Reviewerly/exHarmony huggingface dataset:
Poche: A Priority-Based Flow-Aware In-Network Caching Scheme in Data Center Networks

Datacenters currently deploy shallow-buffered switches to achieve low latency by avoiding long waiting time in the data plane. However, the limited buffer space in the switch causes the frequent overflow and the notorious TCP incast problem. Moreover, the simple scheduling strategy in buffer deprives the switch of the ability to offer deeply differentiated services. Therefore, we present a novel priority-based flow-aware in-network caching scheme, named Poche, which supplies more control capabilities for the network side through introducing some additional cache resource into switches. Poche classifies network traffic into multiple priorities according to the latency requirements of flows. The end server adds priority tags to packets and sets different RTO values for flows with distinct priorities. The switch monitors the buffer utilization of each port and performs the priority-based flow-aware caching and injecting strategies based on the analysis of the scheduling model between the buffer and cache. We conduct comprehensive experiments to compare Poche with the state-of-the-art traffic optimization schemes. The results demonstrate that Poche can reduce the FCTs of latency-sensitive flows by at least 59.1% and improve the network throughput by at least 54.4%, while ensuring the finite cached volume and effectively addressing the incast problem.
AdaBoost-MICNN: a new network framework for pulsar candidate selection

ABSTRACT Pulsar observation and research are of great significance. With the gradual increase in the performance and quantity of observing equipment, the received pulsar observation data also increase geometrically. Machine learning can mine large-scale pulsar data sets to realize automatic pulsar candidate identification. However, the pulsar candidate and non-pulsar candidate data sets are highly imbalanced. The traditional classifier trained on the data set with imbalanced class distribution usually shows poor generalization performance, which will cause the classifier to be biased towards negative instances. To address the problem of poor identification algorithm performance caused by sample imbalance, we describe a new framework, namely AdaBoost-multi-input-CNN (AdaBoost-MICNN). In AdaBoost, multiple baseline models are trained sequentially, and the weight of each training sample changes as the training progresses. Especially the minority samples, which are more easily ignored, will get more attention in subsequent baseline models. This will solve the problem of imbalanced classification. Meanwhile, there are usually four pulsar diagnostic plots. To automatically extract and integrate these four diagnostic plots features, we use multi-input-CNN (MICNN) as the baseline model of AdaBoost. The attention mechanism is introduced to enhance the performance of MICNN’s extraction of features, and design an objective function that adds the maximum mean discrepancy to enhance the anti-interference ability of the model. Finally, the proposed method is tested in the High Time Resolution Universe Medlat Data, and the experimental results verify the effectiveness and efficiency of the method.
Dynamic asymmetric dependence and portfolio management in cryptocurrency markets

As a new form of digital assets based on blockchain technology, the cryptocurrency has received increasing attention from researchers and practitioners. However, less attention has been paid to their joint dynamics from the perspective of portfolio management. This paper investigates the dependence dynamics across four major cryptocurrencies and their economic importance in portfolio management using the data from January 2014 to June 2020. Our empirical analysis shows that significant economic gains can be obtained from modelling dynamic asymmetric dependence among cryptocurrencies. We show that our results are robust to the period of the recent market fluctuations caused by COVID-19. • We investigate the joint dependence across cryptocurrencies. • We explore the economic importance of acknowledging cryptocurrency dependence. • We verify the dynamic asymmetric dependence across cryptocurrencies. • Modelling the dynamic asymmetric dependence can obtain significant economic value. • Our results are robust during the period of the COVID-19 pandemic.
Aggregating Frame-Level Information in the Spectral Domain With Self-Attention for Speaker Embedding

Most pooling methods in state-of-the-art speaker embedding networks are implemented in the temporal domain. However, due to the high non-stationarity in the feature maps produced from the last frame-level layer, it is not advantageous to use the global statistics (e.g., means and standard deviations) of the temporal feature maps as aggregated embeddings. This motivates us to explore stationary spectral representations and perform aggregation in the spectral domain. In this paper, we propose attentive short-time spectral pooling (attentive STSP) from a Fourier perspective to exploit the local stationarity of the feature maps. In attentive STSP, for each utterance, we compute the spectral representations through a weighted average of the windowed segments within each spectrogram by attention weights and aggregate their lowest spectral components to form the speaker embedding. Because most of the feature map energy is concentrated in the low-frequency region of the spectral domain, attentive STSP facilitates the information aggregation by retaining the low spectral components only. Attentive STSP is shown to consistently outperform attentive pooling on VoxCeleb1, VOiCES19-eval, SRE16-eval, and SRE18-CMN2-eval. This observation suggests that applying segment-level attention and leveraging low spectral components can produce discriminative speaker embeddings.
Identifying Deception as a Critical Component of Visualization Literacy

The ability to recognize misleading data visualizations is a key aspect of visualization literacy. In this article, we argue that learning to successfully identify a deceptive graphic requires strategies that deliberately force learners to take an active role in the visualization process. We describe a series of experiments where three groups of learners were shown various deceptive graphics and asked to answer a series of questions. Three different interventions were analyzed to compare the educational effectiveness of the strategies used to engage learners into the process of identifying deceptive visualizations. Our results suggest that the ability to identify deceptive visualizations must be explicitly taught as a core element of visualization literacy. Although both traditional and self-learning approaches are beneficial, the more active the intervention, the higher its educational effectiveness.
V2I-CARLA: A Novel Dataset and a Method for Vehicle Reidentification-Based V2I Environment

Vehicle to infrastructure (V2I) is the current development trend for modern-day intelligent transportation systems and corresponds to an emerging topic in the field of automatic driving. In the perceptual task of road vehicle collaboration, vehicle cross-camera matching plays a key role in achieving automatic driving vehicles overview blind zone perception. Existing reidentification (REID) methods for vehicles cross-camera matching ignore the vehicle detailed characteristics. Considering the shortage of vision distance and a single perspective overlapping problem from the existing datasets, this article builds a vehicle REID dataset V2I-CARLA based on an automatic driving simulator, thereby matching the same vehicle successfully when the common feature is few at different perspectives. For the REID task, this article proposes a method for vehicle REID by introducing a pyramid method, which tends to obtain multiscale features of the vehicle under different cameras, capturing subtle features difference between the vehicles with a similar appearance. Simultaneously, circle loss is used in this work for network optimization, and the network is more distinguished. We evaluated our method on the V2I-CARLA and VeRi776 datasets. The experimental results show the superiority of our method, which renders significant improvement over the baseline on different datasets. The dataset V2I-CARLA is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Yx1322441675/V2I-CARLA</uri> .
Structured Handling of Scoped Effects

Abstract Algebraic effects offer a versatile framework that covers a wide variety of effects. However, the family of operations that delimit scopes are not algebraic and are usually modelled as handlers, thus preventing them from being used freely in conjunction with algebraic operations. Although proposals for scoped operations exist, they are either ad-hoc and unprincipled, or too inconvenient for practical programming. This paper provides the best of both worlds: a theoretically-founded model of scoped effects that is convenient for implementation and reasoning. Our new model is based on an adjunction between a locally finitely presentable category and a category of functorial algebras . Using comparison functors between adjunctions, we show that our new model, an existing indexed model, and a third approach that simulates scoped operations in terms of algebraic ones have equal expressivity for handling scoped operations. We consider our new model to be the sweet spot between ease of implementation and structuredness. Additionally, our approach automatically induces fusion laws of handlers of scoped effects, which are useful for reasoning and optimisation.
On the Linear Convergence of Natural Policy Gradient Algorithm

Markov Decision Processes are classically solved using Value Iteration and Policy Iteration algorithms. Recent interest in Reinforcement Learning has motivated the study of methods inspired by optimization, such as gradient ascent. Among these, a popular algorithm is the Natural Policy Gradient, which is a mirror descent variant for MDPs. This algorithm forms the basis of several popular RL algorithms such as Natural actor-critic, TRPO, PPO, etc, and so is being studied with growing interest. It has been shown that Natural Policy Gradient with constant step size converges with a sublinear rate of $\mathcal{O}(1/k)$ to the global optimal. In this paper, we present improved finite time convergence bounds, and show that this algorithm has geometric (also known as linear) asymptotic convergence rate. We further improve this convergence result by introducing a variant of Natural Policy Gradient with adaptive step sizes. Finally, we compare different variants of policy gradient methods experimentally.
An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

Electronic medical records (EMRs) include many valuable data about patients, which is, however, unstructured. Therefore, there is a lack of both labeled medical text data in Russian and tools for automatic annotation. As a result, today, it is hardly feasible for researchers to utilize text data of EMRs in training machine learning models in the biomedical domain. We present an unsupervised approach to medical data annotation. Syntactic trees are produced from initial sentences using morphological and syntactical analyses. In retrieved trees, similar subtrees are grouped using Node2Vec and Word2Vec and labeled using domain vocabularies and Wikidata categories. The usage of Wikidata categories increased the fraction of labeled sentences 5.5 times compared to labeling with domain vocabularies only. We show on a validation dataset that the proposed labeling method generates meaningful labels correctly for 92.7% of groups. Annotation with domain vocabularies and Wikidata categories covered more than 82% of sentences of the corpus, extended with timestamp and event labels 97% of sentences got covered. The obtained method can be used to label EMRs in Russian automatically. Additionally, the proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabulary.
A Hierarchical Consensus Attention Network for Feature Matching of Remote Sensing Images

Feature matching, referring to establishing high reliable correspondences between two or more scenes with overlapping regions, is of extremely significance to various remote sensing (RS) tasks, such as panorama mosaic and change detection. In this work, we propose an end-to-end deep network for mismatch removal, named hierarchical consensus attention network (HCA-Net), which is one of the critical steps in matching pipeline. Unlike existing practices, our HCA-Net does not rely on global geometric constraints and handcrafted structural representations. The key principle of the proposed HCA-Net is to adaptively enhance neighborhood consensus before evaluating correspondence. To this end, we design a consensus attention mechanism to regularize sparse matches directly. More specifically, consensus attention consists of two novel operations: an encoder–decoder module for calculating compatibility scores and a context-based density representation module. Such attention mechanism can be easily plugged into the existing inlier/outlier classification model in a stacked way to reject outliers. We also propose a hierarchical global-aware network for further improving the accuracy of outlier detection. We compare the proposed HCA-Net with seven state-of-the-art algorithms on several datasets (including various RS images), and the results reveal that our method significantly outperforms the other competitors.
On the Dynamics and Feasibility of Transferred Inference for Diagnosis of Invasive Ductal Carcinoma: A Perspective

It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show that through the use of skip-connections, higher performance metrics are attainable through deeper architectures. However, most evaluative metrics converge on a log scale as we go deeper with diminishing gradient of the metrics&#x2019; curves. Given these two contrasting speculations, in this paper, we implement various vanilla and SOTA CNNs for the diagnosis of one of the most common forms of breast cancer - invasive ductal carcinoma (IDC) - to examine and understand the feasibility of implementation of SOTA CNNs through transferred weights when juxtaposed with vanilla CNNs (and LeNet-5) of varying configurations in terms of their performance metrics and other parameters. In this paper, we solve the dual-objective of studying behavioural aspects of avant-garde CNN models (more specifically, VGG16, VGG19, ResNet50, ResNet50V2, MobileNetV2, and DenseNet121) and proper diagnosis of IDC through intermediate neural activations to critically evaluate and theorize the performance of different models. We notice that among all the models, only VGG16, VGG19, LeNet-5 and a selected vanilla CNN through an optimization procedure were the ones to attain the best metrics, shared amongst them.
Regulating Intermediaries to Protect Personality Rights Online—The Case of the German NetzDG

Along with the shift of communication to the internet, hate speechHate speech and misinformation are also relocating to the Net. Germany has attempted to tackle this problem by enacting the infamous Network Enforcement Act (Netzwerkdurchsetzungsgesetz, or NetzDG), which targets online platforms instead of individual speakers. After providing an overview of the specific challenges presented by internet-based communication, this chapter discusses the NetzDG's regulatory concept. It then critically examines the NetzDG's compatibility with the e-Commerce DirectiveECommerce Directive and its impact on fundamental rightsFundamental rights, namely freedom of expression. Based on the findings, the chapter discusses human-rights-friendly alternatives to NetzDG-style platform regulation.
Intraoperative Navigation in Plastic Surgery with Augmented Reality: A Preclinical Validation Study

Augmented reality allows users to visualize and interact with digital images including three-dimensional holograms in the real world. This technology may have value intraoperatively by improving surgical decision-making and precision but relies on the ability to accurately align a hologram to a patient. This study aims to quantify the accuracy with which a hologram of soft tissue can be aligned to a patient and used to guide intervention.A mannequin's face was marked in a standardized fashion with 14 incision patterns in red and nine reference points in blue. A three-dimensional photograph was then taken, converted into a hologram, and uploaded to HoloLens (Verto Studio LLC, San Diego, Calif.), a wearable augmented reality device. The red markings were then erased, leaving only the blue points. The hologram was then viewed through the HoloLens in augmented reality and aligned onto the mannequin. The user then traced the overlaid red markings present on the hologram. Three-dimensional photographs of the newly marked mannequin were then taken and compared with the baseline three-dimensional photographs of the mannequin for accuracy of the red markings. This process was repeated for 15 trials (n = 15).The accuracy of the augmented reality-guided intervention, when considering all trials, was 1.35 ± 0.24 mm. Markings that were positioned laterally on the face were significantly more difficult to reproduce than those centered around the facial midline.Holographic markings can be accurately translated onto a mannequin with an average error of less than 1.4 mm. These data support the notion that augmented reality navigation may be practical and reliable for clinical integration in plastic surgery.
A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification

Real-world applications especially in the fields of social media have produced massive short text streams. Unlike traditional normal texts, these data present the characteristics of short length, high-volume, high-velocity and variable data distribution etc, which lead to the issues of data sparsity and concept drift. It is hence very challenging for existing short text classification algorithms. Therefore, we propose a flexible Long Short-Term Memory (LSTM) ensemble network based short text stream classification approach, which is implemented in a distributed mode while maintaining the high-accuracy advantage of deep learning models. More specifically, external resource based short text embedding using a pretrained embedding model and CNN is first proposed for the solution to the data sparsity of short texts. Second, to adapt to the high-volume and high-velocity short text streams, a flexible LSTM network is developed and implemented in a distributed mode for classifying short text data streams. Third, a concept drift factor is introduced for adapting to the concept drifts caused by the changing of data distributions. Finally, experiments conducted on three real short text data sets demonstrate that as compared with several state-of-the-art short text (stream) classification approaches, the proposed approach can classify short text streams effectively and efficiently while adapting to concept drifts.
Rotating object detection in remote-sensing environment

Deep learning models have become the mainstream algorithm for processing computer vision tasks. In the tasks of object detection, the detection box is usually set as a rectangular box aligned with the coordinate axis, so as to achieve the complete packaging of the object. However, when facing some objects with large aspect ratio and angle, the bounding box must be enlarged, which makes the bounding box contain a large amount of useless background information. In this study, a different approach based on YOLOv5 is adopted. By this means, the angle information dimension is added at the head, and angle regression is also added at the same time of the boundary regression. Then the loss of the boundary box is calculated by combining ciou and smoothl1, so that the obtained boundary box is more closely suitable for the actual object. At the same time, the original dataset tags are also pre-processed to calculate the angle information of interest. The purpose of these improvements is to realize object detection with angles in remote-sensing images, especially for objects with large aspect ratios, such as ships, airplanes, and automobiles. Compared with the traditional and other state-of-the-art arbitrarily oriented object detection model based on deep learning, experimental results show that the proposed method has a unique effect in detecting rotating objects.
A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

According to characteristics of new problems, the process of finding one or more similar cases from the existing cases to get a new solution is called case-based reasoning (CBR). The kernel idea of CBR is similar in cases having similar solutions. CBR can play its best role only by finding cases that are most similar to new problems through some retrieval methods. Currently, commonly used case retrieval algorithms are basically based on mean operator method. Although the difficulty of calculation is low, the accuracy is limited, and if a certain local similarity is low, the overall result can be affected. We introduce the soft likelihood functions into case retrieval, combine it with KNN, and propose a hybrid retrieval strategy, which is a new and softer way to calculate case similarity. The core of our hybrid retrieval strategy is to aggregate the local similarity and feature similarity of cases by soft likelihood functions, so as to obtain the global similarity. And at the same time, take into account the different attitudinal characteristics of the decision-maker, whether optimistic or pessimistic. The accuracy of this strategy is more than 81 $$\%$$ in simulation experiments on real data sets, which verifies its effectiveness.
Similarity-Aware Collaborative Learning for Patient Outcome Prediction

The rapid growth in the use of electronic health records (EHR) offers promises for predicting patient outcomes. Previous works on this task focus on exploiting temporal patterns from sequential EHR data. Nevertheless, such approaches model patients independently, missing out on the similarities between patients, which are crucial for patients’ health risk assessment. Moreover, they fail to capture the fine-grained progression of patients’ status, which assist in inferring the patients’ future status. In this work, we propose a similarity-aware collaborative learning model SiaCo for patient outcome prediction. In particular, we design two similarity measurers and two global knowledge matrices to separately calculate the similarity of patients with different information levels and support collaborative learning between patients. To capture the more fine-grained progression of patients’ status, we design a parallelized LSTM to model the temporal-dependent patterns of patient status. Finally, SiaCo integrates the information learned from two measures and the parallelized LSTM to predict patient outcomes. Extensive experiments are conducted on two real disease datasets. The experimental results demonstrate that SiaCo outperforms the state-of-the-art models for two typical tasks of patient outcome prediction.