OpenRaiser/Intern-Atlas huggingface dataset:
Fully-Automatic Pipeline for Document Signature Analysis to Detect Money Laundering Activities
Venue: arXiv cs.CV 2021
Year: 2021

Signatures present on corporate documents are often used in investigations of relationships between persons of interest, and prior research into the task of offline signature verification has evaluated a wide range of methods on standard signature datasets. However, such tasks often benefit from prior human supervision in the collection, adjustment and labelling of isolated signature images from which all real-world context has been removed. Signatures found in online document repositories such as the United Kingdom Companies House regularly contain high variation in location, size, quality and degrees of obfuscation under stamps. We propose an integrated pipeline of signature extraction and curation, with no human assistance from the obtaining of company documents to the clustering of individual signatures. We use a sequence of heuristic methods, convolutional neural networks, generative adversarial networks and convolutional Siamese networks for signature extraction, filtering, cleaning and embedding respectively. We evaluate both the effectiveness of the pipeline at matching obscured same-author signature pairs and the effectiveness of the entire pipeline against a human baseline for document signature analysis, as well as presenting uses for such a pipeline in the field of real-world anti-money laundering investigation.
A Decision Model for Decentralized Autonomous Organization Platform Selection: Three Industry Case Studies
Venue: arXiv cs.CY 2021
Year: 2021

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.
RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection
Venue: arXiv cs.AI 2021
Year: 2021

Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications. The metaheuristic nature-inspired feature selection approaches are used for single-objective optimization tasks, though the major problem is their frequent premature convergence, leading to weak contribution to data mining. In this paper, we propose a novel feature selection algorithm named Reinforced Swarm Optimization (RSO) leveraging some of the existing problems in feature selection. This algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along with Reinforcement Learning (RL) to maximize the reward of a superior search agent and punish the inferior ones. This hybrid optimization algorithm is more adaptive and robust with a good balance between exploitation and exploration of the search space. The proposed method is evaluated on 25 widely known UCI datasets containing a perfect blend of balanced and imbalanced data. The obtained results are compared with several other popular and recent feature selection algorithms with similar classifier configurations. The experimental outcome shows that our proposed model outperforms BSO in 22 out of 25 instances (88%). Moreover, experimental results also show that RSO performs the best among all the methods compared in this paper in 19 out of 25 cases (76%), establishing the superiority of our proposed method.
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries. Most literature either focuses on context modeling or boundary refinement, which is less generalizable in open-world scenarios. In this work, we advocate a unified framework(UN-EPT) to segment objects by considering both context information and boundary artifacts. We first adapt a sparse sampling strategy to incorporate the transformer-based attention mechanism for efficient context modeling. In addition, a separate spatial branch is introduced to capture image details for boundary refinement. The whole model can be trained in an end-to-end manner. We demonstrate promising performance on three popular benchmarks for semantic segmentation with low memory footprint. Code will be released soon.
Contact detection between an ellipsoid and a combination of quadrics
Venue: arXiv cs.CG 2021
Year: 2021

We analyze the characteristic polynomial associated to an ellipsoid and another quadric in the context of the contact detection problem. We obtain a necessary and sufficient condition for an efficient method to detect contact. This condition is a feature on the size and the shape of the quadrics and can be checked directly from their parameters. Under this hypothesis, contact can be noticed by means of discriminants of the characteristic polynomial. Furthermore, relative positions can be classified through the sign of the coefficients of this polynomial.
Decentralized Basic Income: Creating Wealth with On-Chain Staking and Fixed-Rate Protocols
Venue: arXiv cs.CY 2021
Year: 2021

In this review, we evaluate the mechanisms behind the decentralized finance protocols for generating stable, passive income. Currently, such savings interest rates can be as high as 20% annually, payable in traditional currency values such as US dollars. Therefore, one can benefit from the growth of the cryptocurrency markets, with minimal exposure to their volatility risks. We aim to explain the rationale behind these savings products in simple terms. The key to this puzzle is that asset deposits in cryptocurrency ecosystems are of intrinsic economic value, as they facilitate network consensus mechanisms and automated marketplaces (e.g. for lending). These functions create wealth for the participants, and they provide unique advantages unavailable in traditional financial systems. Our review speaks to the notion of decentralized basic income - analogous to universal basic income but guaranteed by financial products on blockchains instead of public policies. We will go through their implementations of how savings can be channeled into the staking deposits in Proof-of-Stake (PoS) protocols, through fixed-rate lending protocols and staking derivative tokens, thereby exposing savers with minimal risks. We will discuss potential pitfalls, assess how these protocols may behave in market cycles, as well as suggest areas for further research and development.
Fixed-Price Approximations in Bilateral Trade
Venue: arXiv cs.GT 2021
Year: 2021

We consider the bilateral trade problem, in which two agents trade a single indivisible item. It is known that the only dominant-strategy truthful mechanism is the fixed-price mechanism: given commonly known distributions of the buyer's value $B$ and the seller's value $S$, a price $p$ is offered to both agents and trade occurs if $S \leq p \leq B$. The objective is to maximize either expected welfare $\mathbb{E}[S + (B-S) \mathbf{1}_{S \leq p \leq B}]$ or expected gains from trade $\mathbb{E}[(B-S) \mathbf{1}_{S \leq p \leq B}]$. We improve the approximation ratios for several welfare maximization variants of this problem. When the agents' distributions are identical, we show that the optimal approximation ratio for welfare is $\frac{2+\sqrt{2}}{4}$. With just one prior sample from the common distribution, we show that a $3/4$-approximation to welfare is achievable. When agents' distributions are not required to be identical, we show that a previously best-known $(1-1/e)$-approximation can be strictly improved, but $1-1/e$ is optimal if only the seller's distribution is known.
OpenSync: An opensource platform for synchronizing multiple measures in neuroscience experiments
Venue: arXiv cs.LG 2021
Year: 2021

Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measures such as task accuracy and latency. To create a better understanding of human behavior and brain functionality, we should introduce other measures and analyze behavior from various aspects. However, it is technically complex and costly to design and implement the experiments that record multiple measures. To address this issue, a platform that allows synchronizing multiple measures from human behavior is needed. Method: This paper introduces an opensource platform named OpenSync, which can be used to synchronize multiple measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain the structure and details of OpenSync, provide two case studies in PsychoPy and Unity. Comparison with existing tools: Unlike proprietary systems (e.g., iMotions), OpenSync is free and it can be used inside any opensource experiment design software (e.g., PsychoPy, OpenSesame, Unity, etc., https://pypi.org/project/OpenSync/ and https://github.com/moeinrazavi/OpenSync_Unity). Results: Our experimental results show that the OpenSync platform is able to synchronize multiple measures with microsecond resolution.
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Venue: arXiv cs.LG 2021
Year: 2021

Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signal. In simulations and two real MEG and EEG datasets, we demonstrate the validity of the proposed approach and investigate its behavior when the model assumptions are violated. Our results confirm that Riemannian tangent space methods are robust to differences in the source patterns across observations. We found that this robustness property also transfers to the associated patterns.
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.
Feasibility of GNSS-free Localization: A TDoA-based Approach Using LoRaWAN
Venue: arXiv cs.NI 2021
Year: 2021

LoRaWAN has garnered tremendous attention owing to the low power consumption of end nodes, long range, high resistance to multipath, low cost, and use of license-free sub-GHz bands. Consequently, LoRaWAN is gradually replacing Wi-Fi and Bluetooth in sundry IoT applications including utility metering, smart cities, and localization. Localization, in particular, has already witnessed a surge of alternatives to Global Navigation Satellite System (GNSS), based on Wi-Fi, Bluetooth, Ultra Wide Band, 5G, etc. in indoor and low power domains due to the poor indoor coverage and high power consumption of GNSS. With the need for localization only shooting up with dense IoT deployments, LoRaWAN is seen as a promising solution in this context. Indeed, many attempts employing various techniques such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Received Signal Strength Index (RSSI) have been made to achieve localization using LoRaWAN. However, a significant drawback in this scenario is the lack of extensive data on path loss and signal propagation modeling, particularly in Indian cityscapes. Another demerit is the use of GNSS at some stage primarily for time synchronization of gateways. In this work, we attempt to nullify these two disadvantages of LoRaWAN based localization. The first part of this work presents experimental data of LoRaWAN transmissions inside a typical city building to study signal propagation and path loss. The latter part proposes a standalone GNSS-free localization approach using LoRaWAN that is achieved by applying a collaborative, TDoA-based methodology. An additional stationary node is introduced into the network to allow the synchronization of gateways without GNSS. Finally, the distribution of localization error in a triangle of gateways and the effect of timing resolution, time-on-air, and duty cycle constraints on it are investigated.
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport
Venue: arXiv cs.CV 2021
Year: 2021

Good quality reconstruction and comprehension of a scene rely on 3D estimation methods. The 3D information was usually obtained from images by stereo-photogrammetry, but deep learning has recently provided us with excellent results for monocular depth estimation. Building up a sufficiently large and rich training dataset to achieve these results requires onerous processing. In this paper, we address the problem of learning outdoor 3D point cloud from monocular data using a sparse ground-truth dataset. We propose Pix2Point, a deep learning-based approach for monocular 3D point cloud prediction, able to deal with complete and challenging outdoor scenes. Our method relies on a 2D-3D hybrid neural network architecture, and a supervised end-to-end minimisation of an optimal transport divergence between point clouds. We show that, when trained on sparse point clouds, our simple promising approach achieves a better coverage of 3D outdoor scenes than efficient monocular depth methods.
Recognizing Emotions evoked by Movies using Multitask Learning
Venue: arXiv cs.CV 2021
Year: 2021

Understanding the emotional impact of movies has become important for affective movie analysis, ranking, and indexing. Methods for recognizing evoked emotions are usually trained on human annotated data. Concretely, viewers watch video clips and have to manually annotate the emotions they experienced while watching the videos. Then, the common practice is to aggregate the different annotations, by computing average scores or majority voting, and train and test models on these aggregated annotations. With this procedure a single aggregated evoked emotion annotation is obtained per each video. However, emotions experienced while watching a video are subjective: different individuals might experience different emotions. In this paper, we model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach. Concretely, we propose two deep learning architectures: a Single-Task (ST) architecture and a Multi-Task (MT) architecture. Our results show that the MT approach can more accurately model each viewer and the aggregated annotation when compared to methods that are directly trained on the aggregated annotations. Furthermore, our approach outperforms the current state-of-the-art results on the COGNIMUSE benchmark.
Surrogate Modelling for Injection Molding Processes using Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Injection molding is one of the most popular manufacturing methods for the modeling of complex plastic objects. Faster numerical simulation of the technological process would allow for faster and cheaper design cycles of new products. In this work, we propose a baseline for a data processing pipeline that includes the extraction of data from Moldflow simulation projects and the prediction of the fill time and deflection distributions over 3-dimensional surfaces using machine learning models. We propose algorithms for engineering of features, including information of injector gates parameters that will mostly affect the time for plastic to reach the particular point of the form for fill time prediction, and geometrical features for deflection prediction. We propose and evaluate baseline machine learning models for fill time and deflection distribution prediction and provide baseline values of MSE and RMSE metrics. Finally, we measure the execution time of our solution and show that it significantly exceeds the time of simulation with Moldflow software: approximately 17 times and 14 times faster for mean and median total times respectively, comparing the times of all analysis stages for deflection prediction. Our solution has been implemented in a prototype web application that was approved by the management board of Fiat Chrysler Automobiles and Illogic SRL. As one of the promising applications of this surrogate modelling approach, we envision the use of trained models as a fast objective function in the task of optimization of technological parameters of the injection molding process (meaning optimal placement of gates), which could significantly aid engineers in this task, or even automate it.
Residual Tree Aggregation of Layers for Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Although attention-based Neural Machine Translation has achieved remarkable progress in recent layers, it still suffers from issue of making insufficient use of the output of each layer. In transformer, it only uses the top layer of encoder and decoder in the subsequent process, which makes it impossible to take advantage of the useful information in other layers. To address this issue, we propose a residual tree aggregation of layers for Transformer(RTAL), which helps to fuse information across layers. Specifically, we try to fuse the information across layers by constructing a post-order binary tree. In additional to the last node, we add the residual connection to the process of generating child nodes. Our model is based on the Neural Machine Translation model Transformer and we conduct our experiments on WMT14 English-to-German and WMT17 English-to-France translation tasks. Experimental results across language pairs show that the proposed approach outperforms the strong baseline model significantly
Neural Variational Learning for Grounded Language Acquisition
Venue: arXiv cs.CL 2021
Year: 2021

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our experiments demonstrate that this approach is generalizable to multilingual, highly varied datasets.
Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss
Venue: arXiv cs.LG 2021
Year: 2021

We define disentanglement as how far class-different data points from each other are, relative to the distances among class-similar data points. When maximizing disentanglement during representation learning, we obtain a transformed feature representation where the class memberships of the data points are preserved. If the class memberships of the data points are preserved, we would have a feature representation space in which a nearest neighbour classifier or a clustering algorithm would perform well. We take advantage of this method to learn better natural language representation, and employ it on text classification and text clustering tasks. Through disentanglement, we obtain text representations with better-defined clusters and improve text classification performance. Our approach had a test classification accuracy of as high as 90.11% and test clustering accuracy of 88% on the AG News dataset, outperforming our baseline models -- without any other training tricks or regularization.
Who's Afraid of Thomas Bayes?
Venue: arXiv cs.LG 2021
Year: 2021

In many cases, neural networks perform well on test data, but tend to overestimate their confidence on out-of-distribution data. This has led to adoption of Bayesian neural networks, which better capture uncertainty and therefore more accurately reflect the model's confidence. For machine learning security researchers, this raises the natural question of how making a model Bayesian affects the security of the model. In this work, we explore the interplay between Bayesianism and two measures of security: model privacy and adversarial robustness. We demonstrate that Bayesian neural networks are more vulnerable to membership inference attacks in general, but are at least as robust as their non-Bayesian counterparts to adversarial examples.