Reviewerly/exHarmony huggingface dataset:
Survival time prediction from unannotated lung cancer histopathology images

Lung cancer is the leading cause of cancer-related deaths responsible for over 130,000 deaths each year in the US. Early diagnosis and prompt treatment is crucial for prolonging survival. Many studies have been performed with regard to detecting lung cancer using computed tomography images, or classifying the type of a cancer from pathology images. Some research focuses on survival time prediction instead, which consists of ranking patients according to their expected survival time. This has proven to be a difficult task, and most approaches offer only a slight improvement over random guessing. Instead of ranking patients according to survival time, we propose to predict whether a patient falls into a long- or short-term survival group. We show that this approach outperforms regression-based approaches when predicting precise survival time is not necessary. In addition to that, we show that it is possible to predict short-term and long-term survival from lung cancer histopathology images without ROI annotations. We have obtained an 0.81 AUC when predicting whether a patient would fall into the short-term survival group (less than 12 months) or long-term survival group (greater than 60 months). Furthermore, we show that our model is capable of classifying patients into long and short-term survival even when their survival time falls outside of our chosen ranges.
Underwater target detection with quantum illumination

Quantum Illumination is a powerful tool for detecting a weakly-reflective target embedded in noise environment. Up to now, all quantum illumination techniques have been investigated in free-space scenario. Thus, it is still an open and interesting question whether quantum illumination could be applied to underwater target detection. Here, by using quantum entanglement generated by spontaneous parametric down conversion and joint quantum measurement of returned signal mode and idler mode, we construct a model of underwater target detection with quantum illumination technique. Further, we apply a set of typical experimental parameter and show that a 10-dB enhancement in the detection of a target embedded in 30-meter water can be observed. This work shows the potential applicability of quantum information technology in future underwater target detection.
Review on approaches to concept detection in medical images

Concept detection in medical images involves the identification of various biomedical semantic entities in the images. This is a non-trivial task due to the highly heterogeneous nature of medical images. This heterogeneity is caused due to the different body parts, presence of abnormalities, and imaging techniques used to capture the image. In this paper, a thorough survey on important approaches to concept detection in medical images is presented. Methods such as multi-label classification, sequence-to-sequence learning, detecting concepts from captions, and similarity search-based approaches to concept detection in medical images are reviewed. This paper also highlights the challenges associated with the medical image concept detection task. Possible avenues for further research are also discussed.
Application and Development Prospect of Blockchain in Modern City Governance Information Management System

The supply chain is an organization alliance formed by the aggregation of many enterprises and based on the information flow to realize the coordinated operation of all links between enterprises. Therefore, enhancing the information synergy between enterprises is a weapon to enhance the overall competitiveness of the supply chain. The introduction of information management technology is the technical support and fundamental guarantee to realize the supply chain information synergy capability. The introduction of blockchain solves the two main problems of traceability and irreversibility of transactions in the supply chain, which can effectively improve the transparency of the supply chain. Transparency, as an important point for all stakeholders in the supply chain, can ensure that product information can be viewed and obtained in a timely manner. The purpose of this article is to analyze the role of blockchain in the modern city governance information management system by observing and analyzing the methods of modern city governance information management system and to understand the application and development prospect of blockchain in the modern city governance information management system. The comparison of the blockchain before and after the implementation of the modern city governance information management system and the research on the application and development of the blockchain in the modern city governance information management system were analyzed. Using the method of this article, through the analysis of experimental data, I learned the role of blockchain in the modern city governance information management system and found that the blockchain plays an important role in the application and development of the modern city governance information management system, with a satisfaction of 60%. Through the combination of theoretical and experimental data, the application and development effect of the blockchain in the modern city governance information management system is analyzed to reach 55%. The research results show that the blockchain is in the application and development of the modern city governance information management system. It plays an important role. The strength of information flow transmission, processing, and analysis capabilities directly affects the smooth flow of transactions and interactions in all links of the supply chain.
Joint Entity and Relation Extraction with Adaptive Thresholding

Entity naming recognition and relation extraction are two very important subtasks in natural language processing tasks, and traditional processes do the two tasks separately, which may ignore the dependencies and associations between the two tasks. With the development of deep learning, joint entity and relation extraction based on deep learning has received wide scholarly attention, but the joint extraction task still faces challenges such as unclear entity boundaries and overlapping entities or relations. In this paper, we innovatively design a joint extraction model based on a multi-headed self-attentive mechanism, and also introduce a word-level adaptive threshold to replace the global threshold to help the model extract triples. To verify the effectiveness of our model, we conduct experiments on two publicly available datasets, and the experimental results show that our model has good performance.
ROME: All Overlays Lead to Aggregation, but Some Are Faster than Others

Aggregation is common in data analytics and crucial to distilling information from large datasets, but current data analytics frameworks do not fully exploit the potential for optimization in such phases. The lack of optimization is particularly notable in current “online” approaches that store data in main memory across nodes, shifting the bottleneck away from disk I/O toward network and compute resources, thus increasing the relative performance impact of distributed aggregation phases. We present ROME, an aggregation system for use within data analytics frameworks or in isolation. ROME uses a set of novel heuristics based primarily on basic knowledge of aggregation functions combined with deployment constraints to efficiently aggregate results from computations performed on individual data subsets across nodes (e.g., merging sorted lists resulting from top- k ). The user can either provide minimal information that allows our heuristics to be applied directly, or ROME can autodetect the relevant information at little cost. We integrated ROME as a subsystem into the Spark and Flink data analytics frameworks. We use real-world data to experimentally demonstrate speedups up to 3× over single-level aggregation overlays, up to 21% over other multi-level overlays, and 50% for iterative algorithms like gradient descent at 100 iterations.
Pre-shifted phase coding approach for absolute phase retrieval

The phase coding approach has been extensively applied to three-dimensional (3-D) shape measurement, in which the fringe order determined by the phase coding patterns is utilized to unfold the wrapped phase calculated by the phase-shifted patterns. However, due to inevitable random noises and projector/camera defocus, the fringe order can not be accurately aligned with the wrapped phase, thereby introducing the abnormal jump phase errors. To this end, this paper presents a simple and practical approach with no accuracy loss and no extra patterns to remove the abnormal jump phase errors. Firstly, the designed phase coding patterns are pre-shifted by half fringe period relative to the phase-shifted sinusoidal patterns for ensuring that the decoding order changes and the 2π phase jumps of the wrapped phase are staggered. Then, by analyzing the intensity variations of the phase-shifted sinusoidal patterns, a virtual binary code is generated, which is essentially shifted by a quarter of the fringe period relative to the phase-shifted sinusoidal patterns. Subsequently, the decoding order is divided into three subregions by referring to the distribution of the virtual binary code and the wrapped phase. After completing the segmentation, a regional correction operation (RCO) is implemented. For each subregion, the decoding order remains unchanged or is adjusted to get the correct fringe order. As a result, the interval of the wrapped phase can be accurately identified at the pixel level for phase unwrapping successfully. Benefitting from the simple pre-shifted strategy used for the phase coding patterns and the virtual binary code extracted by the phase-shifted patterns, the proposed approach can completely avoid the abnormal jump phase errors without projecting additional patterns. Moreover, compared with the conventional median filtering approach, the proposed approach has the capability to preserve sharp edges, and thus it is noninvasive. The measurement results demonstrate the reliability and accuracy of the proposed approach in measuring different objects containing intricate surface profiles.
nn-METER

Inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices. In this paper, we propose and develop nn-Meter, a novel and efficient system to accurately predict the DNN inference latency on diverse edge devices. The key idea of nn-Meter is dividing a whole model inference into kernels, i.e., the execution units on a device, and conducting kernel-level prediction. nn-Meter builds atop two key techniques: (i) kernel detection to automatically detect the execution unit of model inference via a set of well-designed test cases; and (ii) adaptive sampling to efficiently sample the most beneficial configurations from a large space to build accurate kernel-level latency predictors. nn-Meter achieves significant high prediction accuracy on four types of edge devices.
Informational Aesthetics and the Digital Exploration of Renaissance Art

The recent rise in radiographic and photographic technology in the examination and interpretation of works of art&mdash;which has accompanied the digitization of artistic creativity in the contemporary world&mdash;has greatly enhanced the power of the naked eye to view and understand the masterpieces of the past. Through modern &lsquo;digital telescopes&rsquo; a new breed of art scientists has emerged to both enhance and challenge traditional art scholarship. In this paper, I will first review some of the major projects in this new field of informational aesthetics and provide examples, from the impressive discoveries of Maurizio Seracini and Pascal Cotte, to the magnificent photographic examination of the Ghent Altarpiece by Van der Snickt and his team at the National Gallery of Art in Washington. Secondly, I will present the results of my own three-year exploration of Leonardo da Vinci&rsquo;s extant paintings and drawings using photographic magnification, leading to the identification of recurrent autographic images and motifs only visible through the lens of a digital camera. Finally, I will discuss the implications of these studies in the philosophy of photography, the world of art and the future of digital humanism.
Adversarial examples generated from sample subspace

Attacks and defenses have been a hot issue in the field of deep learning. Because of the vulnerability of deep learning models, it is easy to attack them by adversarial examples. Adversarial examples are carefully added perturbations to fool the deep learning model. For such samples, humans are able to classify correctly, while deep learning models are prone to give a high confidence false output. The working mechanism of adversarial samples has always been a difficult point and focus of research. In this work, the nature of the attack from the adversarial samples is studied accordingly from the perspective of the main and minor features of PCA (principal component analysis). Firstly, we find that the deep learning model mainly learns the main features of the data, rather than the minor features. Secondly, we discover that perturbations on the main features are more likely to lead to misclassification of the deep learning model, while perturbations on the minor features have little effect. Finally, we propose a method to generate adversarial samples in the sample subspace. Experimental results on both MNIST and CIFAR10 show that the proposed method generates smaller adversarial sample perturbations, which are more difficult to detect by human eyes and more aggressive against white-box attacks on deep learning models.
Recurrent Networks

The Hopfield networks that we discussed in the preceding chapter are special recurrent networks, which have a very constrained structure. In this chapter, however, we lift all restrictions and consider recurrent networks without any constraints. Such general recurrent networks are well suited to represent differential equationsDifferential equations and to solve them (approximately) in a numerical fashion. If the type of the differential equation is known that describes a given system, but the values of the parameters appearing in it are unknown, one may also try to train a suitable recurrent network with the help of example patterns in order to determine the system parameters.
Electronic Instruments

Abstract Electronic musical instruments using electrical signals as a sound source have existed for over 150 years. The chapter on electronic instruments focuses on digital musical instruments and explores the designs of computer-based technologies in the most common present of electronic synthesizers. As these machines advance, so do the capabilities for expressive performance. Instruments may be devices with keyboards, microphones, and/or other kinds of sensors attached or they may exist solely as software applications running on many possible kinds of computers. The chapter describes how performance gestures and sound are treated as numbers and are represented in formats such as the MIDI protocol and sound files. Musical sound is produced by samplers and synthesizers that are programmed for tone generation. The basics of spectrum analysis and synthesis methods are covered in order to understand what a particular tone generator algorithm provides as sonic dimensions to control and which dimensions of control might be available to the performer.
Improved water shower model for greener data centers

The ecological threat is produced by IT industries that are the critical frontrunners of the universal greenhouse gasses with a massive increase in hazardous carbon emission that accumulates gradually due to the computing device usage and its equivalent electricity consumption. The state of being significant in reducing carbon emission with the reduced/appropriate use of power consumption is sloping upwards, which should not be focused only on the hardware design it should also be firmed with appropriate software design and resource allocation. This originates the evolution of proficient algorithms and effective resource provisioning by allocating the loads evenly based on requests like read-only or read-write for the new or existing workloads to improve the performance in a virtualized data center. This paper proposes an improved water shower model, which can effectively manage the resources in data centers, in order to address this challenge. Different with the traditional Water Shower Model which uses the mode of operation to distribute the load among the resources, the proposed improved Trigger - Water Shower Model uses the mode of operation as well as the identification of the heavily loaded and lightly loaded resources, and thus distributes the loan eventually to obtain a reduced carbon emission. The proposed approach is validated by examining the reduction of carbon emission led by the proposed algorithm. The experimental results show more effective utilization of resources with improved average response time as 64 ms with 1.28 mgco2 reduced carbon emission by the proposed approach, compared to the traditional water shower model.
Review on Various Methodologies of Predicting Crime

The survey will make use of both supervised and unsupervised learning algorithm that has been implemented in earlier surveys on crime trend prediction and identification along with its analysis.
Straight Two-Points Distance Calculation in 3D Space Using Midpoint Proximity of Stereo Image

This paper provides straight two-points distance calculation method in 3-dimensional space using stereo image. The images are taken by two identical camera which 10cm apart. Both camera&#x2019;s point of views is parallel to each other. A center vertical line and center horizontal line are added to each image. Each interest point on interest object are marked. To increase the accuracy, each point chooses their parents by choosing the closer midpoint stereo image. The distance calculation adopts Euclidian distance with combination of two images, while the previous method only uses single image for reference. This paper presents the comparison of our work with previous work using our data. The results show that distance calculation using previous method on right image make an error around 3,5%, using previous method of left image make an error around 3,45%, and our method make an error around 2,62%. It proves that our method is slightly better than the previous one.
Unsupervised Facial Action Representation Learning by Temporal Prediction

Due to the cumbersome and expensive data collection process, facial action unit (AU) datasets are generally much smaller in scale than those in other computer vision fields, resulting in overfitting AU detection models trained on insufficient AU images. Despite the recent progress in AU detection, deployment of these models has been impeded due to their limited generalization to unseen subjects and facial poses. In this paper, we propose to learn the discriminative facial AU representation in a self-supervised manner. Considering that facial AUs show temporal consistency and evolution in consecutive facial frames, we develop a self-supervised pseudo signal based on temporally predictive coding (TPC) to capture the temporal characteristics. To further learn the per-frame discriminativeness between the sibling facial frames, we incorporate the frame-wisely temporal contrastive learning into the self-supervised paradigm naturally. The proposed TPC can be trained without AU annotations, which facilitates us using a large number of unlabeled facial videos to learn the AU representations that are robust to undesired nuisances such as facial identities, poses. Contrary to previous AU detection works, our method does not require manually selecting key facial regions or explicitly modeling the AU relations manually. Experimental results show that TPC improves the AU detection precision on several popular AU benchmark datasets compared with other self-supervised AU detection methods.
Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.
The subject of the research in this article is the features and patterns of the use of an interdisciplinary approach in modern scientific research in the field of law. The relevance of this problem and the need to study the patterns of using an interdisciplinary approach in the field of law is due to the fact that, on the one hand, an interdisciplinary approach is becoming increasingly widespread in the legal sciences, and on the other hand, the "inept" use of an interdisciplinary approach in …

The subject of the research in this article is the features and patterns of the use of an interdisciplinary approach in modern scientific research in the field of law. The relevance of this problem and the need to study the patterns of using an interdisciplinary approach in the field of law is due to the fact that, on the one hand, an interdisciplinary approach is becoming increasingly widespread in the legal sciences, and on the other hand, the "inept" use of an interdisciplinary approach in the field of law can lead to a significant decrease in the effectiveness of scientific research in the field of legal sciences and to difficulties in assessing the significance of the obtained research results. The purpose of the work is to identify the patterns of using this approach based on the analysis of the features of the interdisciplinary approach in modern scientific research in the field of legal sciences and to propose ways to use it most effectively in modern scientific research in the field of jurisprudence. В В В В В The scientific novelty of the conducted research is as follows. During the analysis of the features and patterns of the use of an interdisciplinary approach in modern legal science, it was found that not all researchers understand the features (specifics) of the use of an interdisciplinary approach in legal research. The specificity of using an interdisciplinary approach in legal research is that an interdisciplinary approach allows us to identify common patterns of formation, development and functioning of state-legal phenomena as a variety of social phenomena. Therefore, in order to increase the effectiveness of using an interdisciplinary approach in modern scientific research in the field of legal sciences, it is better to conduct research on the purely legal side of state-legal phenomena within the framework of a disciplinary approach and traditional legal methods, an interdisciplinary approach, in our opinion, should play the role of a kind of philosophical basis that does not allow the absolutization of knowledge and assumes the need for dialogue in in order to conduct scientific research more productively.