Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference

There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.
Master Thesis

This master thesis gives an overview of different semantic measures on general knowledge graphs like Wikidata. The focus was on the comparison of short idea texts. For these pairs of ideas, semantic measures were implemented which should represent human intuition as well as possible. To test the quality of the implemented measures, they were compared with a state-of-the-art algorithm from the field of machine learning on the one hand and with the results of a user study on the other hand. The basis of this work is the book ‘Semantic Similarity from Natural Language and Ontology Analysis’. [20], which provides an overview of different semantic measures on Knowledge Graphs. By using this approach to compare short idea texts, the author hopes to show alternatives to commonly used machine learning approaches.
Learning Decentralized Perception Action Communications Loops for Robot Teams

: This paper tackles the problem of learning decentralized policies for 1 a team of homogeneous robots navigating a constrained environment where each 2 robot only has access to information from its onboard sensors. In order to do so, 3 each robot must learn how to perceive the environment around it, communicate 4 correctly with its neighbors and act accordingly. We propose a modular approach 5 we call Graph Memory Policies (GMP) that consists of a perception subsystem 6 and a decentralized collaborative control subsystem. The perception system is 7 trained ofﬂine using data from a single robot and outputs relative way points that 8 a single robot might execute. The decentralized control system takes in as input 9 the waypoints computed by the perception system. There exist 2 two key chal-10 lenges namely that each robot only partially senses the environment and does not 11 have access to a local cost function. To solve these challenges, we look to lever-12 age the local graph structure by employing a new memory-based Graph Neural 13 Network to parameterize robot policies. The memory graph network learns to in-14 teract with a subset of its neighbors and also maintains a notion of past observed 15 states. Through varied experiments and simulations, we show that GMP produces 16 scalable policies that can generalize to different settings and scenarios. 17
S2T: Learning Semantic-Spatial-Temporal Representation for Robust Active Object Tracking

: Active Object Tracking (AOT) aims to track a target object by auto-1 matically controlling the motion of a camera in a 3D scene. It is a challenging 2 task where the pipeline subject to the sub-tasks of target localization, scene under-3 standing and camera motion have to be jointly and interactively tuned. This paper 4 proposes to use both RGB and depth data for AOT. Our method essentially learns a 5 semantic-spatial-temporal (S2T) representation based on the semantic and spatial 6 knowledge with temporal cues derived from the RGB and the depth information, 7 respectively. This is achieved by a speciﬁcally designed S2T module composed of 8 self-enhancement blocks, cross-enhancement blocks, temporal aggregation blocks 9 and S2T fusion block, which not only models the intra/inter-modality relationship 10 of the two types of knowledge, but also fuses them into a uniﬁed temporal-aware 11 representation. Based on the fused representation, we further present an actor-12 critic network optimized with adversarial reinforcement learning to produce cam-13 era motion policies. The proposed AOT pipeline is demonstrated in both virtual 14 and real-world scenes. Code will be made available. 15
Surface Enhanced Raman Spectroscopy of Individual Suspended Aerosol Particles

We report the observation and measurement of surface enhanced Raman spectroscopy (SERS) signatures from individual suspended aerosol particles. To our knowledge, this is the first report of SERS from single suspended particles formed from droplets containing analyte molecules and metallic nanoparticles (MNPs). We describe our experimental setup to generate, charge, and trap microdroplets and an associated spectroscopic measurement arrangement configured to easily switch between trapped, suspended particles and bulk liquid samples of the same materials. Trapped droplets quickly dry to form micron-sized particles mainly composed of inert material, NaCl (>99%) with trace amounts of analyte molecules, and MNPs. As an initial investigation and demonstration of our method, surface enhanced resonance Raman spectroscopy (SERRS) is conducted on 3–5 μm diameter suspended solid particles containing R6G dye molecules and silver nanoparticles (NPs). SERRS signal intensities are linear with particle dye concentrations .
Optochemical properties of gas-phase protonated tetraphenylporphyrin investigated using an optical waveguide NH3 sensor

5,10,15,20-Tetraphenylporphyrin (TPP) was synthesized, and a glass optical waveguide (OWG, which restricts and maintains the light energy in a specific, narrow space and propagates along the space axially) was coated with a gas-phase protonated TPP thin film to develop a sensor for NH3 gas detection. The results show that the TPP thin film agglomerated into H-based J-type aggregates after H2S gas exposure. The molecules in the protonated TPP film OWG sensor acted as NH3 receptors because the gas-phase protonated TPP film morphologically changed from J-type aggregates into free-base monomers when it was deprotonated by NH3 exposure. In this case, H2S gas could be used to increase the relative amount of J-type aggregates in the TPP film and restore the sensor response. The reversible surface morphology of the TPP film was analyzed by 1H NMR spectroscopy, atomic force microscopy, and UV-vis spectroscopy.
Efficient multicomponent synthesis of spiro[indoline-3,4′-thiopyran]-2-ones

ABSTRACT Synthesis of new thiopyran fused spirooxindoles (spiro[indoline-3,4′-thiopyran]-2-ones) were achieved by a multicomponent reaction of N-methyl isatin, malononitrile/ethyl cyanoacetate, and β-oxodithioester using N,N′-dimethylaminopyridine as the catalyst. GRAPHICAL ABSTRACT
Reproducing: Towards Visually Explaining Variational Autoencoders

Reproducing the visual explanation for the basic VAE resulted in figures that are roughly similar to their images. Reproducing the anomaly detection images on the MNIST dataset resulted in images that are intuitively comparable. The anomaly detection on both the UCSDped1 and the MVTec datasets was not on par with the results the authors showed in their paper. The anomaly detection showed overlap between some of the results but not enough to support the claim of the authors in general. When cherry picking the output, similar results to the very few results shown by the author can be achieved.
Direct 3D Printing of Ultralight Graphene Oxide Aerogel Microlattices

Graphene aerogel microlattices (GAMs) hold great prospects for many multifunctional applications due to their low density, high porosity, designed lattice structures, good elasticity, and tunable electrical conductivity. Previous 3D printing approaches to fabricate GAMs require either high content of additives or complex processes, limiting their wide applications. Here, a facile ion‐induced gelation method is demonstrated to directly print GAMs from graphene oxide (GO) based ink. With trace addition of Ca2+ ions as gelators, aqueous GO sol converts to printable gel ink. Self‐standing 3D structures with programmable microlattices are directly printed just in air at room temperature. The rich hierarchical pores and high electrical conductivity of GAMs bring admirable capacitive performance for supercapacitors. The gravimetric capacitance (Cs) of GAMs is 213 F g−1 at 0.5 A g−1 and 183 F g−1 at 100 A g−1, and retains over 90% after 50 000 cycles. The facile, direct 3D printing of neat graphene oxide can promote wide applications of GAMs from energy storage to tissue engineering scaffolds.
Transparent and Flexible Nacre‐Like Hybrid Films of Aminoclays and Carboxylated Cellulose Nanofibrils

Nacre and other biological composites are important inspirations for the design and fabrication of multifunctional composite materials. Transparent, strong, and flexible hybrid films of aminoclays (AC) and carboxylated cellulose nanofibrils (CNF) with a nacre‐like microstructure at AC contents up to 60 wt% are prepared. The high transmittance of visible light is attributed to the high homogeneity of the hybrid films and to the relatively small refractive index contrast between the CNF‐based matrix and synthetic AC. The strength and strain to failure of the hybrids are significantly higher than biogenic nacre and other nacre‐mimicking nanocellulose‐based materials, e.g., montmorillonite‐CNF and graphene oxide‐CNF composite films. The excellent mechanical properties are related to the ionic bonds between the negatively charged carboxylic groups on the CNF and the positively charged amine groups on the AC nanoparticles. This work illustrates the significance of tailoring the interactions between small clay particles and biopolymers in multifunctional materials with potential applications as printable barrier coatings and substrates for optoelectronics.
Does Supply Chain Visibility Enhance Trade Credit? Evidence through Operational Improvements

Scholars and practitioners believe that supply chain visibility (SCV) is critical to effective supply chain management, enabling firms to lower operational costs and enhance efficiency. In this research, we investigate the impact of SCV on firms’ trade credit, a widely neglected yet critical supply chain resource that reduces costs and fuels business growth. Relying on disclosures specified by the 2010 Dodd-Frank Act for supply chain–related conflict minerals, we investigate how a firm’s SCV might lead to higher trade credit received from its suppliers. We adopt a large sample of US firms during 2014–2016, and show that firms with higher SCV actually gain more trade credit from their suppliers. Specifically, customers with higher SCV receive 5.84% more trade credit, which is equivalent to an average of US$39 million. Our evidence indicates that SCV enhances trade credit through improving cost management and operational efficiency, leading to higher creditworthiness of the firms. We also demonstrate that SCV has a more significant effect on trade credit for customers with higher financial constraints and product market uncertainty, indicating that weak and vulnerable firms benefit more from SCV. We further find that SCV leads to less downstream trade credit, indicating that suppliers with higher SCV rely less on trade credit as a mean to attract or capture customers. Overall, our research shows that SCV enhances supply chain finance, which can further be a driver of shareholder value.
Unpaired Depth Super-Resolution in the Wild

Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-world paired data requires specialized setups. Another alternative, generating low-resolution maps from high-resolution maps by subsampling, adding noise and other artificial degradation methods, does not fully capture the characteristics of real-world low-resolution images. As a consequence, supervised learning methods trained on such artificial paired data may not perform well on real-world low-resolution inputs. We consider an approach to depth super-resolution based on learning from unpaired data. While many techniques for unpaired image-to-image translation have been proposed, most fail to deliver effective hole-filling or reconstruct accurate surfaces using depth maps. We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps. We propose a benchmark for unpaired depth SR and demonstrate that our method outperforms existing unpaired methods and performs on par with paired.
Data-driven Smart Ponzi Scheme Detection

A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Experimental results show that this method is significantly better than the existing smart Ponzi scheme detection methods.
Empirical Study of Sustaining the Actualized Value Propositions of Implemented E-Government Projects in Sub-Saharan Africa

Governments in sub-Saharan Africa have implemented e-Government projects. Actualizing the value propositions and sustaining such values are becoming problematic. Some scanty studies on the value propositions of implemented eGovernment projects did not consider actualization of the values. Besides, such studies lack theoretical underpinnings, the identification, and measure of what constitutes actualized values. Neither did they capture what mechanisms could sustain the actualized values nor the contextual conditions enabling its sustainability. Consequently, using a concept-centric systematic review, we identified the value proposition of such implemented projects. By drawing from theories of affordance actualization, realist evaluation (RE) theory, selfdetermination theory, and sustainability framework for e-Government success. We conducted a RE of the implemented e-Government projects in Rwanda using RE as a methodology in three phases. In phase one, we developed the initial program theory (IPT), in phase two, we used contingent valuation as a quantitative approach and realist interview as qualitative method to validate the IPT. Lastly, in the third phase, we synthesized the results of the two investigative case studies to develop the actualized values sustainability framework. Such framework encapsulates, the actualized value propositions, mechanisms and enabling conditions in interactions to sustain the value propositions discovered in the eGovernment investigative contexts.
2020 U.S. Presidential Election: Analysis of Female and Male Users on Twitter

Social media is commonly used by the public during election campaigns to express their opinions regarding different issues. Among various social media channels, Twitter provides an efficient platform for researchers and politicians to explore public opinion regarding a wide range of topics such as economy and foreign policy. Current literature mainly focuses on analyzing the content of tweets without considering the gender of users. This research collects and analyzes a large number of tweets and uses computational, human coding, and statistical analyses to identify topics in more than 300,000 tweets posted during the 2020 U.S. presidential election and to compare female and male users regarding the average weight of the topics. Our findings are based upon a wide range of topics, such as tax, climate change, and the COVID-19 pandemic. Out of the topics, there exists a significant difference between female and male users for more than 70% of topics. Our research approach can inform studies in the areas of informatics, politics, and communication, and it can be used by political campaigns to obtain a gender-based understanding of public opinion.
TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis

Machine Learning (ML) has achieved unprecedented performance in several applications including image, speech, text, and data analysis. Use of ML to understand underlying patterns in gene mutations (genomics) has far-reaching results, not only in overcoming diagnostic pitfalls, but also in designing treatments for life-threatening diseases like cancer. Success and sustainability of ML algorithms depends on the quality and diversity of data collected and used for training. Under-representation of groups (ethnic groups, gender groups, etc.) in such a dataset can lead to inaccurate predictions for certain groups, which can further exacerbate systemic discrimination issues. In this work, we propose TRAPDOOR, a methodology for identification of biased datasets by repurposing a technique that has been mostly proposed for nefarious purposes: Neural network backdoors. We consider a typical collaborative learning setting of the genomics supply chain, where data may come from hospitals, collaborative projects, or research institutes to a central cloud without awareness of bias against a sensitive group. In this context, we develop a methodology to leak potential bias information of the collective data without hampering the genuine performance using ML backdooring catered for genomic applications. Using a real-world cancer dataset, we analyze the dataset with the bias that already existed towards white individuals and also introduced biases in datasets artificially, and our experimental result show that TRAPDOOR can detect the presence of dataset bias with 100% accuracy, and furthermore can also extract the extent of bias by recovering the percentage with a small error.
ExamGAN and Twin-ExamGAN for Exam Script Generation

Nowadays, the learning management system (LMS) has been widely used in different educational stages from primary to tertiary education for student administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. Towards effective learning outcome assessment, the exam script generation problem has attracted many attentions recently. But the research in this field is still in its early stage. Two essential issues have been ignored largely by existing solutions. First, given a course, it is unknown yet how to generate an quality exam script which concurrently has (i) the proper difficulty level, (ii) the coverage of essential knowledge points, (iii) the capability to distinguish academic performances between students, and (iv) the student scores in normal distribution. Second, while frequently encountered in practice, it is unknown so far how to generate a pair of high quality exam scripts which are equivalent in assessment (i.e., the student scores are comparable by taking either of them) but have significantly different sets of questions. To fill the gap, this paper proposes ExamGAN (Exam Script Generative Adversarial Network) to generate high quality exam scripts, and then extends ExamGAN to T-ExamGAN (Twin-ExamGAN) to generate a pair of high quality exam scripts. Based on extensive experiments on three benchmark datasets, it has verified the superiority of proposed solutions in various aspects against the state-of-the-art. Moreover, we have conducted a case study which demonstrated the effectiveness of proposed solution in the real teaching scenarios.
Flow-Assisted Synthesis of Bicyclic Aziridines via Photochemical Transformation of Pyridinium Salts

Herein is described the development and comparison of homemade continuous-flow reactors, a fluorinated ethylene propylene tube (FEP) reactor (internal diameter of 0.4 cm) and two parallel quartz reactors containing two different internal diameters (0.4 and 0.2 cm), and their application to the photochemical transformation of 1-n-butyl- and 1-allylpyridinium bromide salts (1a and 1b, respectively) to the corresponding α-hydroxycyclopentenoaziridines (6-azabicyclo[3.1.0]hex-3-en-2-ols) 2a and 2b. Under recirculating conditions, the FEP reactor allowed the production of a greater quantity of bicyclic aziridine (3.2 g), while the quartz reactor with the same diameter furnished the best productivity results (3.7 g L–1 h–1). In addition, the FEP reactor operated efficiently under circulating conditions (1.75 L of 1a) for 4.5 days (residence time of 17.9 h), allowing the production of 2a at a rate of 2 g/day (62–93% conversion).