Reviewerly/exHarmony huggingface dataset:
Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services

The vigorous development of e-commerce breeds cybercrime.Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce.Behavior-based methods are recognized as a promising method for online payment fraud detection.However, it is a big challenge to build high-resolution behavioral models by using low-quality behavioral data.In this work, we mainly address this problem from data enhancement for behavioral modeling.We extract fine-grained co-occurrence relationships of transactional attributes by using a knowledge graph.Furthermore, we adopt the heterogeneous network embedding to learn and improve representing comprehensive relationships.Particularly, we explore customized network embedding schemes for different types of behavioral models, such as the population-level models, individual-level models, and generalized-agent-based models.The performance gain of our method is validated by the experiments over the real dataset from a commercial bank.It can help representative behavioral models improve significantly the performance of online banking payment fraud detection.To the best of our knowledge, this is the first work to realize data enhancement for diversified behavior models by implementing network embedding algorithms on attribute-level co-occurrence relationships.
Tweaking the Asymmetry of Asymmetric-Key Cryptography on Lattices: KEMs and Signatures of Smaller Sizes

Currently, lattice-based cryptosystems are less efficient than their number-theoretic counterparts (based on RSA, discrete logarithm, etc.) in terms of key and ciphertext (signature) sizes. For adequate security the former typically needs thousands of bytes while in contrast the latter only requires at most hundreds of bytes. This significant difference has become one of the main concerns in replacing currently deployed public-key cryptosystems with lattice-based ones. Observing the inherent asymmetries in existing lattice-based cryptosystems, we propose asymmetric variants of the (module-)LWE and (module-)SIS assumptions, which yield further size-optimized KEM and signature schemes than those from standard counterparts. Following the framework of Lindner and Peikert (CT-RSA 2011) and the Crystals-Kyber proposal (EuroS&P 2018), we propose an IND-CCA secure KEM scheme from the hardness of the asymmetric module-LWE (AMLWE), whose asymmetry is fully exploited to obtain shorter public keys and ciphertexts. To target at a 128-bit quantum security, the public key (resp., ciphertext) of our KEM only has 896 bytes (resp., 992 bytes). Our signature scheme bears most resemblance to and improves upon the Crystals-Dilithium scheme (ToCHES 2018). By making full use of the underlying asymmetric module-LWE and module-SIS assumptions and carefully selecting the parameters, we construct an SUF-CMA secure signature scheme with shorter public keys and signatures. For a 128-bit quantum security, the public key (resp., signature) of our signature scheme only has 1312 bytes (resp., 2445 bytes). We adapt the best known attacks and their variants to our AMLWE and AMSIS problems and conduct a comprehensive and thorough analysis of several parameter choices (aiming at different security strengths) and their impacts on the sizes, security and error probability of lattice-based cryptosystems. Our analysis demonstrates that AMLWE and AMSIS problems admit more flexible and size-efficient choices of parameters than the respective standard versions.
Defense Strategies Against Adversarial Jamming Attacks via Deep Reinforcement Learning

As the applications of deep reinforcement learning (DRL) in wireless communication grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw more attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper study defense strategies against DRL-based jamming attacker on a DRL-based dynamic multichannel access agent. To defend the jamming attacks, we propose three diversified defense strategies: proportional-integral-derivative (PID) control, the use of an imitation attacker and the development of orthogonal policies. We design these strategies and evaluate their performances.
Emote-Controlled

In recent years, streaming platforms for video games have seen increasingly large interest, as so-called esports have developed into a lucrative branch of business. Like for other sports, watching esports has become a new kind of entertainment medium, which is possible due to platforms that allow gamers to live stream their gameplay, the most popular platform being Twitch.tv. On these platforms, users can comment on streams in real time and thereby express their opinion about the events in the stream. Due to the popularity of Twitch.tv, this can be a valuable source of feedback for streamers aiming to improve their reception in a gaming-oriented audience. In this work, we explore the possibility of deriving feedback for video streams on Twitch.tv by analyzing the sentiment of live text comments made by stream viewers in highly active channels. Automatic sentiment analysis on these comments is a challenging task, as one can compare the language used in Twitch.tv with that used by an audience in a stadium, shouting as loud as possible in sometimes nonorganized ways. This language is very different from common English, mixing Internet slang and gaming-related language with abbreviations, intentional and unintentional grammatical and orthographic mistakes, and emoji-like images called emotes . Classic lexicon-based sentiment analysis techniques therefore fail when applied to Twitch comments. To overcome the challenge posed by the nonstandard language, we propose two unsupervised lexicon-based approaches that make heavy use of the information encoded in emotes, as well as a weakly supervised neural network–based classifier trained on the lexicon-based outputs, which is supposed to help generalization to unknown words by use of domain-specific word embeddings. To enable better understanding of Twitch.tv comments, we analyze a large dataset of comments, uncovering specific properties of their language, and provide a smaller set of comments labeled with sentiment information by crowdsourcing. We present two case studies showing the effectiveness of our methods in generating sentiment trajectories for events live streamed on Twitch.tv that correlate well with specific topics in the given stream. This allows for a new kind of implicit real-time feedback gathering for Twitch streamers and companies producing games or streaming content on Twitch. We make our datasets and code publicly available for further research. 1
Rhetorical structure theory: A comprehensive review of theory, parsing methods and applications

Rhetorical structure theory (RST) is a significant theory about discourse organization. With an increasing number of research interests focus on RST, many novel parsing approaches have been proposed and motivated many brand new applications, such as chatbots and other expert and intelligent systems. However, the work on RST dates back many years and there remains a lack of comprehensive literature reviews. The aim of this study is therefore to provide a comprehensive overview of RST, parsing methods and applications. In this paper, we first give a detailed introduction to RST. Then the commonly used discourse treebank: RST-DT is elaborated. We propose a new taxonomy to divide the RST parsing methods into different categories. With a focus on the classical and latest methods that have recently been developed, we review the pros and cons of these approaches, along with the performance analysis of them. We then summarize the applications of RST across various domains. Moreover, we present a comparative study of RST with other discourse structure theories. Finally, we discuss some implications of our findings and outline future research directions in this challenging and fast-growing field.
Post-quantum WireGuard

In this paper we present PQ-WireGuard, a postquantum variant of the handshake in the WireGuard VPN protocol (NDSS 2017).Unlike most previous work on postquantum security for real-world protocols, this variant does not only consider post-quantum confidentiality (or forward secrecy) but also post-quantum authentication.To achieve this, we replace the Diffie-Hellman-based handshake by a more generic approach only using key-encapsulation mechanisms (KEMs).We establish security of PQ-WireGuard, adapting the security proofs for WireGuard in the symbolic model and in the standard model to our construction.We then instantiate this generic construction with concrete post-quantum secure KEMs, which we carefully select to achieve high security and speed.We demonstrate competitiveness of PQ-WireGuard presenting extensive benchmarking results comparing to widely deployed VPN solutions.
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategies

The past decade has witnessed great progress in automatic speech recognition (ASR) due to advances in deep learning. The improvements in performance can be attributed to both improved models and large-scale training data. The key to training such models is the employment of efficient distributed learning techniques. In this article, we provide an overview of distributed training techniques for deep neural network (DNN) acoustic models used for ASR. Starting with the fundamentals of data parallel stochastic gradient descent (SGD) and ASR acoustic modeling, we investigate various distributed training strategies and their realizations in high-performance computing (HPC) environments with an emphasis on striking a balance between communication and computation. Experiments are carried out on a popular public benchmark to study the convergence, speedup, and recognition performance of the investigated strategies.
Lightweight secure image encryption scheme based on chaotic differential equation

The ultimate secure choice for block cryptosystem until now is advanced encryption standard (AES). It is very difficult to implement AES for the constrained situations such as sensor networks, image encryption and RFID tags. In this article, a chaotic oscillator generated by a second order differential equation is used to produce confusion and diffusion in the plaintext message to achieve the desired secrecy. The produced chaotic sequence of random numbers from dynamical system is utilized to scramble the pixels of an image to obtain an encrypted image. Chaos based encryption technique is found secure enough to tackle chosen plaintext attacks and brute force attacks. The specific attributes of chaotic system like, sensitivity to initial conditions, randomness and uncertainty make it suitable for the design of cryptosystem. The dominance of the proposed scheme is acknowledged due to the fact of better cryptographic properties when compared with the algorithms already developed in the literature.
Word-Character Graph Convolution Network for Chinese Named Entity Recognition

Recent researches try to integrate word information into the character-based Chinese NER by modifying the structure of the standard BiLSTM-CRF model. They follow the paradigm of explicitly modeling forward and backward sequences, adopting an LSTM variant that takes both characters and words as input for each direction. Though enriching the representations, these models cannot fully exploit the interaction between future and past contexts. In this paper, we propose a novel word-character graph convolution network (WC-GCN) which uses a cross GCN block to simultaneously process the word-character directed acyclic graphs (DAGs) of two directions. To improve the capture of long-distance dependency, a global attention GCN block is introduced to learn node representations conditioned on a global context. In both blocks, unlike previous works where each word is attached to its associated character or taken as a shortcut between LSTM cells, words and characters are treated equally as nodes in the graph and have their instance-specific representations. Experiments on four widely used datasets show that our proposed model can work standalone or with the standard BiLSTM. Both forms can outperform previous LSTM-based models without training on extra corpora while only an external lexicon and its corresponding pretrained character and word embeddings are needed.
LexOPS: An R package and user interface for the controlled generation of word stimuli

Abstract LexOPS is an R package and user interface designed to facilitate the generation of word stimuli for use in research. Notably, the tool permits the generation of suitably controlled word lists for any user-specified factorial design and can be adapted for use with any language. It features an intuitive graphical user interface, including the visualization of both the distributions within and relationships among variables of interest. An inbuilt database of English words is also provided, including a range of lexical variables commonly used in psycholinguistic research. This article introduces LexOPS, outlining the features of the package and detailing the sources of the inbuilt dataset. We also report a validation analysis, showing that, in comparison to stimuli of existing studies, stimuli optimized with LexOPS generally demonstrate greater constraint and consistency in variable manipulation and control. Current instructions for installing and using LexOPS are available at https://JackEdTaylor.github.io/LexOPSdocs/ .
Optimal Block Withholding Strategies for Blockchain Mining Pools

In proof-of-work (PoW) consensus protocol-based blockchain mining, the pools can increase their rewards by utilizing block withholding attack. As such, how much computational power should be used to attack other pools becomes an important decision issue faced by the pools. This article mainly studies the block withholding attack issue faced by mining pools. Considering the case that there are two pools, where only one pool can attack the other pool, we propose an optimal block withholding strategies for pools. We also illustrate that attacking is not always the optimal strategies for the pools and present the conditions for attacking. With computational experiment approach, we designed several experiments to validate our proposed strategies, and our results can provide important managerial insights for pools in blockchain mining.
Hidden representations in deep neural networks: Part 2. Regression problems

Deep neural networks are an important class of machine learning models useful for representing complex input-output relationships. While their recent success is unparalleled, so is the inability to explain their internal representations. In this second part of a two-part series, we focus on understanding the hidden representations of deep neural networks and the underlying mechanisms for regression problems. We highlight challenges associated with deep neural networks with simple models that help us gain insight into the functioning of the hidden layers and the mechanism of the operation of the network. The article is structured in a tutorial-like fashion for the benefit of new practitioners so that they can appreciate nuances and the pitfalls involved in developing a deep neural network models for regression problems.
Handwritten Arabic Words Recognition System Based on HOG and Gabor Filter Descriptors

Automatic recognition of handwritten Arabic words is a research area opens to a large number of industrial applications. However, the solution to the problem of cursive handwriting recognition still laborious because of the complexity of the morphology of Arabic script. In our work, we study and implement an offline handwritten word recognition system using the IFN / ENIT dataset of Arabic words representing the names of Tunisian cities. We have used two types of descriptors, the oriented gradient histogram (HOG) and Gabor filter for the extraction of features and as a classifier the kNN. Experiments are performed on samples extracted from the IFN/ENIT database. The results highlight the reliability of the kNN classifier for handwritten Arabic word recognition.
Convergence of Constant Step Stochastic Gradient Descent for Non-Smooth Non-Convex Functions

This paper studies the asymptotic behavior of the constant step Stochastic Gradient Descent for the minimization of an unknown function, defined as the expectation of a non convex, non smooth, locally Lipschitz random function. As the gradient may not exist, it is replaced by a certain operator: a reasonable choice is to use an element of the Clarke subdifferential of the random function; another choice is the output of the celebrated backpropagation algorithm, which is popular amongst practioners, and whose properties have recently been studied by Bolte and Pauwels. Since the expectation of the chosen operator is not in general an element of the Clarke subdifferential of the mean function, it has been assumed in the literature that an oracle of the Clarke subdifferential of the mean function is available. As a first result, it is shown in this paper that such an oracle is not needed for almost all initialization points of the algorithm. Next, in the small step size regime, it is shown that the interpolated trajectory of the algorithm converges in probability (in the compact convergence sense) towards the set of solutions of a particular differential inclusion: the subgradient flow. Finally, viewing the iterates as a Markov chain whose transition kernel is indexed by the step size, it is shown that the invariant distribution of the kernel converge weakly to the set of invariant distribution of this differential inclusion as the step size tends to zero. These results show that when the step size is small, with large probability, the iterates eventually lie in a neighborhood of the critical points of the mean function.
SmartSteganogaphy: Light-weight generative audio steganography model for smart embedding application

Massive data is transferred in the Internet of Thing (IoT) every single second. Protecting the security of these data is a crucial task. Steganography is a collection of techniques for concealing the existence of information by embedding it within irrelevant carriers, which could protect security and privacy of the data. Distinct from the cryptography, steganography put emphasis on hiding the existence of the secret. However, the majority of research mainly work on complex algorithm. In this paper, we proposed an audio steganography algorithm which automatically generated from adversarial training. The embedding model is light-weight which could use as machine learning tools in smart device. Besides, the existing audio steganography methods mainly depend on human handcraft, while the proposed method could obtain from meachine learning. The embedding model consists of three neural networks: encoder which embeds the secret message in the carrier, decoder which extracts the message, and discriminator which determines the carriers containing secret messages. The system is trained with different training settings on two datasets. Competed the majority of audio steganographic schemes, the proposed scheme could produce high fidelity steganographic audio. Besides, the extensive experiments demonstrate the robustness and security of our algorithm.
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning

Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations and action policy history, it may incur huge communication overhead while violating the privacy of each agent. Alternatively, this article presents a communication-efficient and privacy-preserving distributed RL framework, coined federated reinforcement distillation (FRD). In FRD, each agent exchanges its proxy experience RM (ProxRM), in which policies are locally averaged with respect to proxy states clustering actual states. To provide FRD design insights, we present ablation studies on the impact of ProxRM structures, neural network architectures, and communication intervals. Furthermore, we propose an improved version of FRD, coined mixup augmented FRD (MixFRD), in which ProxRM is interpolated using the mixup data augmentation algorithm. Simulations in a Cartpole environment validate the effectiveness of MixFRD in reducing the variance of mission completion time and communication cost, compared to the benchmark schemes, vanilla FRD, federated RL (FRL), and policy distillation.
IDIoT: Towards Ubiquitous Identification of IoT Devices through Visual and Inertial Orientation Matching During Human Activity

As Internet-of-Things (IoT) devices become pervasive, opportunities for new, useful services open up. Leveraging existing devices in the environment to enhance the information extracted by personal devices can provide complementary sensing modalities. For instance, an elderly care facility might track progress of their seniors while they exercise by combining basic fitness bracelet data and their motion from vision such as activity/exercise detection. In order for these devices to share and aggregate such information, there needs to be an ID association step, where the devices' physical ID (i.e. the user and location on the body) is matched to their virtual ID (e.g. IP address). Existing approaches to assist this matching process often require intentional interaction, pre-calibration or direct line-of-sight to the device, which become inconvenient as the number of devices increases, or the device is obscured by clothing and body parts. The problem gets worse when devices have multiple users (e.g. family members) or multiple devices are involved simultaneously. We present IDIoT, a calibration-free passive sensing approach that utilizes human-device motion to determine the (user, body location) of each device. IDIoT leverages human pose information of the user, captured by existing 2D cameras (such as on smart TVs), and combines with 3D inertial sensing present on most IoT devices via bone orientation estimation. This way, IDIoT can associate multiple devices even if they are under clothing or in pockets. We extensively characterized IDIoT through real-world experiments and a publicly available dataset with humans wearing 13 on-body devices. Compared to other state-of-the-art baselines, IDIoT achieves up to 2x improvement in device identification, with an average accuracy of up to 92.2%.
Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm

Estimation of software cost and effort is of prime importance in software development process. Accurate and reliable estimation plays a vital role in successful completion of the project. To estimate software cost, various techniques have been used. Constructive Cost Model (COCOMO) is amongst most prominent algorithmic model used for cost estimation. Different versions of COCOMO consider different types of parameters affecting overall cost. Parameters involved in estimation using COCOMO possess vagueness which introduces some degree of uncertainty in algorithmic modelling. The concept of fuzzy logic can deal with uncertainty involved in Intermediate COCOMO cost driver measurements via Fuzzy Inference System (FIS). In the proposed research, an effort has been made wherein, for each cost driver, an FIS is designed to calculate the corresponding effort multiplier. Proposed research provides an insight through evolutionary-based optimization techniques to optimize fuzzy logic-based COCOMO using Particle Swarm Optimization Algorithm. The magnitude of relative error and its mean, calculated using COCOMO NASA2 and COCOMONASA datasets are used as evaluation metrics to validate the proposed model. The model outperforms when compared to other optimization techniques like Genetic Algorithm.