Reviewerly/exHarmony huggingface dataset:
AI-driven banking services: the next frontier for a personalised experience in the emerging market

Purpose Artificial intelligence (AI) is used by banking services primarily to automate systems; however, this ecosystem does not work in emerging markets because human intervention is needed, and there are concerns related to infrastructure. There is plenty of research on AI-mediated banking services, but the existing discussions are cumbersome, and studies on AI's service features in banking for emerging markets are limited. Furthermore, the ongoing discussions are centred on developed markets where automation in banking services is noteworthy and accepted. Through this paper, the authors emphasise the relevance of AI mediation in emerging markets and the possible role of strategising AI in banking services for personalised experiences. Design/methodology/approach The authors' article followed an exploratory, inductive approach through in-depth interviews and thematic analysis. In total, 36 financial experts were interviewed, and the relevant perspectives were analysed to develop the research process and framework for a personalised banking experience. Findings The authors' paper introduced five key themes and presented those themes accordingly. The first theme details the importance of AI-mediated banking and the skills necessary for operational capacity. The second theme is on the relevance of AI-mediated banking awareness amongst users. The third is about channelling the importance of AI-driven interfaces through managers and employees. Fourth, the authors emphasised the relevance of human intervention due to users' demographic patterns. The fifth theme led to a discussion on personalised AI-mediated banking services. Research limitations/implications The authors recommend that managers understand the relevance of quality service amongst users. The authors' paper discusses the relevance of AI and human intervention in banking services; however, the process for seamless, personalised banking experiences is not provided. Thus, this paper encourages managers to build a banking ecosystem that delivers a seamless banking experience through AI. Originality/value The authors' paper highlights the importance of human intervention in AI-driven banking by introducing personalised service experience elements and highlighting the role of customer experience in AI-driven banking services in emerging markets.
SpatioTemporal focus for skeleton-based action recognition

Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition due to their powerful ability to model data topology. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. First, the predefined graph structures are shared throughout the network, lacking the flexibility and capacity to model the multi-grain semantic information. Second, the relations among the global joints are not fully exploited by the graph local convolution, which may lose the implicit joint relevance. For instance, actions such as running and waving are performed by the co-movement of body parts and joints, e.g., legs and arms, however, they are located far away in physical connection. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information from the body joints and parts. As a result, more explainable representations for different skeleton action sequences can be obtained by MCF. In this study, we follow the common practice that the dense sample strategy of the input skeleton sequences is adopted and this brings much redundancy since number of instances has nothing to do with actions. To reduce the redundancy, a temporal discrimination focus module, termed TDF, is developed to capture the local sensitive points of the temporal dynamics. MCF and TDF are integrated into the standard GCN network to form a unified architecture, named STF-Net. It is noted that STF-Net provides the capability to capture robust movement patterns from these skeleton topology structures, based on multi-grain context aggregation and temporal dependency. Extensive experimental results show that our STF-Net significantly achieves state-of-the-art results on three challenging benchmarks NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-Skeleton.
A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction – the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.
A Privacy-Aware and Incremental Defense Method Against GAN-Based Poisoning Attack

Federated learning is usually utilized as a fraud detection framework in the domain of financial risk management, which promotes the model accuracy without training data exchange. One of the challenges in federated learning is the GAN-based poisoning attack. The GAN-based poisoning attack is a type of intractable poisoning attack that causes global model accuracy degradation and privacy leak. Most of the existing defenses for GAN-based poisoning attack have the three problems: 1) dependence on validation datasets; 2) incompetence of dealing with incremental poisoning attack; and 3) privacy leak. To address the above problems, we present a privacy-aware and incremental defense (PID) method to detect malicious participants and protect privacy. In PID, we design a method to accumulate the offset of model parameters from participants in all current epochs to represent the moving tendency for model parameters. Thus, we can distinguish the adversaries from normal participants based on the accumulations in this incremental poisoning attack. We also use multiple trust domains to reduce the rate of misjudging benign participants as adversaries. Moreover, a differentiated differential privacy is utilized before the global model sending to protect the privacy of participants’ training datasets in PID. The experiments conducted on two real-world datasets under financial fraud detection scenario demonstrate that the PID reduces the fallout of adversaries detection (the rate of misjudging benign participants as adversaries) by at least 51.1% and improve the speed of detecting all malicious participants by at least 33.4% compared with two popular defense methods. Besides, the privacy preserving of PID is also effective.
Deep Isolation Forest for Anomaly Detection

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets; our model also inherits desired scalability from iForest.
Weighted Outlier Detection of High-Dimensional Categorical Data Using Feature Grouping

We propose a weighted outlier mining method called WATCH to identify outliers in high-dimensional categorical datasets. WATCH is composed of two distinctive modules: 1) feature grouping by the virtue of correlation measurement among features and 2) outlier mining by assigning scores to objects in each feature groups. At the heart of WATCH is the feature grouping module, which groups an array of features into multiple groups to discover various aspects of feature patterns in each group. The outlier mining module detects outliers from high-dimensional categorical datasets. Except for the number of outliers specified by users, WATCH is conducive to bypassing the optimization of any user-given parameter. We implement and evaluate WATCH using synthetic and real-world datasets. Our experimental results show that WATCH is a promising and practical algorithm to detect outliers in high-dimensional categorical datasets, because WATCH achieves high performance in terms of precision, efficiency, and interpretability.
Verifying Liquidity of Bitcoin Contracts

A landmark security property of smart contracts is liquidity: in a non-liquid contract, it may happen that some funds remain frozen. The relevance of this issue is witnessed by a recent liquidity attack to the Ethereum Parity Wallet, which has frozen $${\sim }160M$$ USD within the contract, making this sum unredeemable by any user. We address the problem of verifying liquidity of Bitcoin contracts. Focussing on BitML, a contracts DSL with a computationally sound compiler to Bitcoin, we study various notions of liquidity. Our main result is that liquidity of BitML contracts is decidable, in all the proposed variants. To prove this, we first transform the infinite-state semantics of BitML into a finite-state one, which focusses on the behaviour of any given set of contracts, abstracting the context moves. With respect to the chosen contracts, this abstraction is sound and complete. Our decision procedure for liquidity is then based on model-checking the finite space of states of the abstraction.
Revisiting Non-Malleable Secret Sharing

A threshold secret sharing scheme (with threshold t) allows a dealer to share a secret among a set of parties such that any group of t or more parties can recover the secret and no group of at most $$t-1$$ parties learn any information about the secret. A non-malleable threshold secret sharing scheme, introduced in the recent work of Goyal and Kumar (STOC'18), additionally protects a threshold secret sharing scheme when its shares are subject to tampering attacks. Specifically, it guarantees that the reconstructed secret from the tampered shares is either the original secret or something that is unrelated to the original secret. In this work, we continue the study of threshold non-malleable secret sharing against the class of tampering functions that tamper each share independently. We focus on achieving greater efficiency and guaranteeing a stronger security property. We obtain the following results:
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

We study the problem of learning to map, in an unsupervised way, between domains A and B, such that the samples b in B contain all the information that exists in samples a in A and some additional information. For example, ignoring occlusions, B can be people with glasses, A people without, and the glasses, would be the added information. When mapping a sample a from the first domain to the other domain, the missing information is replicated from an independent reference sample b in B. Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image. Our solution employs a single two-pathway encoder and a single decoder for both domains. The common part of the two domains and the separate part are encoded as two vectors, and the separate part is fixed at zero for domain A. The loss terms are minimal and involve reconstruction losses for the two domains and a domain confusion term. Our analysis shows that under mild assumptions, this architecture, which is much simpler than the literature guided-translation methods, is enough to ensure disentanglement between the two domains. We present convincing results in a few visual domains, such as no-glasses to glasses, adding facial hair based on a reference image, etc.
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

We explore and expand the $\textit{Soft Nearest Neighbor Loss}$ to measure the $\textit{entanglement}$ of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical tool, it provides insights into the evolution of class similarity structures during learning. Surprisingly, we find that $\textit{maximizing}$ the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures. Maximizing the soft nearest neighbor loss in the hidden layers leads not only to improved generalization but also to better-calibrated estimates of uncertainty on outlier data. Data that is not from the training distribution can be recognized by observing that in the hidden layers, it has fewer than the normal number of neighbors from the predicted class.
DPOD: Dense 6D Pose Object Detector in RGB images

In this work we propose a new method for simultaneous object detection and6DoF pose estimation. Unlike most recent techniques for CNN-based objectdetection and pose estimation, we do not base our approach on the common 2Dcounterparts, i.e. SSD and YOLO, but propose a new scheme. Instead ofregressing 2D or 3D bounding boxes, we output full-sized 2D images containingmulticlass object masks and dense 2D-3D correspondences. Having them at hand, a6D pose is computed for each detected object using the PnP algorithmsupplemented with RANSAC. This strategy allows for substantially better poseestimates due to a much higher number of relevant pose correspondences.Furthermore, the method is real-time capable, conceptually simple and not boundto any particular detection paradigms, such as R-CNN, SSD or YOLO. We test ourmethod for single- and multiple-object pose estimation and compare theperformance with the former state-of-the-art approaches. Moreover, wedemonstrate how to use our pipeline when only synthetic renderings areavailable. In both cases, we outperform the former state-of-the-art by a largemargin.
Regulatory Fitness: Fintech, Funny Money, and Smart Contracts

This article argues that there are many questions that lawyers might ask, and conversations that they might have, about smart contracts; that some questions that are asked are more important than others; and that there are some questions that are not asked but which should be asked. First, it is argued that the question that preoccupies 'coherentists' (concerning the application of the law of contract to smart contracts, and the fit between smart contracts and the paradigmatic 'fiat contracts' that are recognised by the law of contract) is neither as puzzling nor as important as might be supposed. Secondly, it is argued that, if there are concerns about the acceptability of smart contracts, then the conversation that needs to be had is of a 'regulatory-instrumentalist' nature; in particular, if the question is one of public policy restrictions on the use of smart contracts, then the appropriate balance of interests needs to be made by an institution that has both the necessary mandate and the appropriate mind-set. Thirdly, it is argued that there are conversations that we currently do not have but which urgently need to be had. Blockchain is a potentially transformative technology and it is important to have more fundamental conversations about the kind of community that we want to be.
A further study on the inequality constraints in stochastic configuration networks

Stochastic Configuration Networks (SCNs) can be incrementally constructed by using supervisory mechanisms on the selection of random weights and biases. Due to its ease in implementation, fast training and less human intervention, SCNs become increasingly popular for large-scale data analytics. This paper aims to further study the existing constraint condition used in building SCNs. Two new inequality constraints on random parameters assignment are presented, and a theoretical guidance for the key parameter selection in these constraints is given. The newly proposed inequality constraints enlarge the probability of the constraint holding, which implies a quicker learning process. Experimental results with comparisons indicate that the proposed constraints in this paper can greatly reduce the search time for constructing the hidden nodes.
Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

Summary This paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.
Everything You Should Know about Intel SGX Performance on Virtualized Systems

Intel SGX has attracted much attention from academia and is already powering commercial applications. Cloud providers have also started implementing SGX in their cloud offerings. Research efforts on Intel SGX so far have mainly focused on its security and programmability aspects. However, no work has studied in detail the performance degradation caused by SGX in virtualized systems. Such settings are particularly important, considering that virtualization is the de facto building block of cloud infrastructure, yet often comes with a performance impact. This paper presents the first detailed performance analysis of Intel SGX in a virtualized system compareed against bare-metal.Based on our findings, we identify several optimization strategies that would result in performance improvements of Intel SGX on such systems.
Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting

We estimate the Expected Shortfall (ES) of four major cryptocurrencies using various error distributions and GARCH-type models for conditional variance. Our aim is to examine which distributions perform better and to check what component of the specification plays a more important role in estimating ES. We evaluate the performance of the estimations using a rolling-window backtesting technique. Our results highlight the importance of estimating the ES of Bitcoin using a generalized GARCH model and a non-normal error distribution with at least two parameters. Though the results for other cryptocurrencies are less clear-cut, heavy-tailed distributions continue to outperform the normal distribution.
Managing workflow of customer requirements using machine learning

Customer requirements – product specifications issued by the customer – organize the dialog between suppliers and customers and, hence, affect the dynamics of supply networks. These large and complex documents are frequently updated over time, while changes are seldom marked by the customers who issue the requirements. The lack of structure and defined responsibilities, thus, demands an expert to manually process the requirements. Here, the possibility to improve the usual workflow with machine learning algorithms is explored. The whole requirements management process has two major bottlenecks, which can be automatized. The first one, detecting changes, can be accomplished via a document comparison tool. The second one, recognizing the responsibilities and assigning them to the right department, can be solved with standard machine learning algorithms. Here, such algorithms are applied to a dataset obtained from a global automotive industry supplier. The proposed method improves the requirements management process by reducing an expert's workload and thus decreasing the time for processing one document was reduced from 2 weeks to 1 h. Moreover, the method gives a high accuracy of department assignment and can self-improve once implemented into a requirements management system. Although the machine learning methods are very popular nowadays, they are seldom used to improve business processes in real companies, especially in the case of processes that did not require digitalization in the past. Here we show, how such methods can solve some of the management problems and improve their workflow.