Reviewerly/exHarmony huggingface dataset:
An Extensive Formal Security Analysis of the OpenID Financial-Grade API

Forced by regulations and industry demand, banks worldwide are working to open their customers' online banking accounts to third-party services via web-based APIs. By using these so-called Open Banking APIs, third-party companies, such as FinTechs, are able to read information about and initiate payments from their users' bank accounts. Such access to financial data and resources needs to meet particularly high security requirements to protect customers. One of the most promising standards in this segment is the OpenID Financial-grade API (FAPI), currently under development in an open process by the OpenID Foundation and backed by large industry partners. The FAPI is a profile of OAuth 2.0 designed for high-risk scenarios and aiming to be secure against very strong attackers. To achieve this level of security, the FAPI employs a range of mechanisms that have been developed to harden OAuth 2.0, such as Code and Token Binding (including mTLS and OAUTB), JWS Client Assertions, and Proof Key for Code Exchange. In this paper, we perform a rigorous, systematic formal analysis of the security of the FAPI, based on an existing comprehensive model of the web infrastructure - the Web Infrastructure Model (WIM) proposed by Fett, Küsters, and Schmitz. To this end, we first develop a precise model of the FAPI in the WIM, including different profiles for read-only and read-write access, different flows, different types of clients, and different combinations of security features, capturing the complex interactions in a web-based environment. We then use our model of the FAPI to precisely define central security properties. In an attempt to prove these properties, we uncover partly severe attacks, breaking authentication, authorization, and session integrity properties. We develop mitigations against these attacks and finally are able to formally prove the security of a fixed version of the FAPI. Although financial applications are high-stakes environments, this work is the first to formally analyze and, importantly, verify an Open Banking security profile. By itself, this analysis is an important contribution to the development of the FAPI since it helps to define exact security properties and attacker models, and to avoid severe security risks before the first implementations of the standard go live. Of independent interest, we also uncover weaknesses in the aforementioned security mechanisms for hardening OAuth 2.0. We illustrate that these mechanisms do not necessarily achieve the security properties they have been designed for.
An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.
Graph Embedding-Based Dimension Reduction With Extreme Learning Machine

Dimension reduction (DR)-based on extreme learning machine auto-encoder (ELM-AE) has achieved many successes in recent years. By minimizing the self-reconstruction error, the ELM-AE-based DR algorithms learn the compressed representations which facilitate the subsequent classification. However, the existing ELM-AEs only consider the DR problem in an unsupervised manner and ignore the valuable supervised information when these information is available. To find discriminative features of the original data, in this paper, we propose a graph embedding-based DR framework with ELM (GDR-ELM) for DR problems. Instead of self-reconstruction, the proposed GDR-ELM reconstructs all samples according to the weights in a graph matrix containing the supervised information. Furthermore, GDR-ELM can be stacked as building blocks to construct a multilayer framework like other ELM-AEs for more complicated representation learning tasks. Experiments on various datasets demonstrate the effectiveness of the proposed GDR-ELM and its multilayer framework.
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup

Deep neural networks achieve stellar generalisation even when they have enough parameters to easily fit all their training data. We study this phenomenon by analysing the dynamics and the performance of over-parameterised two-layer neural networks in the teacher-student setup, where one network, the student, is trained on data generated by another network, called the teacher. We show how the dynamics of stochastic gradient descent (SGD) is captured by a set of differential equations and prove that this description is asymptotically exact in the limit of large inputs. Using this framework, we calculate the final generalisation error of student networks that have more parameters than their teachers. We find that the final generalisation error of the student increases with network size when training only the first layer, but stays constant or even decreases with size when training both layers. We show that these different behaviours have their root in the different solutions SGD finds for different activation functions. Our results indicate that achieving good generalisation in neural networks goes beyond the properties of SGD alone and depends on the interplay of at least the algorithm, the model architecture, and the data set.
Finding Generalizable Evidence by Learning to Convince Q&amp;A Models

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based questionanswering (QA) as a testbed.We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage.Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans.Given its general nature, this approach improves QA in a robust manner: using agentselected evidence (i) humans can correctly answer questions with only ⇠20% of the full passage and (ii) QA models can generalize to longer passages and harder questions.
Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning

With the rapid growth of generative adversarial networks (GANs), a photo-realistic image can be easily generated from a low-dimensional random vector nowadays. However, the generated image can be used to synthesize several persons who may have a potential effect on society with radical contents. Considering that many techniques to produce a photo-realistic facial image based on different GANs are already available, collecting training images of all possible generative models is difficult; hence, the learning-based approach would not effectively detect a fake image generated using an excluded generative model. To overcome this shortcoming, we propose a two-step pairwise learning approach to learn common fake features over the training images generated by using different generative models. First, the triplet loss will be used to simulate the relation between fake and real images and utilized to learn the discriminative features to determine whether an image is real or fake. Then, we propose a novel coupled network to accurately capture local and global image features of the fake or real images. The experimental results demonstrate that the proposed method outperforms the baseline supervised learning methods for fake facial image detection.
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning

Knowledge graphs (KGs) often suffer from sparseness and incompleteness.Knowledge graph reasoning provides a feasible way to address such problems.Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance.However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs.In this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods.DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods without extra reward engineering.
Computer Game in UNITY Environment for BCI Technology

Currently, the market of available technologies for creating computer games is very large and is still dynamically expanding. 1990s were a breakthrough in the history of computer games development when network games and 3D computer graphics were introduced into the games. ‘TIE Fighter’ space simulator from 1994 was the first representative of this technology. Presently, the Unity engine for creating computer games is one of the most popular programming environments, the first version of which, i.e. Unity 1.0.0, was created by: Dawid Helgason, Joachim Ante and Mikołaj Franciszek in Denmark and launched into the market on 6 June 2005. Not long ago new technologies, discussed in the previous chapters of this monograph, such as Augmented Reality (AR) and Virtual Reality (VR) entered the marked and are still dynamically being developed. The brain–computer interface technology is the latest technology which slowly makes its way into the field of games.
Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models

This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalised cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some cryptopredictors are included in the analysis, such as S&amp;P 500 and Nikkei 225. In this paper, the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility, the student-t distribution is shown to outperform the standard normal approach.
Online active learning for human activity recognition from sensory data streams

Human activity recognition (HAR) is highly relevant to many real-world domains like safety, security, and in particular healthcare. The current machine learning technology of HAR is highly human-dependent which makes it costly and unreliable in non-stationary environment. Existing HAR algorithms assume that training data is collected and annotated by human a prior to the training phase. Furthermore, the data is assumed to exhibit the true characteristics of the underlying distribution. In this paper, we propose a new autonomous approach that consists of novel algorithms. In particular, we adopt active learning (AL) strategy to selectively query the user/resident about the label of particular activities in order to improve the model accuracy. This strategy helps overcome the challenge of labelling sequential data with time dependency which is highly time-consuming and difficult. Because of the changes that may affect the way activities are performed, we regard sensor data as a stream and human activity learning as an online continuous process. In such process the leaner can adapt to changes, incorporate novel activities and discard obsolete ones. To this extent, we propose a novel semi-supervised classifier (OSC) that works together with a novel Bayesian stream-based active learning (BSAL). Because of the changes in the sensor layouts across different houses’ settings, we use Conditional Restricted Boltzmann Machine (CRBM) to handle the features engineering issue by learning the features regardless of the environment settings. CRBM is then applied to extract low-level features from unlabelled raw high-dimensional activity input. The resulting approach will then tackle the challenges of activity recognition using a three-module architecture composed of a feature extractor (CRBM), an online semi-supervised classifier (OSC) equipped with BSAL. CRBM-BSAL-OSC allows completely autonomous learning that adjusts to the environment setting, explores the changes and adapt to them. The paper provides the theoretical details of the proposed approach as well as an extensive empirical study to evaluate the performance of the approach.
BERT4SessRec

This paper describes our solution for the Content-Based Video Relevance Prediction (CBVRP) challenge, where the task is to predict user click-through behavior on new TV series or new movies according to the user's historical behavior. We consider the task as a session-based recommendation problem and we focus on the modeling of the session. Thus, we use the Bidirectional Encoder Representations from Transformer (BERT) methodology and propose a BERT for session-based recommendation (BERT4SessRec) method. Our method has two stages: in the pre-training stage, we use all sessions as training data and train the bidirectional session encoder with the masking trick; in the fine-tuning stage, we use the provided click-through data and train the click-through prediction network. Our method achieves session representations with the help of BERT, which effectively captures the bidirectional correlation in each session. In addition, the pre-training stage makes full use of all sessions, overcoming the positive-negative imbalance problem of the click-through data. We report the results of using different kinds of features on the test set of the challenge, which verify the effectiveness of our method.
Human 3D Pose Estimation with a Tilting Camera for Social Mobile Robot Interaction

Human–Robot interaction represents a cornerstone of mobile robotics, especially within the field of social robots. In this context, user localization becomes of crucial importance for the interaction. This work investigates the capabilities of wide field-of-view RGB cameras to estimate the 3D position and orientation (i.e., the pose) of a user in the environment. For that, we employ a social robot endowed with a fish-eye camera hosted in a tilting head and develop two complementary approaches: (1) a fast method relying on a single image that estimates the user pose from the detection of their feet and does not require either the robot or the user to remain static during the reconstruction; and (2) a method that takes some views of the scene while the camera is being tilted and does not need the feet to be visible. Due to the particular setup of the tilting camera, special equations for 3D reconstruction have been developed. In both approaches, a CNN-based skeleton detector (OpenPose) is employed to identify humans within the image. A set of experiments with real data validate our two proposed methods, yielding similar results than commercial RGB-D cameras while surpassing them in terms of coverage of the scene (wider FoV and longer range) and robustness to light conditions.
Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and Spiking algorithm

Benefiting from the advantages of a wide spatial sampling range and strong continuity, hyperspectral analysis provides a potential way to detect heavy metals in soil. However, it is still a great challenge to identify the spectral response characteristics of heavy metals from naturally polluted soil samples. This paper innovatively produces near standard soil samples for exploring the exact spectral response of cadmium (Cd) in soil and presents a novel method by combining the direct standardization (DS) and Spiking algorithms for integrating multisource spectra to improve the accuracy of Cd concentration estimation. A total of 46 naturally polluted soil samples were collected from a known Cd-contaminated mining area in Xiangjiang River Basin, China. The soil spectra of the naturally polluted soil samples were synchronously measured in the field. Moreover, clean soils with low heavy metal contaminants were collected to produce 65 near standard soil samples with known Cd levels. Then, the spectra and Cd concentrations of all 111 soil samples were measured under laboratory conditions. The principle component stepwise regression (PCSR) analysis results illustrated that the reflectance at all the wavelengths (380–2460 nm) is indicative of the differences in the soil Cd concentrations. Among these, the sensitivity of the spectral reflectance is the strongest at approximately 400 nm, 1000 nm and above 2300 nm. Additionally, the integrated multisource spectra significantly improved the accuracy of soil Cd concentration estimation (coefficient of determination, R2 = 0.96; root mean square error, RMSE = 0.29; ratio of prediction to deviation, RPD = 1.21) when 30 transfer samples and 15 training samples were simultaneously implemented in the combined DS and Spiking algorithm. This will provide a feasible scheme for exploration of spectral response characteristics of multiple soil heavy metals, and highlight the potential of developing low-level and satellite remote sensing on a large scale.
An Empirical Study to Investigate the Impact of Communication Issues in GSD in Pakistan’s IT Industry

Global software development (GSD) practice has been increasingly emerging in the recent few decades in the field of business and software industry.On the one hand, many software development organizations get the benefits of GSD, including but not limited to reduced cost, cheap labor, round the clock working and skilled professionals.On the other hand, these organizations have to face several challenges because of GSD.These challenges pose serious threats to the stability of the GSD projects.Communication between distributed team members is one of the most crucial challenges in GSD.Therefore, the current study aims to identify the communication risk in GSD and also evaluate the impact of these communication risks in GSD environment.A Systematic Literature Review (SLR) has been performed to identify all the communication-related issues in GSD.After that, a conceptual framework has been proposed for evaluating the impact of those issues on communication risk in GSD.An empirical evaluation has been performed on data collected from the software organizations of Pakistan working in GSD based environment.The finding of our study demonstrates that geographical distance, socio-temporal distance, socio-culture distance, team member's attitude, team issues, organizational & architectural issue and customer issue have a significant direct impact on communication risk in GSD.The study also shows that there is a significant correlation between findings of SLR and empirical investigation (r = 0.460, P = 0.005).Further, we believe that the results of our research can help to tackle the issues related to communication in GSD.Therefore, it will help to improve the performance of the development activities of GSD organizations.
Missing data imputation over academic records of electrical engineering students

Abstract Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm (AAA) based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms.
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks

Empirical studies show that gradient-based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. Very recently, a line of work explains in theory that with over-parameterization and proper random initialization, gradient-based methods can find the global minima of the training loss for DNNs. However, existing generalization error bounds are unable to explain the good generalization performance of over-parameterized DNNs. The major limitation of most existing generalization bounds is that they are based on uniform convergence and are independent of the training algorithm. In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. Our work sheds light on explaining the good generalization performance of over-parameterized deep neural networks.
New Interpretations of Normalization Methods in Deep Learning

In recent years, a variety of normalization methods have been proposed to help training neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, some necessary tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help to stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.