Reviewerly/exHarmony huggingface dataset:
Recognizing the content types of network traffic based on a hybrid DNN-HMM model

Protocol identification and application classification for network traffic have been well studied in the past two decades, due to their importance for network management and security defense. One of the challenges to most of existing work comes from the onion-like characteristics of modern network traffic, which enables the actual transmission content or service to be disguised by the external protocols or applications and to be unrecognizable. In some scenarios, unrecognizable traffic may lead to incorrect network management policies and create favorable conditions for cyber attacks. In contrast to most of the existing research that merely focuses on the identification of external protocols and applications, in this work we explore a new scheme for content types recognition by traffic behavior, in which it does not need to inspect the external protocols or applications. The proposed scheme is based on three mature technologies, including Gaussian mixture model (GMM), hidden Markov model (HMM) and deep neural network (DNN). The GMM-HMMs are used to capture the underlying time-varying behavior patterns for the network traffic carrying a specific type of content. To eliminate the instability and limitations caused by the general GMM-HMMs, a shared DNN is derived and combined with the trained HMMs to implement the final recognition of the content types for network traffic. We introduce the architecture and rationale of the proposed scheme in details, derive the algorithms for content recognition, and evaluate its performance with multiple baseline methods via real network traffic. The experiment results not only demonstrate that the proposed scheme is able to accurately and stably recognize the content types of network traffic, but also verify the performance of the proposed scheme on the discrimination for similar and short traffic.
Underwater Image Recovery Using Structured Light

Structured lighting techniques have increasingly been employed in underwater imaging.Except for the limitation in underwater illumination, the desire of underwater image acquisition comes from the applicability of removal of the back scattered light, the critical problem of underwater imaging, using structured lighting techniques.This paper presents an approach for underwater image recovery using structured light and CCD camera.By integrating the scanned frame images, we generate an integration image which can be formulated as the convolution of the surface albedo and the illumination function.Thus, underwater image acquisition is addressed as an optimization problem of image recovery and resolved by deconvolution, rather than the traditional geometric manipulation of frame tiling.The significance of the proposed method is that the forward scattering effect in the recovered image is fully eliminated by the integration operation and collection of the forward scattered light enhances the total imaging energy.By means of the structured lighting technique, an algorithm of using virtual aperture to limit the field of view is proposed to eliminate the back scattered light in frame images.Concerned with applicability of using the broad lighting pattern in structured light systems, the coded structured light with binary pseudorandom sequence is introduced, by which the high-frequency details in image recovery can be preserved through deconvolution.The results of underwater experiments are given.
Towards a 5G Vehicular Architecture

In the coming years, the vehicular networks should be integrated in a global 5G network. Indeed, the 5G system aims to respond to a wide range of services including Ultra-Reliable and Low-Latency Communications (URLLC) and, therefore, vehicular communications. However, the current Cooperative-Intelligent Transport Systems (C-ITS) architecture does not meet the key design recommendations of the 5G architecture design. Thus, this integration is not possible yet. Indeed, five different improvements should be considered to make it possible: network intelligence and automation, edge data processing and interoperability, network control, environment virtualization and finally security and privacy. Different papers have already proposed architectures enabling some of these improvements through the integration of different technologies in the C-ITS architecture. To highlight their strengths and their limits, these states-of-the-art solutions are compared. None of them propose a solution meeting the five identified improvements. That is why this paper presents a new architecture taking all these improvements into account. In particular, to complete the existing work, automation, security, privacy and trust are considered. To do so, a knowledge plane enhancing the functioning of the whole architecture is designed. Moreover, a security and privacy plane, strengthening trust in the vehicular environment thanks to Blockchain, is proposed. The role of the different components of this four-plane architecture is also described. Finally, some of the main challenges of the deployment of the proposed architecture are discussed.
Adapting sequence to sequence models for text normalization in social media

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-theshelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.
A Constructor-Based Reachability Logic for Rewrite Theories

Reachability logic has been applied to 𝕂 rewrite-rule-based language definitions as a language-generic logic of programs to verify a wide range of sophisticated programs in conventional languages. Here we study how reachability logic can be made not just language-generic, but also rewrite-theory-ge neric, so that we can verify both conventional programs based on their rewriting logic operational semantics and distributed system designs specified as rewrite theories. A theory-generic reachability logic is presented and proved sound for a wide class of rewrite theories. Particular attention is given to increasing the logic’s automation by means of constructor-based semantic unification, matching, narrowing, and satisfiability procedures. The relationships to Hoare logic and LTL are discussed, new methods for proving invariants of possibly never terminating distributed systems are developed, and experiments with a prototype implementation illustrating the new methods are presented.
Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning

Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news.However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred.As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis.To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level.Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability.Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90 %, exceeding the performance of alternative approaches by at least 3.80 percentage points.Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.
Discrete Trust-aware Matrix Factorization for Fast Recommendation

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time

We give a polynomial-time algorithm for learning neural networks with one layer of sigmoids feeding into any Lipschitz, monotone activation function (e.g., sigmoid or ReLU). We make no assumptions on the structure of the network, and the algorithm succeeds with respect to {\em any} distribution on the unit ball in $n$ dimensions (hidden weight vectors also have unit norm). This is the first assumption-free, provably efficient algorithm for learning neural networks with two nonlinear layers. Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that combines isotonic regression with kernel methods. It outputs a hypothesis that yields efficient oracle access to interpretable features. It also suggests a new approach to Boolean learning problems via real-valued conditional-mean functions, sidestepping traditional hardness results from computational learning theory. Along these lines, we subsume and improve many longstanding results for PAC learning Boolean functions to the more general, real-valued setting of {\em probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i.d. noise-tolerance.
Strategic Tasks for Explainable Reinforcement Learning

Commonly used sequential decision making tasks such as the games in the Arcade Learning Environment (ALE) provide rich observation spaces suitable for deep reinforcement learning. However, they consist mostly of low-level control tasks which are of limited use for the development of explainable artificial intelligence(XAI) due to the fine temporal resolution of the tasks. Many of these domains also lack built-in high level abstractions and symbols. Existing tasks that provide for both strategic decision-making and rich observation spaces are either difficult to simulate or are intractable. We provide a set of new strategic decision-making tasks specialized for the development and evaluation of explainable AI methods, built as constrained mini-games within the StarCraft II Learning Environment.
Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

The monitoring utilization and workloads of computer hardware components, such as CPU, RAM, bus, and storage, are an ideal way to evaluate the effectiveness of these components. In this paper, we surveyed the basic concepts, characteristics, and parameters of computer systems that determine system performance, and the types of models that provide adequate modeling of these systems. We investigated and developed the applied aspects of the theory of fuzzy sets’ principles and the Matlab environment tools for monitoring and evaluating the state of computing systems. The idea of the paper is to identify the state of the computer infrastructure by using the models of Mamdani and Sugeno FIS (fuzzy inference system) to evaluate the impact of RAM and storage on CPU performance. With this approach, we observed the behavior of computer infrastructure. The results are useful for understanding performance issues with regard to specific bottlenecks and determining the correlation of performance counters. Moreover, the model presents linguistic results. Hereafter, performance counter correlations will support the development of algorithms that can detect whether the performance of a given computer will be affected by a reasonable priority. The performance assertions derived from these approaches allow resource management policies to prevent performance degradation, and as a result, the infrastructure will be able to serve safely as expected. These methods can be applied across the entire spectrum of computer systems, from personal computers to large mainframes and supercomputers, including both centralized and distributed systems. We look forward to their continued use, as well as their improvement when it is necessary to evaluate future systems.
Explaining Simple Natural Language Inference

The vast amount of research introducing new corpora and techniques for (semi-)automatically annotating corpora shows the important role that datasets play in today's research, especially in the machine learning community.This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently discussed with respect to the Natural Language Inference (NLI) task.In this work we conduct an annotation experiment based on a small subset of the SICK corpus.The experiment reveals several problems in the annotation guidelines, and various challenges of the NLI task itself.Our quantitative evaluation of the experiment allows us to assign our empirical observations to specific linguistic phenomena and leads us to recommendations for future annotation tasks, for NLI and possibly for other tasks.
Counterfactual exchange of unknown quantum states

We show that two unknown quantum states, either pure or mixed, can be bidirectionally ``teleported'' counterfactually. In the process of information exchange, there is no physical particle traveling between two communicators. Unlike conventional quantum teleportation, our protocol requires neither prearranged entangled pairs nor Bell measurements. We entangle and disentangle a photon and an atom through multiple measurements in a special sequence. These multiple measurements can smoothly but reversibly control system evolution. The total effect of multiple measurements can be considered as a unitary time evolution operator. Therefore, our protocol can be regarded as a counterfactual quantum swap gate. Based on our approach, all known conventional quantum gates can be counterfactualized.
Introduction to Functional Programming

There has been much hype around Functional Programming in recent years. However, Functional Programming is not a new idea and indeed goes right back to the 1950s and the programming language LISP. However, many people are not clear as to what Functional Programming is and instead jump into code examples and never really understand some of the key ideas associated with Functional Programming such as Referential Transparency.
Design and Implementation of an Object Detection System Using Faster R-CNN

Recent development in object detection are greatly driven by the success of region proposal approaches and region-based convolutional neural networks (R-CNNs). In this paper, we designed and implemented an object detection system using a faster-CNN method that shares full-image convolutional features with a detection network, so as to enable nearly cost-free region proposals. Development of this system is based on the previous work on Faster R-CNN. Results shows that with this method, we could achieve high accuracy while detecting objects.
Hierarchical Optimal Transport for Multimodal Distribution Alignment

In many machine learning applications, it is necessary to meaningfully aggregate, through alignment, different but related datasets. Optimal transport (OT)-based approaches pose alignment as a divergence minimization problem: the aim is to transform a source dataset to match a target dataset using the Wasserstein distance as a divergence measure. We introduce a hierarchical formulation of OT which leverages clustered structure in data to improve alignment in noisy, ambiguous, or multimodal settings. To solve this numerically, we propose a distributed ADMM algorithm that also exploits the Sinkhorn distance, thus it has an efficient computational complexity that scales quadratically with the size of the largest cluster. When the transformation between two datasets is unitary, we provide performance guarantees that describe when and how well aligned cluster correspondences can be recovered with our formulation, as well as provide worst-case dataset geometry for such a strategy. We apply this method to synthetic datasets that model data as mixtures of low-rank Gaussians and study the impact that different geometric properties of the data have on alignment. Next, we applied our approach to a neural decoding application where the goal is to predict movement directions and instantaneous velocities from populations of neurons in the macaque primary motor cortex. Our results demonstrate that when clustered structure exists in datasets, and is consistent across trials or time points, a hierarchical alignment strategy that leverages such structure can provide significant improvements in cross-domain alignment.
A General and Balanced Region-Based Metric for Evaluating Medical Image Segmentation Algorithms

Evaluating medical imaging segmentation is a very complex problem. Several papers proposed methodologies and different metrics pursuing more reliable and unbiased procedures. In this paper, we propose a novel accuracy metric which is more balanced than the well known Dice and Jaccard coefficients. We also prove mathematically that the proposed metric generalizes Dice, Jaccard and the previously proposed Balanced Dice and Balanced Jaccard coefficients. Our experiments show that significant changes in brain tissue segmentation evaluation results are noticed as we applied our new metric.
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets

Residual networks (ResNet) and weight normalization play an important role in various deep learning applications. However, parameter initialization strategies have not been studied previously for weight normalized networks and, in practice, initialization methods designed for un-normalized networks are used as a proxy. Similarly, initialization for ResNets have also been studied for un-normalized networks and often under simplified settings ignoring the shortcut connection. To address these issues, we propose a novel parameter initialization strategy that avoids explosion/vanishment of information across layers for weight normalized networks with and without residual connections. The proposed strategy is based on a theoretical analysis using mean field approximation. We run over 2,500 experiments and evaluate our proposal on image datasets showing that the proposed initialization outperforms existing initialization methods in terms of generalization performance, robustness to hyper-parameter values and variance between seeds, especially when networks get deeper in which case existing methods fail to even start training. Finally, we show that using our initialization in conjunction with learning rate warmup is able to reduce the gap between the performance of weight normalized and batch normalized networks.
Cooperative Path Planning for Multiple Robots With Motion Constraints in Obstacle-Strewn Environment

A cooperative path searching approach is proposed to decouple cooperative path planning for multiple robots into the path planning phase and the trajectory tracking phase. Unexpected local environment changes or failures for some robots will not affect the regular running of other robots. The collision between robots and the motion constraints of robots are not considered in the first phase. A new connection point method suitable for the trap-like map is used to find the shortest path for every robot. Connection point method does not require much computation cost even if the grid map is zoomed in. In the second phase, a cooperative search tracking approach based on modified coevolution pigeon-inspired optimization algorithm is proposed to enable every robot to track the grid path obtained by the connection point method. A competition mechanism with serial number priority is used to cope with collisions between robots. The numerical simulations are performed to verify the effectiveness of the proposed approach.
A Chinese Event Relation Extraction Model Based on BERT

Relation extraction and event extraction are important subtasks of information extraction. To identify the relations and events in Chinese text accurately can help to improve the performance of tasks such as graph construction and risk conduction. Different from the traditional methods, this paper proposes a joint model to extract entities and events in the text, and gives the concept of event relation, to discover the potential relations between the arguments of events and the relations between two or more events. We conduct experiments on a financial dataset, the results show that the new model is 4%-6% higher than the existing event extraction model in F1 score, and the proposed event relation is also meaningful and practical.
An Edge Enhanced SRGAN for MRI Super Resolution in Slice-Selection Direction

The low resolution MRI in slice-select direction will lead to information loss and artifacts in 2D multi-slices MRI, which is not conducive to the diagnosis and treatment of diseases. Therefore, we proposed an edge enhanced super-resolution generative adversarial networks (EE-SRGAN) for MRI super resolution in slice-select direction. Firstly, a two-stage super-resolution generator network (TSSR) for solving the problem that the down-sampling ratio of MRI resolution in single direction reached 12 times. In addition, in order to overcome the problem of image smoothness caused by high peak signal-to-noise ratio (PSNR) and improve the visual reality of reconstruction image, we construct a generative adversarial networks based on TSSR. Finally, in order to achieve more texture details, we proposed an edge enhanced loss function to optimize the generator network. From the experimental results, we find that our TSSR is better (increased 1.78 dB PSNR), EE-SRGAN provides more satisfactory visual effect and beneficial to segmentation task (increased 2.14% Dice index) than state-of-art super-resolution network.