Reviewerly/exHarmony huggingface dataset:
Video distillation

Video has become one of the most important data forms. Video distillation explores more compact data forms and information modalities by analyzing the spatial-temporal and semantic features of video data, which is an important task in computer vision and a key technique in artificial intelligence. With the rapid development of video capturing devices and the increasing human requirements, video analysis tasks are facing numbers of opportunities and challenges. In recent years, large amounts of video distillation approaches are proposed. This paper creatively unifies the theoretical basis of video distillation by analyzing the relationship among data, information and knowledge from the perspective of information theory, and argues that the principle of video distillation is to improve the information capacity of video data. Then, we overview existing approaches in the aspects of video data representation, key content summarization, moving object synopsis and text description generation, etc., and relate the development of video summarization, synopsis and captioning, which are typical tasks in video distillation. More importantly, this paper discusses the advantages and drawbacks of existing approaches, and then points out several key scientific problems that have not yet been addressed, and simultaneously analyzes the potential future development in video distillation.
High-level feature extraction: fixed shape matching

High-level feature extraction concerns finding shapes and objects in computer images. In feature extraction, we generally seek invariance properties so that the extraction result does not vary according to chosen (or specified) conditions. This implies finding objects, whatever their position, their orientation or their size. This chapter concerns shapes that are fixed in shape (such as a segment of bone in a medical image); the following chapter concerns shapes, which can deform (like the shape of a walking person). We first consider whether we can detect objects by thresholding. This is only likely to provide a solution when illumination and lighting can be controlled, so we then consider two main approaches: one is to extract constituent parts; the other is to extract constituent shapes. We can actually collect and describe low-level features described earlier. In this, wavelets can provide object descriptions, as can SIFT and distributions of low-level features. In this way we represent objects as a collection of interest points, rather than using shape analysis. Conversely, we can investigate the use of shape: template matching is a model-based approach in which the shape is extracted by searching for the best correlation between a known model and the pixels in an image. There are alternative ways to compute the correlation between the template and the image. Correlation can be implemented by considering the image or frequency domains, and the template can be defined by considering intensity values or a binary shape. The Hough transform defines an efficient implementation of template matching for binary templates. This technique is capable of extracting simple shapes such as lines and quadratic forms as well as arbitrary shapes. In any case, the complexity of the implementation can be reduced by considering invariant features of the shapes.
Segmentation of Moving Objects using Numerous Background Subtraction Methods for Surveillance Applications

Background subtraction is a key part to detect moving objects from the video in computer vision field. It is used to subtract reference frame to every new frame of video scenes. There are wide varieties of background subtraction techniques available in literature to solve real life applications like crowd analysis, human activity tracking system, traffic analysis and many more. Moreover, there were not enough benchmark datasets available which can solve all the challenges of subtraction techniques for object detection. Thus challenges were found in terms of dynamic background, illumination changes, shadow appearance, occlusion and object speed. In this perspective, we have tried to provide exhaustive literature survey on background subtraction techniques for video surveillance applications to solve these challenges in real situations. Additionally, we have surveyed eight benchmark video datasets here namely Wallflower, BMC, PET, IBM, CAVIAR, CD.Net, SABS and RGB-D along with their available ground truth. This study evaluates the performance of five background subtraction methods using performance parameters such as specificity, sensitivity, FNR, PWC and F-Score in order to identify an accurate and efficient method for detecting moving objects in less computational time.
Hybridization of Vigenere Technique with the Collaboration of RSA for Secure Communication

The security factor is one of the major concerns in today’s world. As security is the breath of communication, as much as we can make our communication system secure, the system will be more trustworthy and be more restricted to snap as well as can save guard from the unauthorized attempt. Either symmetric or asymmetric encryption was used in the earlier method to ensure data security. However, any of them alone makes the system either unsecured or time-consuming. In our thesis work, we have used both the techniques together to make the system as much as reliable and also to make it faster using the hybridization of asymmetric RSA encryption and symmetric modified vigenere technique. This hybridization method sends the vigenere table as an encrypted string using an asymmetric process with the collaboration of the RSA encryption algorithm where the string will be encrypted by the public key generated by the receiver. Later the string will be decrypted using the receiver’s private key. Therefore, we can claim that the extended vigenere method with the collaboration of RSA makes the overall communication more secure, stable, reliable, and faster.
Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.
Object-Based Six-Degrees-of-Freedom Rendering of Sound Scenes Captured with Multiple Ambisonic Receivers

This article proposes a system for object-based six-degrees-of-freedom (6DoF) rendering of spatial sound scenes that are captured using a distributed arrangement of multiple Ambisonic receivers.The approach is based on first identifying and tracking the positions of sound sources within the scene, followed by the isolation of their signals through the use of beamformers.These sound objects are subsequently spatialized over the target playback setup, with respect to both the head orientation and position of the listener.The diffuse ambience of the scene is rendered separately by first spatially subtracting the source signals from the receivers located nearest to the listener position.The resultant residual Ambisonic signals are then spatialized, decorrelated, and summed together with suitable interpolation weights.The proposed system is evaluated through an in situ listening test conducted in 6DoF virtual reality, whereby real-world sound sources are compared with the auralization achieved through the proposed rendering method.The results of 15 participants suggest that in comparison to a linear interpolation-based alternative, the proposed object-based approach is perceived as being more realistic.
Investigating the Identification of Breast Cancer and Its Risk

Breast cancer in American women the most common is violence, this is 40,000 every year Causing deaths .These breast tumors contain Phenol, It is found in a variety of breasts Creates cancer cells. Immune compromised Human breast in mice from cancer cells Grown model Using, breast cancer a minority of cells only new tumors Capable of creating we found that. In the last 30 years, Cell surface marker In terms of expression, Tumor (tum or Initiation) Tum organic Non-cancer distinguishing from cells Possible. Conducted many in the last year Test results updated, the beginning of three trials the results has been reported. Purpose: National Cancer In February 1993 at the Institute For breast cancer International on Screening Conducted the workshop, This is a breast cancer test The most recent in the world Of clinical trial data Comprehensive and Objective critical review Conduct, of new knowledge Current status To evaluate Proof. Further research will be required Identify problems. Breastfeeding in Nigeria Most of the cancer is a common disease. It's mostly worse for reasons Related to prognosis. At the Nigerian Teaching Hospital Managing Breast Cancer Challenges affecting outcomes, Intensity, effect and this to explore the factors designed. The medical records of patients suffering from breast cancer over a period of 8 years (1996-2003) were reviewed in two sections of the Teaching Hospital. Both divisions belong to parts of southwestern Nigeria Serving urban, semi-urban and rural communities. Breast cancer over an 8-year period of study Two hundred and twelve patients were diagnosed with the disease. Breast density, mammography Assessed by, breast tissue reflects the composition. Breast epithelium And stroma than fat Produce fewer X-rays, So mammograms shine Show, at the same time The fat will be darker .In this review, breast density And prone to breast cancer Between character Of the present knowledge of the relationship Of selected areas We provide an overview.
An overview on synthetic administrative data for research

Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.
The impact of different coverslipping methods in the quality of the whole slide images used for diagnosis in pathology

Digital pathology workflow aims to create whole slide images (WSIs) for diagnosis. The quality of the WSIs depends primarily on the quality of the glass slides produced by the pathology laboratory, where the coverslipping method plays an important role. In this study we compare the glass, the film, and the liquid coverslipping methods to evaluate which ones are suitable to create WSIs for diagnosis. The study included 18 formalin-fixed paraffin-embedded tissue blocks. Of each block, 3 consecutive sections were covered using 1 of the 3 methods. The slides were scanned and evaluated for quality criteria by 2 pathologists experienced in digital pathology. The coverslipping method interferes with the quality of the WSIs, as well as with the scanning time and the file size of the WSIs. All coverslipping methods were found suitable for diagnosis. The glass and liquid methods were manual and had similar results concerning the presence of air bubbles/polymer accumulation, air drying artefacts, tissue exposed, and staining alterations. The glass method was the one with more air bubbles. The liquid method was associated with more alterations on the WSIs, but with the lowest file sizes. Automation of coverslipping and calibration of the scanner for the coverslipping method chosen by the pathology laboratory are relevant for the final quality of the WSIs.
Master 2.0’ competence portrait as the basis for the human resources Professionalitet development

Introduction. The transition of the state to the digital economy is characterized by turbulence in the development processes of various industries and services. New economic and technological conditions require the creation and implementation of approaches to assist citizens in the development of new competencies in the digital economy, which is only possible if the architecture of the country's vocational education system is improved. From September 1, 2022, the Ministry of Education of Russia is launching an experimental program "Professionalitet", within the framework of which about 150 thousand students of secondary vocational education organizations will start studying, taking into account the requirements of leading industry enterprises. In addition, one of the initiatives of the "Professionalitet" involves the training of teaching staff who are able to carry out advanced training of qualified competitive personnel. Today, the issue of improving the content of training a master of industrial training, as a key figure in the vocational education system, is again becoming relevant. Therefore, the competence portrait of a master who meets the challenges of modern society and provides high-quality and effective training of personnel requires a significant update. Materials and Methods. The presented research is based on competence-based and personalityoriented approaches to vocational education. Modeling and analysis of domestic and foreign scientific literature and regulatory documents were chosen as research methods. Results. A competency-based model of a vocational training master format 2.0 is proposed, which includes universal (over-professional or soft-), methodical, organizational and managerial, research and digital competencies in new descriptors. Discussion and Conclusions. During the Panel discussion "Competence portrait of a vocational training master as the basis of the human resources of the Russian VET system" within the framework of the business program of the WorldSkills National Final, held in August 2021 in the city of Ufa, experts in the field of professional and vocational pedagogical education, as well as representatives of regional ministries that determine the policy in the field of education, have confirmed the importance of each of the highlighted block of competencies for the masters of industrial training of the future. However, the gap between the psychological, pedagogical and professional components in the content of Master 2.0 training is obvious. In this regard, the task of finding ways to overcome this gap in the design of educational programs that integrate the parallel formation of pedagogical skills through the prism of sectoral content becomes relevant today.
Patch Diffusion: A General Module for Face Manipulation Detection

Detection of manipulated face images has attracted a lot of interest recently. Various schemes have been proposed to tackle this challenging problem, where the patch-based approaches are shown to be promising. However, the existing patch-based approaches tend to treat different patches equally, which do not fully exploit the patch discrepancy for effective feature learning. In this paper, we propose a Patch Diffusion (PD) module which can be integrated into the existing face manipulation detection networks to boost the performance. The PD consists of Discrepancy Patch Feature Learning (DPFL) and Attention-Aware Message Passing (AMP). The DPFL effectively learns the patch features by a newly designed Pairwise Patch Loss (PPLoss), which takes both the patch importance and correlations into consideration. The AMP diffuses the patches through attention-aware message passing in a graph network, where the attentions are explicitly computed based on the patch features learnt in DPFL. We integrate our PD module into four recent face manipulation detection networks, and carry out the experiments on four popular datasets. The results demonstrate that our PD module is able to boost the performance of the existing networks for face manipulation detection.
An autonomous agent for negotiation with multiple communication channels using parametrized deep Q-network

Agent-based negotiation aims at automating the negotiation process on behalf of humans to save time and effort. While successful, the current research considers communication between negotiation agents through offer exchange. In addition to the simple manner, many real-world settings tend to involve linguistic channels with which negotiators can express intentions, ask questions, and discuss plans. The information bandwidth of traditional negotiation is therefore restricted and grounded in the action space. Against this background, a negotiation agent called MCAN (multiple channel automated negotiation) is described that models the negotiation with multiple communication channels problem as a Markov decision problem with a hybrid action space. The agent employs a novel deep reinforcement learning technique to generate an efficient strategy, which can interact with different opponents, i.e., other negotiation agents or human players. Specifically, the agent leverages parametrized deep Q-networks (P-DQNs) that provides solutions for a hybrid discrete-continuous action space, thereby learning a comprehensive negotiation strategy that integrates linguistic communication skills and bidding strategies. The extensive experimental results show that the MCAN agent outperforms other agents as well as human players in terms of averaged utility. A high human perception evaluation is also reported based on a user study. Moreover, a comparative experiment shows how the P-DQNs algorithm promotes the performance of the MCAN agent.
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis

The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.The development of the ABSA task is very much hindered by the lack of annotated data.To tackle this, the prior works have studied the possibility of utilizing the sentiment analysis (SA) datasets to assist in training the ABSA model, primarily via pretraining or multi-task learning.In this article, we follow this line, and for the first time, we manage to apply the Pseudo-Label (PL) method to merge the two homogeneous tasks.While it seems straightforward to use generated pseudo labels to handle this case of label granularity unification for two highly related tasks, we identify its major challenge in this paper and propose a novel framework, dubbed as Dualgranularity Pseudo Labeling (DPL).Further, similar to PL, we regard the DPL as a general framework capable of combining other prior methods in the literature (Rietzler et al., 2019;Bai et al., 2020).Through extensive experiments, DPL has achieved state-of-the-art performance on standard benchmarks surpassing the prior work significantly (Liu et al., 2021).
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning

Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training.However, the tradition of generating adversarial perturbations for each input embedding (in the settings of NLP) scales up the training computational complexity by the number of gradient steps it takes to obtain the adversarial samples.To address this problem, we leverage Flooding method which primarily aims at better generalization and we find promising in defending adversarial attacks.We further propose an effective criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch.Our approach requires zero adversarial sample for training, and its time consumption is equivalent to fine-tuning, which can be 2-15 times faster than standard adversarial training.We experimentally show that our method improves BERT's resistance to textual adversarial attacks by a large margin, and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks.
RST Discourse Parsing with Second-Stage EDU-Level Pre-training

Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing.Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).To this end, we propose a second-stage EDU-level pretraining approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models.Concretely, the two tasks are (1) next EDU prediction (NEP) and ( 2) discourse marker prediction (DMP).We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation.Experimental results on a benckmark dataset show that our method is highly effective, leading a 2.1-point improvement in F1-score.All codes and pre-trained models will be released publicly to facilitate future studies.
The Assessment of Cloud Service Trustworthiness State Based on D-S Theory and Markov Chain

The massive cloud service market is full of various services with uneven quality. Even the services that have passed the platform detection will have unknown trustworthiness problems in the actual use process. The risk environment of the cloud service determines that its trustworthiness is random. The static trustworthiness assessment results can only reflect the cloud service trustworthiness at a certain time, not enough to reflect the real trustworthiness of the cloud service. To objectively reflect the trustworthiness of the cloud service, it is necessary to further assess the cloud service trustworthiness state and its changes on the basis of trustworthiness level measurement. To solve this problem, this paper combs the trustworthiness indicators of the cloud service, puts forward an effective assessment method of cloud service trustworthiness level based on D-S theory, and puts forward the representation method of cloud service trustworthiness state and its transition state combined with Markov chain, so as to realize the effective assess of cloud service trustworthiness state and its changes. Finally, through case analysis, it shows that the method proposed in this paper is feasible, can effectively assess the cloud service trustworthiness state and its changes, and provide users with detailed assessment results, so as to help users make reasonable service selection and trustworthiness management. This research has important research significance for ensuring the trustworthiness of the cloud service and improving the security of cloud service market.