Reviewerly/exHarmony huggingface dataset:
Multisource Heterogeneous Domain Adaptation With Conditional Weighting Adversarial Network

Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing HDA studies focus on the single-source scenario. In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains. In this article, we study the multisource HDA problem and propose a conditional weighting adversarial network (CWAN) to address it. The proposed CWAN adversarially learns a feature transformer, a label classifier, and a domain discriminator. To quantify the importance of different source domains, CWAN introduces a sophisticated conditional weighting scheme to calculate the weights of the source domains according to the conditional distribution divergence between the source and target domains. Different from existing weighting schemes, the proposed conditional weighting scheme not only weights the source domains but also implicitly aligns the conditional distributions during the optimization process. Experimental results clearly demonstrate that the proposed CWAN performs much better than several state-of-the-art methods on four real-world datasets.
Segmentation of Handwritten Arabic Graphemes Using a Directed Convolutional Neural Network and Mathematical Morphology Operations

Due to the nature of Arabic handwriting, segmenting words into characters/graphemes is the most difficult and critical task of the recognition system. The present paper proposes an approach to segment handwritten Arabic words into graphemes based on a directed Convolutional Neural Network (CNN) and Mathematical Morphology Operations (MMO). Arabic script is cursive, which means that almost all graphemes are connected via horizontal links; therefore, a technique to remove links will facilitate the segmentation of graphemes. In general, an MMO such as erosion seems suitable for getting the job done, but since Arabic handwriting is difficult, MMOs cause information loss and suffer from many issues such as diacritics and over-traces, which lead to over/under/bad segmentations. To overcome limitations, the present paper addresses these issues in the following order: the over-traces issue is addressed for the first time in the literature; a robust algorithm for diacritics extraction is provided; and finally, the main segmentation algorithm adopts a strategy based on a Partial Dilation (PD)-Global Erosion (GE) technique to combat the information loss issue. The PD phase amplifies important regions, while GE eliminates links between graphemes. The complementarity between PD and GE facilitates the extraction of graphemes and creates resistance against information loss. To properly tackle these difficult problems, this article exploits the robustness of CNNs, so a new directed CNN model is suggested. The idea is to draw the model's attention to certain targeted features, which are selected according to the nature of the problem addressed. The proposed directed CNN is used in all phases of the segmentation process. The experimental results are very encouraging and show that the proposed directed CNN model outperformed basic CNN in many experiments. The results also reveal that the followed strategy improved the ability of MMOs to perform segmentation and to compete with other approaches in this research area.
FedADC: Accelerated Federated Learning with Drift Control

Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.
P2ISE: Preserving Project Integrity in CI/CD Based on Secure Elements

During the past decade, software development has evolved from a rigid, linear process to a highly automated and flexible one, thanks to the emergence of continuous integration and delivery environments. Nowadays, more and more development teams rely on such environments to build their complex projects, as the advantages they offer are numerous. On the security side however, most environments seem to focus on the authentication part, neglecting other critical aspects such as the integrity of the source code and the compiled binaries. To ensure the soundness of a software project, its source code must be secured from malicious modifications. Yet, no method can accurately verify that the integrity of the project’s source code has not been breached. This paper presents P2ISE, a novel integrity preserving tool that provides strong security assertions for developers against attackers. At the heart of P2ISE lies the TPM trusted computing technology which is leveraged to ensure integrity preservation. We have implemented the P2ISE and quantitatively assessed its performance and efficiency.
Modelling Photosynthetic Active Radiation (PAR) through meteorological indices under all sky conditions

In this study, ten-minute meteorological data-sets recorded at Burgos, Spain, are used to develop models of Photosynthetic Active Radiation (PAR) following two different procedures: multilinear regression and Artificial Neural Networks. Ten Meteorological Indices (MIs) are chosen as inputs to the models: clearness index (kt), diffuse fraction (kd), direct fraction (kb), Perez's clear sky index (ɛ), brightness index (Δ), cloud cover (CC), air temperature (T), pressure (P), solar azimuth cosine (cosZ), and horizontal global irradiation (RaGH). The experimental data are clustered according to the sky conditions, following the CIE standard sky classification. A previous feature selection procedure established the most adequate MIs for modelling PAR in clear, partial and overcast sky conditions. RaGH was the common MI used by all models and for all sky conditions. Additional variables were also included: the geometrical parameter, cosZ, and three variables related to the sky conditions, kt,ε, and Δ. Both modelling methods, multilinear regression and ANN, yielded very high determination coefficients (R2) with very close results in the models for each of the different sky conditions. Slight improvements can be observed in the ANN models. The results underline the equivalence of multilinear regression models and ANN models of PAR following previous feature selection procedures.
A method for generation of substitution box based on random selection

Two basic requirements must be met to encrypt data. These requirements are confusion and diffusion properties. In particular, block cipher algorithms are based on a cryptographic component known as substitution-box to provide the need for confusion. Therefore, attack scenarios generally focus on this cryptographic component. There are alternative design approaches for substitution-box design. It is known that each design approach has several advantages and disadvantages. In this study, an alternative method is proposed to address the problems of the substitution-box design approach based on random selection. The success of the proposed method has been tested in three different scenarios. Analysis results for these three scenarios showed that generated substitution-box provides performance increase for nonlinearity criterion from s-box design criteria. Successful results achieved; It points out that improvement cryptographic components can be used in a variety of practical applications such as block ciphers, masks to prevent side-channel attacks, random number generators, and image encryption algorithms.
Decomposed bounded floats for fast compression and queries

Modern data-intensive applications often generate large amounts of low precision float data with a limited range of values. Despite the prevalence of such data, there is a lack of an effective solution to ingest, store, and analyze bounded, low-precision, numeric data. To address this gap, we propose Buff, a new compression technique that uses a decomposed columnar storage and encoding methods to provide effective compression, fast ingestion, and high-speed in-situ adaptive query operators with SIMD support.
Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.
Bayesian learning of stochastic dynamical models

A new methodology for rigorous Bayesian learning of high-dimensional stochastic dynamical models is developed. The methodology performs parallelized computation of marginal likelihoods for multiple candidate models, integrating over all state variable and parameter values, and enabling a principled Bayesian update of model distributions. This is accomplished by leveraging the dynamically orthogonal (DO) evolution equations for uncertainty prediction in a dynamic stochastic subspace and the Gaussian Mixture Model-DO filter for inference of nonlinear state variables and parameters, using reduced-dimension state augmentation to accommodate models featuring uncertain parameters. Overall, the joint Bayesian inference of the state, model equations, geometry, boundary conditions, and initial conditions is performed. Results are exemplified using two high-dimensional, nonlinear simulated fluid and ocean systems. For the first, limited measurements of fluid flow downstream of an obstacle are used to perform joint inference of the obstacle's shape, the Reynolds number, and the O(105) fluid velocity state variables. For the second, limited measurements of the concentration of a microorganism advected by an uncertain flow are used to perform joint inference of the microorganism's reaction equation and the O(105) microorganism concentration and ocean velocity state variables. When the observations are sufficiently informative about the learning objectives, we find that our posterior model probabilities correctly identify either the true model or the most plausible models, even in cases where a human would be challenged to do the same.
Persistent rule-based interactive reinforcement learning

Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Additionally, the information provided by each interaction is not retained and instead discarded by the agent after a single-use. In this work, we propose a persistent rule-based interactive reinforcement learning approach, i.e., a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Our experimental results show persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer. Moreover, rule-based advice shows similar performance impact as state-based advice, but with a substantially reduced interaction count.
Refiner

Modern mobile applications often produce decentralized data, i.e., a huge amount of privacy-sensitive data distributed over a large number of mobile devices. Techniques for learning models from decentralized data must properly handle two natures of such data, namely privacy and massive engagement. Federated learning (FL) is a promising approach for such a learning task since the technique learns models from data without exposing privacy. However, traditional FL methods assume that the participating mobile devices are honest volunteers. This assumption makes traditional FL methods unsuitable for applications where two kinds of participants are engaged: 1) self-interested participants who, without economical stimulus, are reluctant to contribute their computing resources unconditionally, and 2) malicious participants who send corrupt updates to disrupt the learning process. This paper proposes Refiner, a reliable federated learning system for tackling the challenges introduced by massive engagements of self-interested and malicious participants. Refiner is built upon Ethereum, a public blockchain platform. To engage self-interested participants, we introduce an incentive mechanism which rewards each participant in terms of the amount of its training data and the performance of its local updates. To handle malicious participants, we propose an audit scheme which employs a committee of randomly chosen validators for punishing them with no reward and preclude corrupt updates from the global model. The proposed incentive and audit scheme is implemented with cryptocurrency and smart contract, two primitives offered by Ethereum. This paper demonstrates the main features of Refiner by training a digit classification model on the MNIST dataset.
Combat Security Alert Fatigue with AI-Assisted Techniques

The main challenge for security information and event management (SIEM) is to find critical security incidents among a huge number of false alerts generated from separate security products. To address the alert fatigue problem that is common for security experts operating the SIEM, we propose a new alert screening scheme that leverages artificial intelligence (AI)-assisted tools to distinguish actual threats from false alarms without investigating every alert. The proposed scheme incorporates carefully chosen learning algorithms and newly designed visualization tools to facilitate speedy alert analysis and incident response. The proposed scheme is evaluated on an alert dataset collected in the security operation center of an enterprise. With a recall rate of 99.598% for highly critical alerts and a false positive rate of 0.001% reported, the proposed scheme demonstrated very promising potential for real world security operations. We believe the proposed scheme is effective in addressing the alert fatigue problem, and therefore paves the way for a consolidated security solution for network security at the enterprise level.
I’ll be there for you? Effects of Islamophobic online hate speech and counter speech on Muslim in-group bystanders’ intention to intervene

Online hate speech is very common. This is problematic as degrading social groups can traumatize targets, evoke stress, and depression. Since no reaction of others could suggest the acceptability of hate speech, bystander intervention is essential. However, it is unclear when and how minorities react to hate speech. Drawing from social identity theory and research on in-group intervention, we inquire how Islamophobic online hate speech and counter speech by majority or minority members shape Muslims’ willingness to intervene. Thus, in an online experiment ( N = 362), we varied the presence of Islamophobic online hate speech and counter speech by a (non-) Muslim. Results showed that Islamophobic online hate speech led to a perceived religious identity threat which, in turn, increased the personal responsibility to intervene and resulted in higher intentions to utter factual counter speech. In addition, counter speech by both majority and minority members directly reduced Muslims’ intentions to counterargue hatefully.
Echo State Network Based Soft Sensor for Monitoring and Fault Detection of Industrial Processes

In this paper a semi-automatic computationally inexpensive system is developed and implemented for monitoring and fault detection of industrial processes. The system uses a soft sensor based on Echo State Networks (ESN) and is able to capture the non-linear dynamic relationships in the process data, making it convenient for real-time monitoring applications. The soft sensor is set to simulate normal operating conditions, so that when the process is governed by other causes, possibly in failure, high residues occur and allow the failure identification. In addition, the system monitors the reliability of the model predictions by tracking the internal states of the ESN dynamic reservoir, indicating whether the model predictions can be used instead of the measured data. The system is successfully applied to the Mackey-Glass Anomaly Benchmark (MGAB) and to the monitoring of critical pieces of equipment of a real oil and gas plant.
A weighted word embedding based approach for extractive text summarization

Automatic text summarization (ATS) is a method to condense a long size text document into abridging form by enveloping all the primary information and central theme. Numerous ATS models have already prospected in this direction. However, many of those do not capture the semantic features and latent meanings of the text documents. In this paper, we present a weighted word vector representation method concerning TF-IDF for ATS. The proposed model is a prospective method for huge data on the internet that can catch all possible semantic meanings from the text along with the statistical and linguistic features. The proposed word vectors help to strengthen the diversity of the generated summary by discriminating semantically dissimilar sentences. Besides, we evaluate the proposed model on news articles taken from DUC 2007 dataset using the ROUGE summary evaluation metric. Moreover, we compare the proposed model against the four state-of-the-art summarization models and observe that our proposed approach outperforms among all the baselines and able to produce coherent, meaningful, diverse, and least redundant summaries.
Adopting threat modelling in agile software development projects

The goal of secure software engineering is to create software that keeps performing as intended, even when exposed to attacks. Threat modelling is considered to be a key activity to reach this goal, but has turned out to be challenging to implement in agile teams. This paper presents results from four different studies, in which we have investigated how agile teams do threat modelling today. Study A is based on observations and document analysis from five teams in a single organisation, Study B is based on interviews with eight individuals from four different organisations, Study C is based on a questionnaire survey of 45 students at two different universities, and Study D is based on interviews with seven teams in a single organisation, supplemented with document analysis. Our results include findings, challenges and current good practice related to the use of Data Flow Diagrams, STRIDE and the Microsoft Threat Modelling Tool. We also cross-check our findings with previous relevant work, and provide recommendations for making the threat modelling activities more useful to agile teams.
A multi-task learning approach for improving travel recommendation with keywords generation

Travel recommendation is very critical to helping users quickly find products or services that they are interested in. The key to travel recommender systems is learning user shopping intentions, which are expressed through various supervision signals, such as the clicked products and their titles. Existing travel recommendation methods commonly infer user intentions from click behaviors on travel products. However, remarkable keywords in the product title, such as departure, destination, travel time, hotel, and transportation are paid less attention. To this end, we hypothesize that modeling click sequences and product keywords in title jointly would result in a more holistic representation of a product and towards more accurate recommendations. Thus, we propose a TRKG (short for Travel Recommendation with Keywords Generation) model, which fulfills the travel recommendation and keywords generation tasks simultaneously. To generate explainable outputs, unlike most previous approaches that regard the product title as a hidden feature vector, TRKG regards keywords in the product title as an additional supervision signal. Meanwhile, TRKG integrates the long-term and short-term user preferences in the travel recommendation component and the keywords generation component. To evaluate the proposed model, we constructed datasets from a large tourism e-commerce website in China. Extensive experiments demonstrate that the proposed method yields significant improvements over state-of-the-art methods.
Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.