Reviewerly/exHarmony huggingface dataset:
Towards a national strategy for digital pathology in Switzerland

Precision medicine is entering a new era of digital diagnostics; the availability of integrated digital pathology (DP) and structured clinical datasets has the potential to become a key catalyst for biomedical research, education and business development. In Europe, national programs for sharing of this data will be crucial for the development, testing, and validation of machine learning-enabled tools supporting clinical decision-making. Here, the Swiss Digital Pathology Consortium (SDiPath) discusses the creation of a Swiss Digital Pathology Infrastructure (SDPI), which aims to develop a unified national DP network bringing together the Swiss Personalized Health Network (SPHN) with Swiss university hospitals and subsequent inclusion of cantonal and private institutions. This effort builds on existing developments for the national implementation of structured pathology reporting. Opening this national infrastructure and data to international researchers in a sequential rollout phase can enable the large-scale integration of health data and pooling of resources for research purposes and clinical trials. Therefore, the concept of a SDPI directly synergizes with the priorities of the European Commission communication on the digital transformation of healthcare on an international level, and with the aims of the Swiss State Secretariat for Economic Affairs (SECO) for advancing research and innovation in the digitalization domain. SDPI directly addresses the needs of existing national and international research programs in neoplastic and non-neoplastic diseases by providing unprecedented access to well-curated clinicopathological datasets for the development and implementation of novel integrative methods for analysis of clinical outcomes and treatment response. In conclusion, a SDPI would facilitate and strengthen inter-institutional collaboration in technology, clinical development, business and research at a national and international scale, promoting improved patient care via precision medicine.
Assessment of the Variational Quantum Eigensolver: Application to the Heisenberg Model

We present and analyze large-scale simulation results of a hybrid quantum-classical variational method to calculate the ground state energy of the anti-ferromagnetic Heisenberg model. Using a massively parallel universal quantum computer simulator, we observe that a low-depth-circuit ansatz advantageously exploits the efficiently preparable Néel initial state, avoids potential barren plateaus, and works for both one- and two-dimensional lattices. The analysis reflects the decisive ingredients required for a simulation by comparing different ansätze, initial parameters, and gradient-based versus gradient-free optimizers. Extrapolation to the thermodynamic limit accurately yields the analytical value for the ground state energy, given by the Bethe ansatz. We predict that a fully functional quantum computer with 100 qubits can calculate the ground state energy with a relatively small error.
Does Virtual Reality Trigger Visits and Booking Holiday Travel Packages?

Although virtual reality technology is increasingly being used in tourism, its potential as a shopping tool and as an avenue for marketing and selling tourism products and services has not yet been examined. Likewise, very little is known about how exploring holiday packages through virtual reality affects behavioral intention to visit tourist destinations. This study aims to compare the visit intentions evoked and the process of booking holiday travel packages between an immersive virtual reality environment (displayed through Oculus head-mounted glasses) and a traditional web-based 2D platform. A causal model is proposed and tested for both designs. Using a between-subjects experimental design with a sample of 202 individuals, the experiences of two randomly selected groups were observed as they bought holiday tour packages to Rio de Janeiro, Brazil. The first group made a simulated purchase in an immersive virtual reality environment using a head-mounted device, and the second group made the purchase on a traditional e-commerce website. The findings revealed that the scores given to sense of presence, attitude change, and perceived ease of use were greater among those who made the purchase in the more immersive virtual reality environment. However, the relationships between the variables in the causal model were stronger for the classic website than for the virtual reality setting. Attitude change positively affected intention to visit a destination more in the virtual reality environment.
An Improved Normed-Deformable Convolution for Crowd Counting

In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct head-scale information, the head features are obtained incompletely. Deformable convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. By learning the coordinate offsets of the sampling points, it is tractable to improve the ability to adjust the receptive field. However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information. To handle the non-uniformed sampling, an improved Normed-Deformable Convolution (<i>i.e.,</i>NDConv) implemented by Normed-Deformable loss (<i>i.e.,</i>NDloss) is proposed in this paper. The offsets of the sampling points which are constrained by NDloss tend to be more even. Then, the features in the heads are obtained more completely, leading to better performance. Especially, the proposed NDConv is a light-weight module which shares similar computation burden with Deformable Convolution. In the extensive experiments, our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF_QNRF, and UCF_CC_50 dataset, achieving 61.4, 7.8, 91.2, and 167.2 MAE, respectively. The code is available at <uri>https://github.com/bingshuangzhuzi/NDConv</uri>.
Theories of “Gender” in NLP Bias Research

The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how "gender" is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time.
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation

In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing density variation, humans tend to locate and count the targets in low-density regions, and reason the number in high-density regions. We observe that CNN focus on the local information correlation using a fixed-size convolution kernel and the Transformer could effectively extract the semantic crowd information by using the global self-attention mechanism. Thus, CNN could locate and estimate crowds accurately in low-density regions, while it is hard to properly perceive the densities in high-density regions. On the contrary, Transformer has a high reliability in high-density regions, but fails to locate the targets in sparse regions. Neither CNN nor Transformer can well deal with this kind of density variation. To address this problem, we propose a CNN and Transformer Adaptive Selection Network (CTASNet) which can adaptively select the appropriate counting branch for different density regions. Firstly, CTASNet generates the prediction results of CNN and Transformer. Then, considering that CNN/Transformer is appropriate for low/high-density regions, a density guided adaptive selection module is designed to automatically combine the predictions of CNN and Transformer. Moreover, to reduce the influences of annotation noise, we introduce a Correntropy based optimal transport loss. Extensive experiments on four challenging crowd counting datasets have validated the proposed method.
GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services

According to OWASP 2021, cross-site scripting (XSS) attacks are increasing through specially crafted XML documents. The attacker injects a malicious payload with a new pattern and combination of scripts, functions, and tags that deceits the existing security mechanisms in web services. This paper proposes an approach, GeneMiner, encompassing GeneMiner-E to extract new features and GeneMiner-C for classification of input payloads as malicious and nonmalicious. The proposed approach evolves itself to the changing patterns of attack payloads and identifies adversarial XSS attacks. The experiments have been conducted by collecting data from open source and generating various combinations of scripts, functions, and tags using an incremental genetic algorithm. The experimental results show that the proposed approach effectively detects newly crafted malicious XSS payloads with an accuracy of 98.5%, which is better than the existing classification techniques. The approach learns variations in the existing attack sample space and identifies the new attack payloads with reduced efforts.
Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).
Surface Passivation of III–V GaAs Nanopillars by Low-Frequency Plasma Deposition of Silicon Nitride for Active Nanophotonic Devices

Numerous efforts have been devoted to improve the electronic and optical properties of III–V compound materials via reduction of their nonradiative states, aiming at highly efficient III–V sub-micrometer active devices and circuits. Despite many advances, the poor reproducibility and short-term passivation effect of chemical treatments, such as sulfidation and nitridation, requires the use of protective encapsulation methods, not only to protect the surface, but also to provide electrical isolation for device manufacturing. There is still a controversial debate on which combination of chemical treatment and capping dielectric layer can best reproducibly protect the crystal surface of III–V materials while being compatible with readily available semiconductor-foundry plasma deposition methods. This work reports on a systematic experimental study on the role of sulfide ammonium chemical treatment followed by dielectric coating (either silicon oxide or nitride) in the passivation effect of GaAs/AlGaAs nanopillars. Our results conclusively show that, under ambient conditions, the best surface passivation is achieved using ammonium sulfide followed by encapsulation with a thin layer of silicon nitride by low-frequency plasma-enhanced chemical deposition. Here, the sulfurized GaAs surfaces, high level of hydrogen ions, and low-frequency (380 kHz) excitation plasma that enable intense bombardment of hydrogen, all seem to provide a combined active role in the passivation mechanism of the pillars by reducing the surface states. As a result, we observe up to a 29-fold increase of the photoluminescence (PL) integrated intensity for the best samples as compared to untreated nanopillars. X-ray photoelectron spectroscopy analysis confirms the best treatments show remarkable removal of gallium and arsenic native oxides. Time-resolved micro-PL measurements display nanosecond lifetimes resulting in a record-low surface recombination velocity of ∼1.1 × 104 cm s–1 for dry-etched GaAs nanopillars. We achieve robust, stable, and long-term passivated nanopillar surfaces, which creates expectations for remarkable high internal quantum efficiency (IQE > 0.5) in nanoscale light-emitting diodes. The enhanced performance paves the way to many other nanostructures and devices such as miniature resonators, lasers, photodetectors, and solar cells, opening remarkable prospects for GaAs active nanophotonic devices.
Skew information-based coherence generating power of quantum channels

We study the ability of a quantum channel to generate quantum coherence when it applies to incoherent states. We define the measure of coherence generating power (CGP) for a generic quantum channel to be the average coherence generated by the quantum channel acting on a uniform ensemble of incoherent states based on the skew information-based coherence measure. We present explicitly the analytical formulae of the CGP for any arbitrary finite dimensional unitary channels. We derive the mean value of the CGP over the unitary groups and investigate the typicality of the normalized CGP. Furthermore, we give an upper bound of the CGP for the convex combinations of unitary channels. Detailed examples are provided to calculate exactly the values of the CGP for the unitary channels related to specific quantum gates and for some qubit channels.
Learning Universal Adversarial Perturbation by Adversarial Example

Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to evaluate the robustness of the model from a more effective and accurate perspective. The existing universal attack methods fail to exploit the differences and connections between the instance and universal levels to produce dominant perturbations. To address this challenge, we propose a new universal attack method that unifies instance-specific and universal attacks from a feature perspective to generate a more dominant UAP. Specifically, we reformulate the UAP generation task as a minimax optimization problem and then utilize the instance-specific attack method to solve the minimization problem thereby obtaining better training data for generating UAP. At the same time, we also introduce a consistency regularizer to explore the relationship between training data, thus further improving the dominance of the generated UAP. Furthermore, our method is generic with no additional assumptions about the training data and hence can be applied to both data-dependent (supervised) and data-independent (unsupervised) manners. Extensive experiments demonstrate that the proposed method improves the performance by a significant margin over the existing methods in both data-dependent and data-independent settings. Code is available at https://github.com/lisenxd/AT-UAP.
Role of Genetic Algorithm in Optimization of Hindi Word Sense Disambiguation

The Word Sense Disambiguation system (WSD) is widely used in many fields, including business, research, education, and government organizations. The availability of natural language data on the internet has grown in tandem with the rapid advancement of technology and the widespread use of the internet. Disambiguation is best tool for avoiding the problem of misperception. The most important feature of this approach is that words with multiple meanings of nouns can be easily disambiguated according to the sense of the sentences. As a result of incorporating all of these factors, This work will concentrate on the accuracy of adjectives and verbs. Many natural languages, including English, German, Arabic, Assamese, and others, have different methodologies proposed, however, work on Word Sense Disambiguation in Hindi is limited. For Hindi Word Sense Disambiguation, the proposed algorithm employs a genetic algorithm. The dynamic configuration window function, including the ambiguous terms left and right, is used. The central tenet of this method is that the target term and its surroundings share a common topic. With approximately 65.17%, 72%, and 74.1 %, respectively, the proposed work has compared to existing approaches such as graph-based approaches, association rules approaches, and probabilistic latent semantic analysis approaches. The proposed method has an accuracy of 80%, which indicating that it improves the existing Work by 8% in terms of accuracy and other parameters. Results show that the proposed work generates more accurate results for Hindi word sense disambiguation.
The effect of e-procurement on financial performance: Moderating the role of competitive pressure

The importance of E-procurement is significant for the development of nations. Findings of previous studies in terms of predictors and consequences of using E-procurement are inconsistent and most prior literature were conducted in developed countries. The purpose of this study is to examine the predictors and consequences of using e-procurement. Based on resource-based view (RBV) and Technology-Organization-Environment framework (TOE), the study proposed that technological (relative advantage, compatibility, and complexity) and organizational factor (top management support, organizational readiness, and Information System (IS) committee) will have significant effect on e-procurement which in turn expected to affect the firm performance. Competitive pressure is proposed as a moderating variable between technological and organizational factors, and e-procurement. The population of the study includes large companies in Jordan. Purposive sampling was deployed to collect the data using a questionnaire. The findings were derived from 221 responses. Data analysis was conducted using Smart PLS. The findings showed that technological (relative advantage, compatibility, and complexity) and organizational (top management support and organizational readiness) have significant effect on e-procurement which in turn affected firm performance. Competitive pressure did not moderate the effect of technological and organizational factors on e-procurement. The findings help the policy makers in Jordan to increase the usage of e-procumbent and firm performance by focusing on the benefits and reducing the complexity of using a new technology.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion

Temporal knowledge graphs store the dynamics of entities and relations during a time period.However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios.Hence, modeling temporal knowledge graphs to complete the missing facts is important.In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule networkbased embedding model for Temporal knowledge graph completion.TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks.Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information.Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion.Additional analysis also shows that TempCaps is efficient 1 .
Object Tracking Based on Satellite Videos: A Literature Review

Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively.
Exploiting shape cues for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training.To this end, previous methods adopt the common pipeline: they generate pseudo masks from class activation maps (CAMs) and use such masks to supervise segmentation networks.However, it is challenging to derive comprehensive pseudo masks that cover the whole extent of objects due to the local property of CAMs, i.e., they tend to focus solely on small discriminative object parts.In this paper, we associate the locality of CAMs with the texturebiased property of convolutional neural networks (CNNs).Accordingly, we propose to exploit shape information to supplement the texture-biased CNN features, thereby encouraging mask predictions to be not only comprehensive but also well-aligned with object boundaries.We further refine the predictions in an online fashion with a novel refinement method that takes into account both the class and the color affinities, in order to generate reliable pseudo masks to supervise the model.Importantly, our model is end-to-end trained within a single-stage framework and therefore efficient in terms of the training cost.Through extensive experiments on PASCAL VOC 2012, we validate the effectiveness of our method in producing precise and shape-aligned segmentation results.Specifically, our model surpasses the existing state-of-the-art single-stage approaches by large margins.What is more, it also achieves a new state-of-the-art performance over multi-stage approaches, when adopted in a simple two-stage pipeline without bells and whistles.