Miracle-dz/UniFAR-FTUs huggingface dataset:
Doc2Vec-based Approach for Extracting Diverse Evaluation Expressions from Online Review Data

This paper proposes a method for extracting diverse expressions from online movie review texts for a given keyword query. When people watch a movie that makes them cry, they generally do not say “I cried. ” Instead, they use such euphemistic language as “I needed a handkerchief” or “My makeup was running. ” To enable information retrieval based on audience reactions such as “movies that make me cry” using review texts, a variety of paraphrased expressions must be collected for arbitrary queries. Our proposed method extracts such expressions from review datasets by applying two extensions to Doc2Vec: 1) it changes the granularity of the training sentences to mitigate a lack of context, and 2) it applies query expansion for similarity calculation in advance. We conducted a large-scale experiment using crowdsourcing with 1.29 million actual sentences taken from Yahoo! Movies, Japan. The experimental result revealed that changing the training data granularity and adding the query expansion are both effective to accurately collect more diverse expressions that have a meaning similar to the given query.
Towards Proactive Financial Crime and Fraud Detectionthrough Artificial Intelligence and RegTech Technologies

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.
Reserch on PDR Algorithm based on BP Neural Network

Aiming at the problem of PDR indoor location, this paper proposes an improved location method based on BP neural network. In the scene of sensor layout and head, it is difficult to distinguish the head deviation straight walking state and turning state in the PDR positioning method, which leads to positioning errors. Aiming at the problem in this scene, through a lot of analysis, it is found that there is obvious characteristic difference between straight heading and turning in acceleration sensor data. In view of this discovery, this paper constructs BP neural network to train and classify head deviation straight walking and turning, and the final training result is good, which fully meets the experimental requirements, corrects the heading and improves the positioning accuracy.
Intrusion detection model based on multi-category feature fusion

ABSTRACT The intrusion detection model distinguishes normal traffic from abnormal traffic by analyzing network traffic. Recently, convolutional neural networks have been developed in intrusion detection. According to the influences of different network-data-traffic characteristics on network attacks, the work proposed an intrusion detection model based on the fusion of multiple features. This model divided the network data traffic into 4 datasets according to different features for feature extraction separately. After combining the fully connected neural network for feature fusion, the stochastic gradient descent method was used to train the model to obtain the detection results. In the experiment, the KDD Cup 1999(KDD99) dataset was used as the training and test sets. The results showed that compared with traditional machine learning algorithms and DSEBM-e, RF-DAGMM, LSTM-RESNE deep-learning models, the intrusion detection model based on multi-category feature fusion proposed in the work could improve the accuracy and recall rates of the intrusion detection model.
Design, Development, and Evaluation of a Physical Exercise Monitoring and Managing System for Athletes

The worldwide sports industry is booming through the usage of information and communication technologies. The collection, analysis, and presentation of athlete data is common practice for professional individual and team sports to assess individuals (athletes)/teams capability, fatigue, and subsequent adaptation responses; examine potential improvement areas, and minimize the risk of injury. Nutrition and exercise plans are also blended to meet specific training requirements and build strategic programs to maximize athletes’ ability to perform. An effective and efficient system is required for the athletes and their mentors to monitor and manage the athlete’s physical exercise. Therefore, the purpose of this article is to reveal the user requirements for creating an athlete monitoring system and to propose a wearable system based on the revealed requirements. To achieve these objectives, a Design Science Research (DSR) approach was adopted. As such, an empirical study (through semi-structured interviews) was conducted with 41 participants to reveal the system requirements; then a wearable athlete monitoring application was developed considering the revealed requirements. Finally, the proposed system was evaluated with 21 participants through the System Usability Scale (SUS) method. The study found that the proposed system is reliable, user-friendly, and useful for monitoring and managing physical exercise for the athletes and their mentors. The study also showed that the proposed system is useful and usable regardless of the athlete’s age or gender.
Evidence for Teaching Practices that Broaden Participation for Women in Computing

Computing has, for many years, been one of the least demographically diverse STEM fields, particularly in terms of women's participation [12, 36]. The last decade has seen a proliferation of research exploring new teaching techniques and their effect on the retention of students who have historically been excluded from computing. This research suggests interventions and practices that can affect the inclusiveness of the computer science classroom and potentially improve learning outcomes for all students. But research needs to be translated into practice, and practices need to be taken up in real classrooms. The current paper reports on the results of a focused systematic "state-of-the-art" review of recent empirical studies of teaching practices that have some explicit test of the impact on women in computing. Using the NCWIT Engagement Practices Framework as a means of organization, we summarize this research, outline the practices that have the most empirical support, and suggest where additional research is needed.
Identifying Legal Party Members from Legal Opinion Documents using Natural Language Processing

Law and order is a field that can highly benefit from the contribution of Natural Language Processing (NLP) to its betterment. An area in which NLP can be of immense help is, information retrieval from legal documents which function as legal databases. The extraction of legal parties from the aforementioned legal documents can be identified as a task of high importance since it has a significant impact on the proceedings of contemporary legal cases. This study proposes a novel deep learning methodology which can be effectively used to find a solution to the problem of identifying legal party members in legal documents. In addition to that, in this paper, we introduce a novel data set which is annotated with legal party information by an expert in the legal domain. The deep learning model proposed in this study provides a benchmark for the legal party identification task on this data set. Evaluations for the solution presented in the paper show that our system has 90.89% precision and 91.69% recall for an unseen paragraph from a legal document, thus conforming the success of our attempt.
What we Talk about when we Talk about Earth on Earth Day?

April 22, 2021, marked the 51st anniversary of Earth Day. With the growing imperativeness of environmental protection and sustainability, we want to study people’s collective attention and conversations on this themed day. What are the top-of-mind discourses and central topics about the earth? How do people feel about them, hopeful or pessimistic? How do they change over time, especially after the COVID-19 pandemic? To answer these, we extracted and quantified top frequent features, co-occurring hashtags, sentiment words, and latent sub-topics from about 300K tweets posted on the Earth Day of 2009, 2013, 2017, and 2021. The results demonstrated the longitudinal dynamics of people’s rhetoric and focus regarding protecting the earth – from resources conservation to climate changes, as well as the plummeted optimism toward environmental topics after the pandemic. The findings of our paper can help decision-makers to better assess the “voices of the people” and inform evidence-based decision-making.
Research on ROI extraction algorithm for finger vein recognition based on capsule neural network

In the identification system of finger vein, the extraction of the region of interest (ROI) is a very important step. It determines whether the ROI of the vein image of the same finger has a high similarity, and then directly affects the following two steps of feature extraction and recognition and matching. This paper used deep neural network for the ROI extraction of the finger vein, and proposed a ROI extraction algorithm for finger vein based on capsule neural network aiming at the problems of information loss in traditional convolution neural network. By using two kinds of public data sets for the test, it can be seen that the algorithm of this paper has a good effect on ROI extraction of finger vein images.
A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments

Using head mounted displays (HMDs) in conjunction with virtual reality (VR), vision researchers are able to capture more naturalistic vision in an experimentally controlled setting. Namely, eye movements can be accurately tracked as they occur in concert with head movements as subjects navigate virtual environments. A benefit of this approach is that, unlike other mobile eye tracking (ET) set-ups in unconstrained settings, the experimenter has precise control over the location and timing of stimulus presentation, making it easier to compare findings between HMD studies and those that use monitor displays, which account for the bulk of previous work in eye movement research and vision sciences more generally. Here, a visual discrimination paradigm is presented as a proof of concept to demonstrate the applicability of collecting eye and head tracking data from an HMD in VR for vision research. The current work’s contribution is 3-fold: firstly, results demonstrating both the strengths and the weaknesses of recording and classifying eye and head tracking data in VR, secondly, a highly flexible graphical user interface (GUI) used to generate the current experiment, is offered to lower the software development start-up cost of future researchers transitioning to a VR space, and finally, the dataset analyzed here of behavioral, eye and head tracking data synchronized with environmental variables from a task specifically designed to elicit a variety of eye and head movements could be an asset in testing future eye movement classification algorithms.
Red Yeast Improves the Potential Safe Utilization of Solid Waste (Phosphogypsum and Titanogypsum) Through Bioleaching

Phosphogypsum (PG) and titanium gypsum (TG), as a by-product (solid waste) in phosphate fertilizer and titanium dioxide industry, are causing serious environmental hazards. The resource/harmless application of PG and TG is the development trend in the future. The biological function of red yeast (Rho: Rhodotorula mucilaginosa) can effectively reduce the concentration of pollutants in the environment and has the potential of biological flotation/purification of mineral solid waste. In this study, the bioremediation mechanism and safe utilization efficiency of Rho for different contents of PG and TG were explored by using its biological flotation function. The X-ray fluorescence spectrometry (XRF) results showed that F was the main toxic element in PG and TG, and Pb and Cd did not reach the detection limit. The processing capacity of Rho for PG (>10 g/ml) is higher than that of TG (<5 g/ml). After bioleaching by Rho, the proportion of F in PG and TG solid decreased by 61.45–63.79% and 49.45–59.19%, respectively. The results of three-dimensional fluorescence, extracellular polymeric substance (EPS) extraction, X-ray diffraction (XRD), and scanning electron microscopy (SEM) confirmed that Rho could accelerate the release of harmful elements (F) in PG and TG. SEM showed that Rho cells and secretions adhered and wrapped on PG/TG, causing PG/TG decomposition and fragmentation. In addition, the adsorption of EPS and the formation of Ca5(PO4)3F are two main ways for Rho to remove F. Furthermore, under the condition of high concentration bioleaching, Rho can accelerate the release and utilization of P in PG, which is not only for the re-precipitation of Ca5(PO4)3F but also conducive to the reproduction and utilization of microorganisms. Meanwhile, the purification/safe reuse of PG by Rho is easier than that of TG. Therefore, the toxicity of PG and TG bioleaching by Rho can be greatly reduced, suggesting the huge potential of Rho in soil improvement and remediation.
BEAM: technology for autonomous self-analysis

BEAM (Beacon-based Exception Analysis for Multimissions) is an end-to-end method of data analysis intended for real-time fault detection and characterization. It provides a generic system analysis capability for potential application to deep space probes and other highly automated systems. This paper describes in brief the architecture, application, and operating theory of BEAM. BEAM provides a generalized formalism for diagnostics and prognostics in virtually any instrumented system. Consideration is given to all standard forms of data, both time-varying (sensor or extracted feature) quantities and discrete measurements, embedded physical and symbolic models, and communication with other autonomy-enabling components such as planners and schedulers. This approach can be adapted to on-board or ground-based implementations with no change to the basic operating theory. The approach is illustrated with an overview of application types, past validations, and ongoing efforts.
EjRAV1/2 Delay Flowering Through Transcriptional Repression of EjFTs and EjSOC1s in Loquat

Most species in Rosaceae usually need to undergo several years of juvenile phase before the initiation of flowering. After 4–6 years’ juvenile phase, cultivated loquat (Eriobotrya japonica), a species in Rosaceae, enters the reproductive phase, blooms in the autumn and sets fruits during the winter. However, the mechanisms of the transition from a seedling to an adult tree remain obscure in loquat. The regulation networks controlling seasonal flowering are also largely unknown. Here, we report two RELATED TO ABI3 AND VP1 (RAV) homologs controlling juvenility and seasonal flowering in loquat. The expressions of EjRAV1/2 were relatively high during the juvenile or vegetative phase and low at the adult or reproductive phase. Overexpression of the two EjRAVs in Arabidopsis prolonged (about threefold) the juvenile period by repressing the expressions of flowering activator genes. Additionally, the transformed plants produced more lateral branches than the wild type plants. Molecular assays revealed that the nucleus localized EjRAVs could bind to the CAACA motif of the promoters of flower signal integrators, EjFT1/2, to repress their expression levels. These findings suggest that EjRAVs play critical roles in maintaining juvenility and repressing flower initiation in the early life cycle of loquat as well as in regulating seasonal flowering. Results from this study not only shed light on the control and maintenance of the juvenile phase, but also provided potential targets for manipulation of flowering time and accelerated breeding in loquat.
The 100 G capturing robot - too fast to see

This paper discusses the capturing robot with the maximum acceleration of 100 G in design specification. We aim find the combination of the arm with a mass of 0.1 kg and the spring capable of producing the initial compressed force of 100 N, in order to achieve the 100 G. To reduce the total capturing time, we propose an arm/gripper coupling mechanism where the spring energy initially accumulated in the arm is transferred to the kinetic energy of the arm and continuously to the kinetic energy for closing the gripper at the capturing point without any time lag. The experimental results show the maximum acceleration of 91 G and the capturing time of 25 ms were achieved. Experiments on capturing a dropping ball were also executed with the assistance of the 1 ms-vision.
Improving food quality for the organic poultry meat sector : a Quality Function Deployment approach

The paper presents results from an Italian study on the development of the food quality for poultry meat into the organic sector, using the Quality Function Deployment (QFD) technique. Results show that among the visual characteristics of poultry meat consumers assign greater importance to attributes strictly related to the animal welfare issue. Price and product appearance (colour and fat presence) come as second. To meet these needs, producers can effectively operate along the supply chain by acting on: housing type, genotype lines and stocking density (animal concentration/mq). Information about these issues (labelling) should also be provided to the consumers.
Ultra Step-Up DC–DC Converter With Reduced Switch Stress

In this paper, a new single-switch nonisolated dc-dc converter with high voltage transfer gain and reduced semiconductor voltage stress is proposed. The proposed topology utilizes a hybrid switched-capacitor technique for providing a high voltage gain without an extreme switch duty cycle and yet enabling the use of a lower voltage and RDS-ON MOSFET switch so as to reduce cost, switch conduction, and turn-on losses. In addition, the low voltage stress across the diodes allows the use of Schottky rectifiers for alleviating the reverse-recovery current problem, leading to a further reduction in the switching, and conduction losses. The principle of operation and a comparison with other high step-up topologies are presented. Two extensions of the proposed converter are also introduced and discussed. Simulation and experimental results are also presented to demonstrate the effectiveness of the proposed scheme.
A primogenius study on characterisation of c-Si solar cell

The use of solar photovoltaic (PV) as distributed energy resources is increasingly being pursued as a supplement and alternative to conventional grid electricity. It has become an important source of energy because of its high reliability, requirement of very little maintenance and capability of generating power with no noise and emissions. Silicon solar cell as renewable energy resource has been extensively studied in the last couple of years. In view of enormous application and interest of crystalline (c-Si) silicon solar cell for PV power generation across the world, an elementary study has been tried to be carried out in this paper to comprehend the performance of the silicon solar cell. Basic and critical characterisation and analysis have been performed with PC1D modelling. Also the effect of back surface field layer on efficiency is observed with and without rear passivation.
SAPSI-M: Computer Program for Analyzing Asphalt Concrete Pavements under Moving Arbitrary Loads

A new solution for the dynamic analysis of asphalt concrete pavements subjected to moving fluctuating loads has been developed. The method builds on an existing model for stationary loads, SAPSI, which uses the complex response method of transient analysis with a continuum solution in the horizontal direction and a finite-element solution in the vertical direction. The structural model is an n-layered damped-elastic medium. The subgrade can be modeled as either a rigid base or a semi-infinite half space. The loads are surface pressure loads, and the analysis is under axisymmetric conditions. The moving loads are modeled as a series of pulses with a duration equal to the time required for the wheel to pass a fixed point in the pavement. The proposed method of analysis in the new version of the program, SAPSI-M, is an improvement over the existing methods because (a) it treats moving fluctuating loads on an n-layered damped-elastic system; (b) it incorporates such important factors as wave propagation, inertia, and damping effects of the medium, as well as frequency-dependent asphalt concrete properties; and (c) it can handle any load configuration, thus making possible the modeling of multiple wheel configurations of truck axles and airplane landing gears. Theoretical results have shown that the effect of vehicle speed is significant, in part due to the frequency-dependent properties of the asphalt concrete. Comparison with field strain data from full-scale pavement tests has shown excellent agreement.
Cooperative Task Planning of Multi-Robot System

Multiple cooperating robots hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make effective use of specialists designed for a single purpose rather than requiring that a single robot be a generalist. Multi-robot coordination, however, is a complex problem. An empirical study is described in the present paper that sought general guidelines for task allocation strategies. Different task allocation strategies are identified, and demonstrated in the multirobot environment. A simulation study of the methodology is carried out in a simulated grid world. The results show that there is no single strategy that produces best performance in all cases, and that the best task allocation strategy changes as a function of the noise in the system. This result is significant, and shows the need for further investigation of task allocation strategies.
Modeling of wavelength multiplexed lightwave systems

Very-high-capacity optical communication systems have been demonstrated by wavelength-division multiplexing (WDM) of several high-bitrate channels. Large distances have been achieved using optical amplifiers to compensate the fiber loss. The performance of these systems is determined by the interplay of several limiting factors: optical signal-to-noise ratio, fiber chromatic dispersion, and fiber nonlinearities. The transmitted optical power, amplifier spacing, and fiber dispersion map must all be simultaneously optimized. Accurate modeling is essential not only to optimize the design of WDM systems, but also to gain a better understanding of the limiting effects and to devise techniques to ameliorate their impact. Theoretical modeling and split-step simulations are complementary techniques to understand the performance of WDM lightwave systems. Their strengths and weaknesses are discussed, and a synergistic approach to their use is described. Comparisons with experimental results are provided.