Reviewerly/exHarmony huggingface dataset:
Chinese Word Segmentation in Electronic Medical Record Text via Graph Neural Network-Bidirectional LSTM-CRF Model

Electronic medical record (EMR) text word segmentation is the basis of natural language processing in medicine. Due to the characteristics of EMR, such as strong specialization, high cost of annotation, special writing style and sustained growth of terminology, the current Chinese word segmentation (CWS) methods cannot fully meet the requirements of the application of EMR. In order to solve this problem, an EMR word segmentation model based on Graph Neural Network (GNN), bidirectional Long Short-Term Memory network (Bi-LSTM) and conditional random field (CRF) is designed in this paper to improve the segmentation effect and reduce the dependence on data set. In the model, GNN based on the domain lexicon is used to learn the local composition features, Bi-LSTM is used to capture the long-term dependence and context sequence information, and CRF is used to obtain the optimal annotation sequence based on the sentence level label information. Through multi-feature interaction, the ambiguity resolution and new word recognition in the EMR word segmentation are effectively carried out. Compared with CWS tools such as Jieba and Pkuseg, as well as baseline models and state-of-the-art methods, the precision and recall rate of the model in this paper have been significantly improved.
Design of basic process of information security risk assessment in cloud computing environment

in the risk assessment of cloud computing, how to quantify the risk probability is one of the most important problems. Generally speaking, people can only make use of conjecture, quantitative information and so on. But the risk probability obtained by this way is often different from the actual probability. In this paper, by constructing the attack and defense game model of malicious attacker and defender in cloud computing, the interactive game process of attack and defense is analyzed. Cloud computing system can predict the behavior of attack, get scientific quantitative risk probability, and then realize the risk assessment process of cloud computing system.
Single-image dehazing based on dark channel prior and fast weighted guided filtering

Haze scatters light transmitted in the air and reduces image quality, which greatly decreases the interpretability and intelligibility of an image. To solve these problems, we propose an improved real-time image dehazing algorithm based on dark channel prior and fast weighted guided filtering. First, the image is divided into dark areas and bright areas by the K-means clustering algorithm, and the atmospheric light value is calculated according to the proportion of the bright area in the whole image. Second, the fast weighted guided filtering algorithm is employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Finally, the gamma correction and automatic contrast enhancement algorithms are used to adjust the brightness and contrast of the dehazing image. Experimental results demonstrate that the proposed method can effectively remove the halo artifacts, improve the color deviation, and retain more details in the images.
Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale

Individuals are gaining more control of their personal data through recent data privacy laws such the General Data Protection Regulation and the California Consumer Privacy Act. One aspect of these laws is the ability to request a business to delete private information, the so called right to be forgotten or right to erasure. These laws have serious financial implications for companies and organizations that train large, highly accurate deep neural networks (DNNs) using these valuable consumer data sets. However, a received redaction request poses complex technical challenges on how to comply with the law while fulfilling core business operations. We introduce a DNN model lifecycle maintenance process that establishes how to handle specific data redaction requests and minimize the need to completely retrain the model. Our process is based upon the membership inference attack as a compliance tool for every point in the training set. These attack models quantify the privacy risk of all training data points and form the basis of follow-on data redaction from an accurate deployed model; excision is implemented through incorrect label assignment within incremental model updates.
IVOS - The ITEC Interactive Video Object Search System at VBS2021

We present IVOS, an interactive video content search system that allows for object-based search and filtering in video archives. The main idea behind is to use the result of recent object detection models to index all keyframes with a manageable set of object classes, and allow the user to filter by different characteristics, such as object name, object location, relative object size, object color, and combinations for different object classes – e.g., “large person in white on the left, with a red tie”. In addition to that, IVOS can also find segments with a specific number of objects of a particular class (e.g., “many apples” or “two people”) and supports similarity search, based on similar object occurrences.
Hybrid Context-Aware Word Sense Disambiguation in Topic Modeling Based Document Representation

We propose a hybrid context based topic model for word sense disambiguation in document representation. Document representation is an essential part of various document based tasks, and word sense disambiguation is to capture the distinctions of word senses in the representation. Traditional methods mainly rely on knowledge libraries for data enrichment; however, semantics division for a word may vary from different domain-specific datasets. We aim to discover more particular word semantic differences for each input dataset and handle the disambiguation problem without data enrichment. The challenge for this disambiguation is to (1) divide various senses for each polysemous word while (2) preserve the differences between synonyms. Most of the existing models are either based on separate context clusters or integrating an auxiliary module to specify word senses. They can hardly achieve both (1) and (2) since different senses of a word are assumed to be independent and their intrinsic relationships are ignored. To solve this problem, we estimate a word sense by both the context in which it occurs and the contexts of its other occurrences. Besides, we introduce the “Bag-of-Senses” (BoS) assumption: a document is a multiset of word senses, and the senses are generated instead of the words. Our experiments on three standard datasets show that our proposal outperforms other state-of-the-art methods in terms of accuracy of word sense estimation, topic modeling, and document classification.
Real-Time Recommendation System for Online Broadcasting Advertisement

In online broadcasting, users are exposed to advertisements for various items. Traditional advertising systems do not satisfy the expectations of various users because they provide advertisements without considering the characteristics of individuals. Personalized advertisement services can be provided by introducing recommendation algorithms that take account of users' context and history. However, since the existing recommendation system is based on users' consumption history, it does not quickly reflect the users' interests that change according to items appearing in the content. In addition, when the user's history is sparse, the performance of the recommendation system is degraded. In this paper, we propose a recommendation system for online broadcasting advertisements. The proposed system calculates the similarity between users based on the user's region of interest (ROI). The user's preference for the item is predicted by comparing the rating history of similar users. To reduce the time for calculating the similarity between users, a tree-structured user profile model is introduced. Finally, we conduct experiments to evaluate the performance of the proposed advertisement recommendation system.
Human action recognition and art interaction based on convolutional neural network

Although great efforts and progress have been made in human action classification, there are still some problems that have not been completely solved. In addition, the application of artificial intelligence in art has become a major trend, the development of this field is slow, the research of the art interaction technology has great research value in the field of artificial intelligence. Based on convolutional neural network(CNN), this paper studies the human behavior detection algorithm and its interactive key technology in the art field. Through the important frame extraction, joint point optimization, and the improved bottom-up algorithm, the detection effect is improved. At the same time, a new neural network is designed, which is composed of separable multi branch network and style generation network. Firstly, the human skeleton is identified efficiently and the behavior is judged accurately, then Generative Adversarial Networks(GAN) generate the style AI picture based on this. Through cross validation on MPII data set, Kinect data set and the Martial arts data set, the accuracy can reach 93%, which shows the model effect is very good. In addition, the further interactive application of unity3d is used, which can make virtual characters and follow the change of detection situation effectively. Our work provide the foundation for the future research of AI art field.
Spatial and Temporal Variations in the Geochemistry of Cauvery River (Tamil Nadu, India) Sediments and its Implication to Weathering

South-West (SW), North-East (NE) and Post-monsoon Cauvery River sediment geochemistry characterizes the intensity of chemical weathering ascribing to the relative mobility of elements during weathering. The negative correlation between Al2O3% and SiO2% signifies that the sediments are enriched with quartz and, Al2O3% vs. TiO2% relationship deduces the granite and granodiorite are the major source rocks of Cauvery River sediment. The weathering trend of Cauvery River sediments is accounted by the incidence of illite clay minerals. The weathering intensity of SW monsoon sediments (57.73%), NE monsoon sediments (64.17%) and post-monsoon sediments (64.79%) specifies the weak to intermediate intensity of weathering, which is controlled by precipitation. The higher concentration of Ba (459.45-856.95ppm) symbolises the presence of K-feldspar.
Blockchain consensus protocols, energy consumption and cryptocurrency prices

Cryptocurrencies employ different consensus protocols to verify transactions. While the “proof-of-work” consensus protocol is the most energy-consuming protocol, “proof-of-stake” and the hybrid of these two consensus protocols, which consume considerably less energy, have also been introduced. We employ portfolio analysis to explore whether energy is a fundamental economic factor affecting cryptocurrency prices. Surprisingly, our results suggest that, on average, cryptocurrencies employing proof-of-work consensus protocols do not generate returns that are significantly different from those that incorporate proof-of-stake consensus protocols. Even more surprising is that our results show that cryptocurrencies that incorporate the hybrid version of these consensus protocols generate significantly higher average returns than the other groups. A possible explanation for this phenomenon may be that the cryptocurrency market is still driven by the trust factor rather than the energy factor.
On the optimal certification of von Neumann measurements

In this report we study certification of quantum measurements, which can be viewed as the extension of quantum hypotheses testing. This extension involves also the study of the input state and the measurement procedure. Here, we will be interested in two-point (binary) certification scheme in which the null and alternative hypotheses are single element sets. Our goal is to minimize the probability of the type II error given some fixed statistical significance. In this report, we begin with studying the two-point certification of pure quantum states and unitary channels to later use them to prove our main result, which is the certification of von Neumann measurements in single-shot and parallel scenarios. From our main result follow the conditions when two pure states, unitary operations and von Neumann measurements cannot be distinguished perfectly but still can be certified with a given statistical significance. Moreover, we show the connection between the certification of quantum channels or von Neumann measurements and the notion of $q$-numerical range.
Random Number Generation from Internal LFSR and Fluctuation of Sampling Interval

An unpredictable random number generator (URNG) adopts a deterministic algorithm with volatile internal states of a microprocessor, which makes the output of the URNG practically unpredictable. This study examines the URNG design proposed by Suciu et al., wherein performance counters are considered as entropy sources. Our experiments confirm that the URNG with performance counters requires a relatively long sampling interval with a background task to produce a high-quality random sequence. On this basis, we propose a new URNG design that is suitable for embedded systems. A simple 128-bit LFSR (Linear Feedback Shift Register) is built in a processor, whose lower 32-bit value is used as a random number. If an adequate sampling interval is maintained, the derived values pass the DIEHARD test.
Chapter 25. Model Counting

Model counting, or counting the number of solutions of a propositional formula, generalizes SAT and is the canonical #P-complete problem. Surprisingly, model counting is hard even for some polynomial-time solvable cases like 2-SAT and Horn-SAT. Efficient algorithms for this problem will have a significant impact on many application areas that are inherently beyond SAT, such as bounded-length adversarial and contingency planning, and, perhaps most importantly, general probabilistic inference. Model counting can be solved, in principle and to an extent in practice, by extending the two most successful frameworks for SAT algorithms, namely, DPLL and local search. However, scalability and accuracy pose a substantial challenge. As a result, several new ideas have been introduced in the last few years that go beyond the techniques usually employed in most SAT solvers. These include division into components, caching, compilation into normal forms, exploitation of solution sampling methods, and certain randomized streamlining techniques using special constraints. This chapter discusses these techniques, exploring both exact methods as well as fast estimation approaches, including those that provide probabilistic or statistical guarantees on the quality of the reported lower or upper bound on the model count.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection

We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. We provide a new dataset of ~40,000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation. It includes ~15,000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate. Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets. We show that model performance is substantially improved using this approach. Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick. They also perform better on HateCheck, a suite of functional tests for online hate detection. We provide the code, dataset and annotation guidelines for other researchers to use. Accepted at ACL 2021.
Where to now? Searching beyond Medline

This article looks at the tools available to you to extend your search beyond the major bibliographic sources. The article identifies the type of literature you can find and which tools are suitable to use to find them. It aims to help you to broaden the scope of your search to find more relevant material. There is a warning about predatory journals and the need to take a critical approach to material that has not been peer-reviewed.
Deep linear discriminant analysis hashing

The conventional supervised hashing methods based on classification do not entirely meet the requirements of the hashing technique; on the other hand, the linear discriminant analysis (LDA) approach does satisfy such demands. In this paper, we propose to perform the LDA objective over deep networks to learn efficient hashing codes in a truly end-to-end fashion. However, a complicated eigenvalue decomposition within each mini-batch in every epoch has to be faced with when simply optimizing the deep network with respect to the LDA objective. Here, the LDA objective is transformed into a simple least-square problem, which naturally overcomes the intractable problems and can be easily solved by an off-the-shelf optimizer. Such deep extension can also overcome the weaknesses of LDA hashing involving the limited linear projection and feature learning. Numerous experiments were conducted on three benchmark datasets. The proposed deep LDA hashing approach exhibits an improvement of nearly 70 points for the CIFAR-10 dataset over the conventional strategy. Additionally, the proposed approach is found to be superior to several state-of-the-art methods for various metrics.
A Gradient Flow Framework For Analyzing Network Pruning

Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed to prune trained models. Despite lacking justification for their use early-on in training, such measures result in surprisingly low accuracy loss. To better explain this behavior, we develop a general framework that uses gradient flow to unify state-of-the-art importance measures through the norm of model parameters. We use this framework to determine the relationship between pruning measures and evolution of model parameters, establishing several results related to pruning models early-on in training: (i) magnitude-based pruning removes parameters that contribute least to reduction in loss, resulting in models that converge faster than magnitude-agnostic methods; (ii) loss-preservation based pruning preserves first-order model evolution dynamics and is therefore appropriate for pruning minimally trained models; and (iii) gradient-norm based pruning affects second-order model evolution dynamics, such that increasing gradient norm via pruning can produce poorly performing models. We validate our claims on several VGG-13, MobileNet-V1, and ResNet-56 models trained on CIFAR-10/CIFAR-100.
Discovering a set of policies for the worst case reward

We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward function defined as a linear combination of known features. We consider a specific class of policy compositions which we call set improving policies (SIPs): given a set of policies and a set of tasks, a SIP is any composition of the former whose performance is at least as good as that of its constituents across all the tasks. We focus on the most conservative instantiation of SIPs, set-max policies (SMPs), so our analysis extends to any SIP. This includes known policy-composition operators like generalized policy improvement. Our main contribution is an algorithm that builds a set of policies in order to maximize the worst-case performance of the resulting SMP on the set of tasks. The algorithm works by successively adding new policies to the set. We show that the worst-case performance of the resulting SMP strictly improves at each iteration, and the algorithm only stops when there does not exist a policy that leads to improved performance. We empirically evaluate our algorithm on a grid world and also on a set of domains from the DeepMind control suite. We confirm our theoretical results regarding the monotonically improving performance of our algorithm. Interestingly, we also show empirically that the sets of policies computed by the algorithm are diverse, leading to different trajectories in the grid world and very distinct locomotion skills in the control suite.
Accurate Learning of Graph Representations with Graph Multiset Pooling

Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed hierarchical graph pooling methods, on the other hand, may yield the same representation for two different graphs that are distinguished by the Weisfeiler-Lehman test, as they suboptimally preserve information from the node features. To tackle these limitations of existing graph pooling methods, we first formulate the graph pooling problem as a multiset encoding problem with auxiliary information about the graph structure, and propose a Graph Multiset Transformer (GMT) which is a multi-head attention based global pooling layer that captures the interaction between nodes according to their structural dependencies. We show that GMT satisfies both injectiveness and permutation invariance, such that it is at most as powerful as the Weisfeiler-Lehman graph isomorphism test. Moreover, our methods can be easily extended to the previous node clustering approaches for hierarchical graph pooling. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks with high memory and time efficiency, and obtains even larger performance gain on graph reconstruction and generation tasks.
Delphi method for evaluating scientific research proposals in a neutrosophic environment

The scientific research proposal is part of the task to be carried out in academic and research institutions around the world. This is a complex decision-making problem, because decision-makers must determine the projects that are appropriate to the subjects addressed by the institution, those projects must be achievable within a reasonable deadline, they must have the financial means and the budget necessary to be carried out, the staff must be sufficiently qualified and an optimum number ofpersonnel must be available tosucceed the tasks and not interfere with other research projects. This is a predictive problem, thus, the proposed model is based on Delphi method for evaluating research projects and is supported by neutrosophy. Delphi method is widely applied in the prediction of future events, in this model we introduce the uncertainty and indeterminacy modeled with neutrosophy. As the best of our knowledge, this model is the first one, which applies a neutrosophic Delphi method in the evaluation of scientific research proposals. Finally, a hypothetical case study illustrates the applicability of the method.