Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Dual Consensus ADMM Based on Partition for DC-DOPF With Carbon Emission Trading

This article presents a distributed alternating direction method of multipliers (ADMMs) approach for solving the direct current dynamic optimal power flow with carbon emission trading (dc-DOPF-CET) problem. Generally, the ADMM-based distributed approaches disclose boundary buses and branches information among adjacent subsystems. As opposed to these methods, the proposed method (dc-ADMM-P) adopts a novel strategy which uses consensus ADMM to solve the dual of dc-DOPF-CET while only discloses boundary branches information among adjacent subsystems. Moreover, the convergence performance of dc-ADMM-P is improved by reducing the number of dual multipliers and employing an improved update step of the multiplier. DC-ADMM-P is tested on cases ranging from 6 to 1062 buses, with comparison with other distributed/decentralized methods. The simulation results verify the high efficiency of dc-ADMM-P in solving the dc-DOPF problem with complex (nonlinear) factors which can be formulated as convex separable functions. Meanwhile, it also shows the improvement of convergence performance by reducing the number of dual multipliers and employing a new update strategy for the multiplier.
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a relation graph of trigger-argument structures. Unlike existing structured prediction tasks such as dependency parsing, the task targets to detect DAG structures, which constitute events, from the relation graph. We define actions to construct events and use all the beams in a beam search to detect all event structures that may be overlapping and nested. The search process constructs events in a bottom-up manner while modelling the global properties for nested and overlapping structures simultaneously using neural networks. We show that the model achieves performance comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of any syntactic and hand-engineered features. Further analyses on the development set show that our model is more computationally efficient while yielding higher F1-score performance.
Collaborative Perception From Data Association To Localization

During the last decade, visual sensors have become ubiquitous. One or more cameras can be found in devices ranging from smartphones to unmanned aerial vehicles and autonomous cars. During the same time, we have witnessed the emergence of large scale networks ranging from sensor networks to robotic swarms. Assume multiple visual sensors perceive the same scene from different viewpoints. In order to achieve consistent perception, the problem of correspondences between observed features must be first solved. Then, it is often necessary to perform distributed localization, i.e. to estimate the pose of each agent with respect to a global reference frame. Having everything set in the same coordinate system and everything having the same meaning for all agents, operation of the agents and interpretation of the jointly observed scene become possible. The questions we address in this thesis are the following: first, can a group of visual sensors agree on what they see, in a decentralized fashion? This is the problem of collaborative data association. Then, based on what they see, can the visual sensors agree on where they are, in a decentralized fashion as well? This is the problem of cooperative localization. The contributions of this work are five-fold. We are the first to address the problem of consistent multiway matching in a decentralized setting. Secondly, we propose an efficient decentralized dynamical systems approach for computing any number of smallest eigenvalues and the associated eigenvectors of a weighted graph with global convergence guarantees with direct applications in group synchronization problems, e.g. permutations or rotations synchronization. Thirdly, we propose a state-of-the art framework for decentralized collaborative localization for mobile agents under the presence of unknown cross-correlations by solving a minimax optimization problem to account for the missing information. Fourthly, we are the first to present an approach to the 3-D rotation localization of a camera sensor network from relative This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/3292 bearing measurements. Lastly, we focus on the case of a group of three visual sensors. We propose a novel Riemannian geometric representation of the trifocal tensor which relates projections of points and lines in three overlapping views. The aforementioned representation enables the use of the state-of-the-art optimization methods on Riemannian manifolds and the use of robust averaging techniques for estimating the trifocal tensor. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Konstantinos Daniilidis
Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred

In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size.
Enhancing Denoised Image Via Fusion With a Noisy Image

Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.
An Anonymous Network Website Fingerprinting Attack Method Based on Wavelet Transformation

The anonymous network in protecting user privacy information, but also for the use of criminals, for its illegal acts, and increase the cover monitoring, tracking and obstacle of the government organs forensics, Aiming at the problem of anonymous network, this paper proposes a website fingerprinting attack method for TOR anonymous network. Wavelet transform is introduced to deal with the traffic generated by visiting the website. SVM algorithm is used to learn and generate classifiers, establish fingerprint feature library, and then compare the data generated by the target visiting website with the feature library to confirm its visiting website. In this way, the experimental results show that the average recognition accuracy of target visited websites is 96.7% under all known features of websites, which is superior to literature [17].
A Fraudulent Transactions Simulation Method Based on Genetic Algorithm

When using machine learning or other methods to construct the fraud detection models, the banking industry faces such problems: the number of fraud transactions data is too small, which affect the training of anti-fraud model and the detection effect of fraud transaction. This paper proposed a data simulation algorithm based on genetic algorithm (GA-DS). By studying the feature of real fraudulent transactions, we designed the crossover mutation and other genetic operators, explored the suitable fitness function that can evaluate the quality of simulated data, and generated simulated data satisfying the characteristics of the original transaction. The experiment result shows that mixing the simulated data and the original data into the training can improve the detection ability of anti-fraud model.
Explaining Scene-selective Visual Areas Using Task-specific Deep Neural Network Representations

Deep neural networks (DNNs) are currently the models that account for higher variance of the responses from the human visual cortex. In this work, we aim to explore the power of DNNs as a tool to gain insights into functions of visual brain areas. Particulary, we focus on scene selective visual areas. We use a set of DNNs trained to perform different visual tasks, comprising 2D, 3D and semantic aspects of scene perception, to explain fMRI responses in early visual cortex (EVC) and scene selective visual areas (OPA, PPA). We find that EVC representation is more similar to early layers of all DNNs and deeper layers of 2D-task DNNs. OPA representation is more similar to deeper layers of 3D DNNs, whereas PPA representation to deeper layers of semantic DNNs. We extend our study to performing searchlight analysis using such task specific DNN representations to generate task-specificity maps of visual cortex, and visualize their overlap with existing ROI parcels. Our findings suggest that DNNs trained on a diverse set of visual task can be used to gain insights into functions of visual cortex. Our approach has the potential to be applied beyond visual areas.
Program Transmission Method of PLC Equipments’ Remote Monitoring Software Based on Virtual Serial Port

为了方便PLC设备维护人员在设备出现故障时进行问题排查和程序调试，本文设计了基于虚拟串口的PLC设备远程监控的程序传输方法，实现了程序的高效稳定传输。 本文使用Microsoft.NET的MFC开发平台编程实现，为用户提供友好的操作界面，并充分利用MFC的多线程技术和串口控件，搭建了基于虚拟串口的串口通信和基于TCP/IP协议的网络通讯的传输结构。 本文给出了两种通讯方式下通讯协议的设计和通讯接口的具体实现，并对程序传输进行了测试。 In order to facilitate trouble shooting and program debugging for the PLC equipment maintenance personnel when the equipments malfunction, the program transmission method of PLC equipments’ remote monitoring software based on virtual serial port is designed. And this method realizes the efficient and stable transmission of the program. This software is developed based on the MFC (Microsoft Foundation Classes) platform in Microsoft.NET, which provides users a friendly operation interface. This software makes full use of multi-thread technique and serial communication control, building the transmission structure which is made of serial communication based on virtual serial port and network communication based on the TCP/IP protocol. The design of communication protocols and the concrete realization of communication interface are given with program transmission tested in this paper.
Concepts in Bounded Rationality: Perspectives from Reinforcement Learning

of “Concepts in Bounded Rationality: Perspectives from Reinforcement Learning”, by David Abel, A.M., Brown University, May 2019. In this thesis, I explore the relevance of computational reinforcement learning to the philosophy of rationality and concept formation. I first argue that the framework of reinforcement learning can be used to formalize an intuitive notion of bounded rationality, thereby enabling new analysis of rational agency under realistic assumptions. I then show how the process of concept formation is intimately connected to an agent’s capacity to be rational, subject to relevant resource constraints. Together, these claims suggest that rational agents must adopt a reasonable set of concepts. The main contribution of this work is an initial characterization of what constitutes a good set of concepts for a resource constrained rational agent: namely, the set makes the appropriate trade-off between simplicity (so planning and learning are easy) and representational power (so decisions are effective).
SMARTWALL: A NEW WEB-BASED PLATFORM FOR THE VALORIZATION OF THE
MEDIEVAL WALL OF AVILA

Abstract. As stated the Nara Document on Authenticity and the last Restoration Charter, valorisation of Cultural Heritage sites requires to understand its tangible and intangible values. These values need to be considered as variables that change along the time and space, and they are the result of the interaction between the cultural heritage assets with its history (civilization, societies, etc.). Under this basis, the research project SmartWall attempts to develop a new platform for the valorisation of the Medieval Wall of Ávila, and thus the transmission of its tangible and intangible values to the society. To this end, SmartWall uses an efficient combination between 360 images, 3D/4D models and a geospatial database. This combination provides the following capabilities: i) upgrading the information in real-time, adding, modifying or deleting any type of document; ii) filtering the information in accordance with specific criteria and; iii) managing the information according with different profiles of the user (e.g. expert user, students and tourists). All this information is boosted with a direct link to a 4D model of the Wall, which allows analysing any aspect of the Wall through time. The development and validation of the SmartWall platform was performed in the Alcázar Gate, one of the most emblematic parts of the Medieval Wall of Avila.
Recursive Context-Aware Lexical Simplification

This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-the-art systems in lexical simplification.
Integration of networks and pathways with StarBioTrek package

High-throughput genomic technologies bring to light a comprehensive hallmark of molecular changes of a disease. It is increasingly evident that genes are not isolated from each other and the identification of a gene signature can only partially elucidate the de-regulated biological functions in a disease. The comprehension of how groups of genes (pathways) are related to each other (pathwaycross talk) could explain biological mechanisms causing diseases. Biological pathways are important tools to identify gene interactions and decrease the large number of genes to be studied by partitioning them into smaller groups. Furthermore, recent scientific studies have demonstrated that an integration of pathways and networks, instead of a single component of the pathway or a single network, could lead to a deeper understanding of the pathology. StarBioTrek is an R package for the integration of biological pathways and networks which provides a series of functions to support the user in their analyses. In particular, it implements algorithms to identify pathways cross-talk networks and gene network drivers in pathways. It is available as open source and open development software in the Bioconductor platform.
Vcemway: A One-Stop Solution for Robust Inference with Multi-Way Clustering

Most Stata commands allow cluster(varname) as an option, popularizing the use of standard errors that are robust to one-way clustering. Butwhen it comes to adjusting standard errors for multi-way clustering, there is nosolution that is as widely applicable. While several user-written packages support multi-way clustering, each package is compatible with only a subset of models that Stata’s ever-expanding library of commands allows the researcher to estimate. We introduce a command vcemway that provides a one-stop solution for multi-way clustering. vcemway works with any estimation command that allows cluster(varname) as an option, and adjusts standard errors, individual significance statistics and confidence intervals in output tables for multi-way clustering in specified dimensions. The covariance matrix used in making this adjustment is stored in e(V), meaning that any subsequent call to postestimation commands that use e(V) as input (e.g. test and margins) will also produce results that are robust to multi-way clustering.
Novel Object Scale Differences in Deep Convolutional Neural Networks versus Human Object Recognition Areas

Deep Convolutional Neural Networks (CNNs) are lauded for their high accuracy in object classification, as well as their striking similarity to human brain and behaviour. Both humans and CNNs maintain high classification accuracy despite changes in the scale, rotation, and translation of objects. In this study, we present images of novel objects at different scales and compare representational similarity in the human brain versus CNNs. We measure human fMRI responses in primary visual cortex (V1) and the object selective lateral occipital complex (LOC). We also measure the internal representations of CNNs that have been trained for largescale object recognition. Novel objects lack consensus on their name and identity, and therefore do not clearly belong to any specific object category. These novel objects are individuated in LOC, but not V1. V1 and LOC both significantly represent size and pixel information. In contrast, the late layers of CNNs show they are able to individuate objects but do not retain size information. Thus, while the human brain and CNNs are both able to recognise objects in spite of changes to their size, only the human brain retains this size information throughout the later stages of information processing.
ARCHITECTURAL HERITAGE RECOGNITION IN HISTORICAL FILM FOOTAGE USING NEURAL NETWORKS

Abstract. Researching historical archives for material suitable for photogrammetry is essential for the documentation and 3D reconstruction of Cultural Heritage, especially when this heritage has been lost or transformed over time. This research presents an innovative workflow which combines the photogrammetric procedure with Machine Learning for the processing of historical film footage. A Neural Network is trained to automatically detect frames in which architectural heritage appears. These frames are subsequently processed using photogrammetry and finally the resulting model is assessed for metric quality. This paper proposes best practises in training and validation on a Cultural Heritage asset. The algorithm was tested through a case study of the Tour Saint Jacques in Paris for which an entirely new dataset was created. The findings are encouraging both in terms of saving human effort and of improvement of the photogrammetric survey pipeline. This new tool can help researchers to better manage and organize historical information.
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature

Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences. Towards achieving this grand goal, a crucial first step is to extract past interventions and when and where they have been applied, from hundreds of thousands of reports automatically. In this paper, we developed a corpus annotated with interventions to foster research, and developed an information extraction system for extracting interventions and their location and time from text. We demonstrate early, very encouraging results on extracting interventions.
Real-Time Visual Multi-Target Tracking in Realistic Tracking Environments

Real-Time Visual Multi-Target Tracking in Realistic Tracking Environments Jacob Harley White Department of Electrical and Computer Engineering, BYU Master of Science This thesis focuses on visual multiple-target tracking (MTT) from a UAV. Typical state-ofthe-art multiple-target trackers rely on an object detector as the primary detection source. However, object detectors usually require a GPU to process images in real-time, which may not be feasible to carry on-board a UAV. Additionally, they often do not produce consistent detections for small objects typical of UAV imagery. In our method, we instead detect motion to identify objects of interest in the scene. We detect motion at corners in the image using optical flow. We also track points long-term to continue tracking stopped objects. Since our motion detection algorithm generates multiple detections at each time-step, we use a hybrid probabilistic data association filter combined with a single iteration of expectation maximization to improve tracking accuracy. We also present a motion detection algorithm that accounts for parallax in non-planar UAV imagery. We use the essential matrix to distinguish between true object motion and apparent object motion due to parallax. Instead of calculating the essential matrix directly, which can be time-consuming, we design a new algorithm that optimizes the rotation and translation between frames. This new algorithm requires only 4 ms instead of 47 ms per frame of the video sequence. We demonstrate the performance of these algorithms on video data. These algorithms are shown to improve tracking accuracy, reliability, and speed. All these contributions are capable of running in real-time without a GPU.
REM: From Structural Entropy to Community Structure Deception

This paper focuses on the privacy risks of disclosing the community structure in an online social network. By exploiting the community affiliations of user accounts, an attacker may infer sensitive user attributes. This raises the problem of community structure deception (CSD), which asks for ways to minimally modify the network so that a given community structure maximally hides itself from community detection algorithms. We investigate CSD through an information-theoretic lens. To this end, we propose a community-based structural entropy to express the amount of information revealed by a community structure. This notion allows us to devise residual entropy minimization (REM) as an efficient procedure to solve CSD. Experimental results over 9 real-world networks and 6 community detection algorithms show that REM is very effective in obfuscating the community structure as compared to other benchmark methods.
Detecting Causal Language Use in Science Findings

Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication. Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal”, and “direct causal” categories, achieving an accuracy of 0.90 and a macro-F1 of 0.88. We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed. The prediction result shows that 21.7% studies used direct causal language exclusively in their conclusions, and 32.4% used some direct causal language. We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community. Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.