Reviewerly/exHarmony huggingface dataset:
Simtrojan: Stealthy Backdoor Attack

Recent researches indicate deep learning models are vulnerable to adversarial attacks. Backdoor attack, also called trojan attack, is a variant of adversarial attacks. An malicious attacker can inject backdoor to models in training phase. As a result, the backdoor model performs normally on clean samples and can be triggered by a backdoor pattern to recognize backdoor samples as a wrong target label specified by the attacker. However, the vanilla backdoor attack method causes a measurable difference between clean and backdoor samples in latent space. Several state-of-the-art defense methods utilize this to identify backdoor samples. In this paper, we propose a novel backdoor attack method called SimTrojan, which aims to inject backdoor in models stealthily. Specifically, SimTrojan makes clean and backdoor samples have indistinguishable representations in latent space to evade current defense methods. Experiments demonstrate that SimTrojan achieves a high attack success rate and is undetectable by state-of-the-art defense methods. The study suggests the urgency of building more effective defense methods.
An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification

The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods.
On incorrectness logic and Kleene algebra with top and tests

Kleene algebra with tests (KAT) is a foundational equational framework for reasoning about programs, which has found applications in program transformations, networking and compiler optimizations, among many other areas. In his seminal work, Kozen proved that KAT subsumes propositional Hoare logic, showing that one can reason about the (partial) correctness of while programs by means of the equational theory of KAT. In this work, we investigate the support that KAT provides for reasoning about incorrectness, instead, as embodied by O'Hearn's recently proposed incorrectness logic. We show that KAT cannot directly express incorrectness logic. The main reason for this limitation can be traced to the fact that KAT cannot express explicitly the notion of codomain, which is essential to express incorrectness triples. To address this issue, we study Kleene Algebra with Top and Tests (TopKAT), an extension of KAT with a top element. We show that TopKAT is powerful enough to express a codomain operation, to express incorrectness triples, and to prove all the rules of incorrectness logic sound. This shows that one can reason about the incorrectness of while-like programs by means of the equational theory of TopKAT.
Improving the performance of bagging ensembles for data streams through mini-batching

Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and transient behavior. Compared to traditional (batch) data mining, stream processing algorithms have additional requirements regarding computational resources and adaptability to data evolution. They must process instances incrementally because the data's continuous flow prohibits storing data for multiple passes. Ensemble learning achieved remarkable predictive performance in this scenario. Implemented as a set of (several) individual classifiers, ensembles are naturally amendable for task parallelism. However, the incremental learning and dynamic data structures used to capture the concept drift increase the cache misses and hinder the benefit of parallelism. This paper proposes a mini-batching strategy that can improve memory access locality and performance of several ensemble algorithms for stream mining in multi-core environments. With the aid of a formal framework, we demonstrate that mini-batching can significantly decrease the reuse distance (and the number of cache misses). Experiments on six different state-of-the-art ensemble algorithms applying four benchmark datasets with varied characteristics show speedups of up to 5X on 8-core processors. These benefits come at the expense of a small reduction in predictive performance.
Face Mask Detection using CNN

Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person's face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security.
PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems.
Efficient clustering of short text streams using online-offline clustering

Short text stream clustering is an important but challenging task since massive amount of text is generated from different sources such as micro-blogging, question-answering, and social news aggregation websites. The two major challenges of clustering such massive amount of text is to cluster them within a reasonable amount of time and to achieve better clustering result. To overcome these two challenges, we propose an efficient short text stream clustering algorithm (called EStream) consisting of two modules: online and offline. The online module of EStream algorithm assigns a text to a cluster one by one as it arrives. To assign a text to a cluster it computes similarity between a text and a selected number of clusters instead of all clusters and thus significantly reduces the running time of the clustering of short text streams. EStream assigns a text to a cluster (new or existing) using the dynamically computed similarity thresholds. Thus EStream efficiently deals with the concept drift problem. The offline module of EStream algorithm enhances the distributions of texts in the clusters obtained by the online module so that the upcoming short texts can be assigned to the appropriate clusters.
Madrid Quantum Network

The rapid technological advances in the field of quantum communications and the arrival of quantum computers, with a sufficient number of qubits to break the most used encryption algorithms used to date will change the Internet paradigm, as we know it today. Research centers, universities, industries and governments are working hard on the development of more scalable and efficient quantum communications networks, as well as the standardization of protocols, processes and interfaces needed for its industrialization. Many are the definitions of what will be the quantum Internet of the future (its architecture and components), but we offer a more relaxed definition of this concept, so that it is already possible to have a primitive QI with the technology available today, but without all the functionalities and components that will have in the future. As an example, the Madrid Quantum Network meets the requirements of this basic QI, proving to be a first step towards more complex architectures and functionalities. In addition, this concept has direct application as an intranet in restricted military environments, where high levels of security are required and whose extension is limited to metropolitan areas.
Quantum dynamics of mixed polarization states: effects of environment-mediated intermode coupling

We study the quantum dynamics of mixed polarization states represented by the two-mode boson system interacting with an environment. Using the form of general dynamical equations for averaged operators that preserve normal ordering, we show that the dynamical regimes of the averaged Stokes operators crucially depend on the anisotropy of the relaxation operator resulting from the intermode coupling induced by the anisotropic environment. We analyze how this coupling impacts the quantum bit error rate in the well-known single-photon polarization-coding quantum key distribution protocol BB84.
A Concise TSK Fuzzy Ensemble Classifier Integrating Dropout and Bagging for High-Dimensional Problems

Improving the tradeoff between accuracy and interpretability is essential for the problem of handling high-dimensional data in Takagi–Sugeno–Kang (TSK) fuzzy systems and providing insights into real-world tasks. However, the TSK fuzzy system becomes complex and challenging to interpret as the data dimension increases. Here, we report an ensemble classifier, which is an enhanced adaptive network-based fuzzy inference system (ANFIS) integrating improved bagging and dropout to build concise fuzzy rule sets. First, the high-dimensional feature space is decomposed into a series of low-dimensional feature subsets using the bagging and random subspace method to train multiple ANFISs. An improved dropout strategy is then applied in training ANFISs by temporarily disabling rules in each training epoch and deleting rules after training to obtain sparse rulesets with high-quality rules. These sub-models are subsequently aggregated to perform the fuzzy inference. Results on high-dimensional benchmark datasets confirm that both the bagging and dropout strategies are effective, providing high interpretability by reducing the co-firing degrees and rules of sub-models while guaranteeing accuracy at the same time.
Order-guided deep neural network for emotion-cause pair prediction

Emotion-Cause Pair Extraction (ECPE) is a prediction task aiming to extract the emotions and their corresponding causes in a target document. The existing methods for this problem mainly focus on modeling the dependence between emotion clauses and related cause clauses and the interaction among emotion-cause pairs. However, these methods ignore the order information between emotion clauses and their cause clauses, which can be proved useful for the ECPE task. In this paper, we propose an order-guided deep predictive model, which integrates different orders between emotion clauses and their cause clauses into an end-to-end framework to tackle this task. Specifically, we build an order-guided clause encoder with a three-level long-short term memory (LSTM) network to learn the different orders from forward LSTM, backward LSTM and Bi-LSTM, respectively. In this way, the deep networks with different directions can effectively capture different orders, and therefore improve the performance of our model in this prediction task. Additionally, the previous methods use only a shared word encoder to capture word-level emotion and cause information, resulting in paying more attention to emotion information and lacking the ability to capture cause information. In order to overcome this deficiency, we design both an emotion-aware word encoder and a cause-aware word encoder to enhance the ability to capture the emotion and cause information. The experiment results illustrate that our method outperforms the other baselines on two real-world datasets, and demonstrate the effectiveness of the proposed method.
Hybrid Fusion Based Approach for Multimodal Emotion Recognition with Insufficient Labeled Data

In this paper, a deep learning based fusion approach has been proposed to classify the emotions portrayed by image and corresponding text into discrete emotion classes. The proposed method first implements intermediate fusion on image and text inputs and then applies late fusion on image, text, and intermediate fusion’s output. We have also come up with a way to handle the unavailability of labeled multimodal emotional data. We have prepared a new dataset built on Balanced Twitter for Sentiment Analysis dataset (B-T4SA) dataset containing an image, text, and emotion labels, i.e., ‘happy,’ ‘sad,’ ‘hate’ and ‘anger.’ The emotion recognition accuracy of 90.20% has been achieved by the proposed method. Along with multi-class emotion recognition, we’ve also compared the sentiment classification results and found the proposed method to perform better than the benchmark approaches.
Cyclic changes in verbal person-number indexes are unlikely

Abstract This paper discusses the emergence and demise of verbal person-number indexes on the basis of a sample of 310 languages. First, qualitative evidence is provided to show that there are different ways in which indexes may emerge, and that independent anaphoric pronouns are not the only possible source. Second, quantitative evidence is provided against the claim that indexes tend to demise via phonological attrition in the course of time. A considerable degree of demise is not a universally likely process, but rather a major restructuring process that requires additional – areal – triggers in order to come about. Thus, 92% of the languages of my sample do not show any strong tendency toward losing their indexes, and the degree of demise of their indexes is persistently low when compared to the proto-forms. This is despite the fact that indexes constantly change over time, and the phonetic shape found in the proto-languages is never faithfully preserved in the modern languages. Finally, those few languages that exhibit a relatively high degree of demise are not randomly distributed across the world, but are clustered in the following areas: Northwestern Europe, Eastern South East Asia with Oceania and, possibly, Mid Africa as well Northern South America.
Person Re-Identification Based on Joint Loss and Multiple Attention Mechanism

Person re-identification (ReID) is the use of computer vision and machine learning techniques to determine whether the pedestrians in the two images under different cameras are the same person. It can also be regarded as a matching retrieval task for person targets in different scenes. The research focuses on how to obtain effective person features from images with occlusion, angle change, and target attitude change. Based on the present difficulties and challenges in ReID, the paper proposes a ReID method based on joint loss and multi-attention network. It improves the person re-identification algorithm based on global characteristics, introduces spatial attention and channel attention and constructs joint loss function, improving the characteristic extraction ability of the network and improving the model performance of person re-identification. It analyzes the validity and necessity of each module of the algorithm through the ablation experiment. In addition, it carries out training and evaluation on the two person re-recognition data sets Market1501 and MSMT17, and verifies the advantages of the proposed algorithm in contrast to other advanced algorithms.
Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties

For networked multisensor systems (NMSs), a soft measurement model with five uncertainties is presented, which can be viewed as a “soft sensor” such that the measurements received by estimators can be obtained via the computations of this model. A novel “soft sensor” concept is presented. Three new approaches are presented, which include a fictitious white noises approach with compensating random uncertainties, an extended Lyapunov equation approach with three kinds of the Lyapunov equations, and a universal integrated covariance intersection (ICI) fusion approach with integrating local estimators and their cross-covariances. Applying them, the universal ICI and two fast ICI (FICI) fusion time-varying and steady-state minimax robust Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. They improve the robust accuracies of the original covariance intersection (CI) and fast CI(FCI) fusers, and overcome their drawback to require known local estimators and their conservative variances. Their robustness, accuracy relations, stability, steady-state property, and three modes of convergence are proved. The proposed new concept, approaches and results constitute a new methodology, a universal robust fusion Kalman filtering theory and a new filtering stability theory. A simulation example applied to the vehicle suspension system shows their effectiveness and applicability.
Bert-Based Text Keyword Extraction

Abstract With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.
An Evaluation Framework for User Experience Using Eye Tracking, Mouse Tracking, Keyboard Input, and Artificial Intelligence: A Case Study

User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users’ interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website.