical studies show that small scale industries are directly and indirectly connected to the world of internet, but penetration testing, we are going to do this project.In a website if there are any vulnerability, the website can easily hack.So, using penetration testing we are going to built a scanner which will find the vulnerabilities the website.With the help of vulnerability scanning in a website, inspection of the potential points of exploit on a website to identify security holes.A vulnerability scan will deduct and classifies system weeness.It will also deduct bugs in a website and communication equipment.It will predict effeteness of counter measure.
LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise

Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and then refines the coarse saliency map by the densely connected conditional random field (CRF). Different from ROSA that rely on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD models on both adversarial and clean images, as well as inference speed. In general, LeNo consists of a simple shallow noise and noise estimation that embedded in the encoder and decoder of arbitrary SOD networks respectively. Inspired by the center prior of human visual attention mechanism, we initialize the shallow noise with a cross-shaped gaussian distribution for better defense against adversarial attacks. Instead of adding additional network components for post-processing, the proposed noise estimation modifies only one channel of the decoder. With the deeply-supervised noise-decoupled training on state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works not only on adversarial images but also on clean images, which contributes stronger robustness for SOD. Our code is available at https://github.com/ssecv/LeNo.
Microring-based programmable coherent optical neural networks

Coherent programmable integrated photonics circuits have shown great potential as specialized hardware accelerators for deep learning tasks, which usually involve the use of linear matrix multiplication and nonlinear activation components. We design, simulate and train an optical neural network fully based on microring resonators, which shows advantages in terms of device footprint and energy efficiency. We use tunable coupled double ring structures as the interferometer components for the linear multiplication layers and modulated microring resonators as the reconfigurable nonlinear activation components. We then develop optimization algorithms to train the direct tuning parameters such as applied voltages based on the transfer matrix method and using automatic differentiation for all optical components.