Miracle-dz/UniFAR-FTUs huggingface dataset:
Tomographic three-dimensional position measurement of particles using a light field camera

The tracing particle plays a key role in measuring velocity of flow field. The accuracy of tracing particle’s spatial localization determines the accuracy of flow field velocity measurement. The traditional multi-camera technology for photographing tracing particles is costly and time consuming. A light field camera is used to collect particle images, and improves tomographic inversion algorithm to obtain the three-dimensional spatial position of particles faster and more accurately. Based on imaging principle of the light field camera system, a forward tracking model of light emitted by particles is established to realize the imaging simulation of particles. On the basis of this model, a tomographic inversion model is constructed to reversely trace the ray and realize the mapping between non-zero pixels and spatial voxels. A descending dimension method is proposed to calculate the weighting matrix. Multiplicative algebraic reconstruction technology is improved by combined with the similar triangle principle. The Gaussian Blob model is used to determine center position of the voxel with largest intensity value to obtain the three-dimensional position of particles. Experiments indicate that the improved MART algorithm which is used to perform three-dimensional inversion of particles can significantly increase calculation efficiency under the same accuracy level. It is more suitable for applications requiring high calculation speed. The x-axis coordinate error is ±0.06mm. The y-axis coordinate error is ±0.07mm. The z-axis coordinate error is ±0.58mm.
The impact of self-advocacy organizations on the subjective well-being of people with intellectual disabilities: A systematic review of the literature.

BACKGROUND A high sense of subjective well-being has been associated with more prosocial behaviours, better health, work productivity and positive relationships. The aim of this systematic review was to explore what impact self-advocacy has on the subjective well-being of people with intellectual disabilities. METHOD The authors reviewed articles focusing on the perspectives of adults with intellectual disabilities engaged with self-advocacy groups. Searches were performed in PsychINFO, Web of Science, SCOPUS, MEDLINE and CIHNL databases, resulting in 16 articles meeting the inclusion criteria. A framework synthesis approach was used to extract data deductively based on the Dynamic Model of Wellbeing. RESULTS While self-advocacy has a positive impact on all domains of the Dynamic Model of Wellbeing, negative impacts associated with participation in a self-advocacy group were also reported. CONCLUSIONS The benefits of participating in self-advocacy groups on the well-being of individuals with intellectual disabilities outweigh the disadvantages.
StructSim: Querying Structural Node Similarity at Billion Scale

Structural node similarity is widely used in analyzing complex networks. As one of the structural node similarity metrics, role similarity has the good merit of indicating automorphism (isomorphism). Existing algorithms to compute role similarity (e.g., RoleSim and NED) suffer from severe performance bottlenecks, and thus cannot handle large real-world graphs. In this paper, we propose a new framework StructSim to compute nodes’ role similarity. Under this framework, we prove that StructSim is guaranteed to be an admissible role similarity metric based on the maximum matching. While maximum matching is too costly to scale, we then devise the BinCount matching to speed up the computation. BinCount-based StructSim admits a precomputed index to query one single pair in O(k log D) time, where k is a small user-defined parameter and D is the maximum node degree. Extensive empirical studies show that StructSim is significantly faster than existing works for computing structural node similarities on the real-world graphs, with comparable effectiveness.
A Bi-Objective Programming Model for Multimodal Transportation Routing Problem of Bulk Cargo Transportation

In this paper, we propose a bi-objective programming model to select the optimal route for moving a consignment of goods between the anchorage area at the Gulf of Thailand to factories throughout Thailand using a multimodal transportation network and joint consideration of transportation mode selection. The bi-objective programming model is constructed with features based on two objective functions - minimizing transportation costs and minimizing CO2 emissions, subject to transportation jobs that should be completed before their respective due dates. The solutions are obtained by the ε-constraint method, after which a Pareto frontier will be plotted. The decision makers can make a decision tradeoff between the two objectives from that plot.
Real-Time Cross Online Matching in Spatial Crowdsourcing

With the development of mobile communication techniques, spatial crowdsourcing has become popular recently. A typical topic of spatial crowdsourcing is task assignment, which assigns crowd workers to users’ requests in real time and maximizes the total revenue. However, it is common that the available crowd workers over a platform are too far away to serve the requests, so some user requests may be rejected or responded at high money cost after long waiting. Fortunately, the neighbors of a platform usually have available resources for the same services. Collaboratively conducting the task allocation among different platforms can greatly improve the quality of services, but have not been investigated yet. In this paper, we propose a Cross Online Matching (COM), which enables a platform to "borrow" unoccupied crowd workers from other platforms for completing the user requests. We propose two algorithms, deterministic cross online matching (DemCOM) and randomized cross online matching (RamCom) for COM. DemCOM focuses on the largest obtained revenue in a greedy manner, while RamCom considers the trade-off between the obtained revenue and the probability of request being accepted by the borrowed workers. Extensive experimental results verify the effectiveness and efficiency of our algorithms.
Interactive Cleaning for Progressive Visualization through Composite Questions

In this paper, we study the problem of interactive cleaning for progressive visualization (ICPV): Given a bad visualization V , it is to obtain a "cleaned" visualization V whose distance is far from V , under a given (small) budget w.r. t. human cost. In ICPV, a system interacts with a user iteratively. During each iteration, it asks the user a data cleaning question such as "how to clean detected errors x? ", and takes value updates from the user to clean V . Conventional wisdom typically picks a single question (e.g., "Are SIGMOD conference and SIGMOD the same? ") with the maximum expected benefit in each iteration. We propose to use a composite question – i.e., a group of single questions to be treated as one question – in each iteration (for example, Are SIGMOD conference in t1 and SIGMOD in t2 the same value, and are t1 and t2 duplicates? ). A composite question is presented to the user as a small connected graph through a novel GUI that the user can directly operate on. We propose algorithms to select the best composite question in each iteration. Experiments on real-world datasets verify that composite questions are more effective than asking single questions in isolation w.r. t. the human cost.
DAG: A General Model for Privacy-Preserving Data Mining : (Extended Abstract)

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. SMC has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc – they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, −, ×, /, and power). Our model is general – its operators, if pipelined together, can implement various functions, even complicated ones. The experimental results also show that our DAG model can run in acceptable time.
HowSim: A General and Effective Similarity Measure on Heterogeneous Information Networks

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. Measuring the similarity among objects is an important task in data mining applications. Currently, several similarity measures are defined for HIN. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r. t. the quality of the similarity scores. This makes the existing similarity measures difficult to use in real applications. To address this problem, we extend SimRank, a well-known similarity measure for homogeneous graphs, to HINs, by introducing the concept of decay graph. The newly proposed relevance measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, are demonstrated by extensive experiments.
Boron Neutron Capture Therapy and Photodynamic Therapy for High-Grade Meningiomas

Meningiomas are the most common type of intracranial brain tumors in adults. The majority of meningiomas are benign with a low risk of recurrence after resection. However, meningiomas defined as grades II or III, according to the 2016 World Health Organization (WHO) classification, termed high-grade meningiomas, frequently recur, even after gross total resection with or without adjuvant radiotherapy. Boron neutron capture therapy (BNCT) and photodynamic therapy (PDT) are novel treatment modalities for malignant brain tumors, represented by glioblastomas. Although BNCT is based on a nuclear reaction and PDT uses a photochemical reaction, both of these therapies result in cellular damage to only the tumor cells. The aim of this literature review is to investigate the possibility and efficacy of BNCT and PDT as novel treatment modalities for high-grade meningiomas. The present review was conducted by searching PubMed and Scopus databases. The search was conducted in December 2019. Early clinical studies of BNCT have demonstrated activity for high-grade meningiomas, and a phase II clinical trial is in progress in Japan. As for PDT, studies have investigated the effect of PDT in malignant meningioma cell lines to establish PDT as a treatment for malignant meningiomas. Further laboratory research combined with proper controlled trials investigating the effects of these therapies is warranted.
Artificial Landmark-based Indoor Navigation System for an Autonomous Unmanned Aerial Vehicle

An artificial landmark-based indoor navigation system was developed to provide reliable and high accuracy indoor navigation service to an autonomous Unmanned Aerial Vehicle (UAV) with a low computational cost. In this approach, the position of the UAV with respect to the indoor environment was determined by the position information extracted from the artificial landmarks. The artificial landmarks were constructed based on QR codes which stored a unique text string that refer to an absolute coordinate of the indoor environment. Machine learning approaches were applied to provide the system with the ability to detect the landmark through the vision streamed by the aircraft. The system has been experimentally evaluated for proof of concept under practical conditions. The experiment results have proved that using QR code-based artificial landmark as a low computational cost solution is possible to achieve reliable and high accuracy indoor navigation service for an autonomous UAV.
Research on the Cause of Building Fire Using Social Network Analysis

Building fire is one of the major threats to human beings now. This paper presents a quantitative method based on social network analysis (SNA) for evaluating the cause and risk index of building fire. Firstly, available data from 67 case reports of major building fire accidents is extracted. In order to analyze the patterns and characteristics of major road building fire accidents, a relationship network map is constructed by using SNA. By establishing a building fire risk index system, this paper also lists common fire risks and causes. The result results can further deepen the understanding of the causes of construction fire accidents and the interaction between them. Through SNA, this paper finds the key causes of building fire accidents, and which combinations of causes are most likely to cause accidents. The establishment of risk indicator system can provide reference for fire prevention and early warning work.
Development of character strengths across the deployment cycle among U.S. Army soldiers.

OBJECTIVE Despite a narrative of post-traumatic growth and resilience, research reliably demonstrating positive character development following adversity has proved elusive. In the current study, we examined changes in character strengths in Army soldiers deploying for the first time. METHOD The sample was comprised of 212,386 Army soldiers (Mage = 26.5 years old, SD = 7.13; 70.8% White) who were deploying for the first time. Character strengths were assessed once before and up to three times following soldiers' return from deployment. RESULTS We found evidence for two classes of change-a resilient class ("stable high") and a declining class ("persistent low"). Most soldiers were resilient-they had high levels of character strengths prior to deployment and changed very little across the deployment cycle. Approximately 40% of soldiers started with lower character and experienced initial declines post-deployment, from which they experienced no more than small gains over time. CONCLUSIONS Character strengths were highly stable across the deployment transition but some soldiers experienced initial declines from which they never fully rebounded. The findings are discussed in the context of the mechanisms that drive character development.
Personal Loan Fraud Detection Based on Hybrid Supervised and Unsupervised Learning

In recent years, we have been witnessing a dramatic increase on the personal loan for consumption, due to the rapid development of e-services, including e-commerce, e-finance and mobile payments. Resulting from the lack of effective grid verification and supervision, it inevitably leads to largescale losses caused by credit loan fraud [1]. Considering the difficulty of manual inspection and verification on the large amount of credit card transactions, machine learning methods are commonly used to detect fraudulent transactions automatically. This article has applied the Extreme Gradient Boosting(XGBoost) model for data mining and analysis, which is inspired by its brilliant reputation in various data mining contests. With people’s growing concern about privacy protection, how can we apply data mining techniques while taking consideration into privacy terms is one problem. Additionally, according to current loan fraud detection studies, some features are considered to contain little information or a bit of redundancy, whereas others hold the critical information which makes things harder when feature engineering. In order to filter useless information and preserve the useful information without knowing the meaning of our data, this paper combines Kernel Principal Component Analysis (Kernel PCA) together with XGBoost algorithm and proposes a new hybrid unsupervised and supervised learning model, KPXGBoost. We use grid search to avoid over-fitting and compare the performance of both XGBoost and P-XGBoost and other classical machine learning methods. It turns out that P-XGBoost outperforms XGBoost in fraud detection, which provides a new perspective to detecting the fraud behaviour while protecting clients’ privacy.
Keys as Features for Graph Entity Matching

Keys for graphs aim to uniquely identify entities represented by vertices in a graph, using the combination of topological constraints and value equality constraints. This paper proposes graph matching keys, referred to as GMKs, an extension of graph keys with similarity predicates on values, supporting approximation entity matching. We treat entity matching as a classification problem, and propose GMKSLEM, a supervised learning method for graph entity matching. In GMKSLEM, a feature extraction method is provided to discover candidate GMKs (CGMKs) to construct features for vector representation, and then high-quality features and representations are generated by feature selection. Moreover, GMKSLEM provides support to explain the classification results. Using real-life data, we experimentally verify the effectiveness of GMKSLEM, as well as its interpretability.
Detecting Group Beliefs Related to 2018's Brazilian Elections in Tweets: A Combined Study on Modeling Topics and Sentiment Analysis

2018's Brazilian presidential elections highlighted the influence of alternative media and social networks, such as Twitter. In this work, we perform an analysis covering politically motivated discourses related to the second round in Brazilian elections. In order to verify whether similar discourses reinforce group engagement to personal beliefs, we collected a set of tweets related to political hashtags at that moment. To this end, we have used a combination of topic modeling approach with opinion mining techniques to analyze the motivated political discourses. Using SentiLex-PT, a Portuguese sentiment lexicon, we extracted from the dataset the top 5 most frequent group of words related to opinions. Applying a bag-of-words model, the cosine similarity calculation was performed between each opinion and the observed groups. This study allowed us to observe an exacerbated use of passionate discourses in the digital political scenario as a form of appreciation and engagement to the groups which convey similar beliefs.
Improving Nutritive Value of Purple Field Corn Residue and Rice Straw by Culturing with White-Rot Fungi

It was hypothesized that white-rot fungus fermented with rice straw and purple field corn improves nutrient utilization via enhanced digestibility and lowers methane (CH4) production due to the effects of the lovastatin compound. The aim of the current experiment was to investigate the effect of inoculation of two fungi belonging to white-rot fungus type on feed value and ruminal fermentation characteristic. The experiment was carried out according to a completely randomized 3 × 3 factorial design: three roughage sources (rice straw, purple corn stover, and purple corn field cob) for three inoculation methods (untreated, P. ostreatus treated, and V. volvacea treated). The two fungi increased concentration of lovastatin when compared to the untreated, and P. ostreatus had higher lovastatin production potential than V. volvacea (p < 0.05). The yield of lovastatin was obtained from rice straw fermentation with P. ostreatus. The monomeric anthocyanin content (MAC) in untreated purple field corn cobs was higher than in the fermentation groups. Ruminal fermentation gas production from soluble fractions ranged from −2.47 to 1.14 and differed among the treatments (p < 0.01). In comparison to all treatments, the gas production rate for the insoluble fraction was significantly highest (p < 0.01) in treatment alone, in which purple field corn stover was fermented with P. ostreatus and V. volvacea. There was significant interaction in in vitro dry matter digestibility at 12 h of incubation. Purple field corn cob had a higher significant effect on in vitro DM digestibility at 12 and 24 h after incubation when compared to that of other groups. Moreover, current research has found that roughage fermented with P. ostreatus and V. volvacea increased in vitro DM digestibility at 24 h after incubation. Fermenting roughage with fungi did not affect rumen pH, which ranged from 6.60 to 6.91 (p > 0.05), while P. ostreatus resulted in increased levels of ruminal ammonia-nitrogen concentrations. Propionic acid increased in all roughages fermented with P. ostreatus or V. volvacea after 8 h of ruminal fermentation testing. The two fungi fermented as substrate treatments had significantly lower (p < 0.05) CH4 production. Based on the improved rumen DM digestibility and reduced CH4 production, P. ostreatus and V. volvacea could be utilized for enhancing feeding efficiency of roughage.
On Sharding Open Blockchains with Smart Contracts

Current blockchain systems suffer from a number of inherent drawbacks in its scalability, latency, and processing throughput. By enabling parallel confirmations of transactions, sharding has been proposed to mitigate these drawbacks, which usually requires frequent communication among miners through a separate consensus protocol.In this paper, we propose, analyze, and implement a new distributed and dynamic sharding system to substantially improve the throughput of blockchain systems based on smart contracts, while requiring minimum cross-shard communication. Our key observation is that transactions sent by users who only participate in a single smart contract can be validated and confirmed independently without causing double spending. Therefore, the natural formation of a shard is to surround one smart contract to start with. The complication lies in the different sizes of shards being formed, in which a small shard with few transactions tends to generate a large number of empty blocks resulting in a waste of mining power, while a large shard adversely affects parallel confirmations. To overcome this problem, we propose an inter-shard merging algorithm with incentives to encourage small shards to merge with one another and form a larger shard, an intra-shard transaction selection mechanism to encourage miners to select different subsets of transactions for validation, as well as a parameter unification method to further improve these two algorithms to reduce the communication cost and improve system reliability.We analyze our proposed algorithms using the game theoretic approach, and prove that they converge to a Nash Equilibrium. We also present a security analysis on our sharding design, and prove that it resists adversaries who occupy at most 33% of the computation power. We have implemented our designs on go-Ethereum 1.8. 0 and evaluated their performance using both real-world blockchain transactions and large-scale simulations. Our results show that throughput has been improved by 7.2×, and the number of empty blocks has been reduced by 90%.