Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on recognizing predicate of Chinese sentences

Automatically recognizing predicate is one of the important research topics in shallow parsing.Based on the theory of “predicate is the head of Chinese sentence”,predicate’s context of Chinese sentences has been analyzed in detail,and the main factors that affects the presence of predicate have been discussed.Then,A probability model for recognizing predicate of Chinese sentences has been presented.The probabilities of quasi-predicates are estimated by maximum likelihood estimation.Discounted model is used to smooth parameters.Proposed method is tested with a small corpora,and the average accuracy of predicate recognition reaches 80.6% for adjective predicate and 83.2% for verb predicate,respectively.The experimental results show that this method is feasible and effective.
Research and Implementation of ID3 Based on Distributed Database System

A new decision tree algorithm DDTA(Distributed Decision Tree Algorithm) based on distributed data repositories is presented in this paper.The algorithm divides each distributed and isomorphic data set uses with the idea of informational entropy increase.Each distributional site divides its own data repository with the dividing properties transmitted by the server,and the server obtains the dividing properties by calculating the informational entropy increase of various properties with information transmitted from all the distributed sites.The experiment shows that DDTA algorithm is effective in excavating distributionally isomorphic data repository with a decision tree,and that the decision tree generated in the environment of distributional multi-data repositories is correct.Compared with the algorithm INDUS,the algorithm has less cost in communication.
Fault detection through multi-fractal nature of traffic

Significant progress has been developed previously in modeling network traffic with fractals. These developments have given rise to a new insight and physical understanding of the effects of scaling properties in measured network traffic. Among them, multi-fractal models fit measured data more naturally. This paper takes advantage of multi-fractal model to detect fault in network traffic. Faults in a self-similar traffic destroy the singularity structure at the time points they occur, resulting in a significant deviation from those of normal traffic. For fault detection, we measure the degree of deviation of singularity exponent at every time segment through a deviation indicator Q based on structure function S/sub j/(q). Since faults usually bring out abnormal bursts against natural ones in traffic, the proposed algorithm is thereby able to detect them. As demonstrated on simulated and real network traffic data, this algorithm can detect abnormal traffic loads with natural traffic bursts in the background.
Incorporation of Shape Memory Polymers in Interactive Design

This paper seeks to explore the question of how to incorporate smart materials into a design to aesthetically demonstrate science and engineering concepts in an interactive way. This work introduces the development of interactive artworks using a shape memory polymer (SMP) material that changes shape based on an external thermal stimulus. In this paper we explore how interdisciplinary work between engineering and science is needed to create an artwork that mimics natural phenomena around us. This paper discusses fabrication and electronic implementation challenges associated with utilizing the shape memory properties of the material. Specifically, the paper explores some ways to obtain different geometric shapes of the SMP and to utilize different sources of thermal stimulus to create a shape memory effect (SME) in artworks.
ELECTRONICĂ SOFT ESTIMATES FOR DOUBLY ITERATIVE DECODING FOR 16 – QAM AND 64 – QAM MODULATION

In order to increase the spectral efficiency, a bit interleaved coded modulation can be combined with a high order modulation scheme like Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM), improving the communication system’s performance. In this paper, we derive and explain the soft estimates for a doubly iterative decoder using space-time turbo codes and a large number of transmit and receive antennas for 16–QAM and 64–QAM modulation used in a mobile communication system.
A NOTE ON INCOME AND POVERTY OF PERSONS WITH DISABILITIES IN METRO MANILA

Main conclusions are the followings: (1) the incidence of poverty is higher among sample PWDs than that of total population in Metro Manila; (2) the depth of poverty is greater among sample PWDs, too; (3) female PWDs are less privileged than male PWDs with the same level of education, age, marital status and sort of disability; and (4) large variations among PWDs in income and level of education. In other words, while there are some PWDs who made a great achievement in business and life, a substantial number of PWDs have very low monetary income with low level of education.
Detecting and Characterizing Lateral Phishing at Scale

Author(s): Ho, G; Cidon, A; Gavish, L; Schweighauser, M; Paxson, V; Savage, S; Voelker, GM; Wagner, D | Abstract: © 2019 by The USENIX Association. All rights reserved. We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefit-ting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks.
Prevalence of G 2 P [ 4 ] and G 12 P [ 6 ] Rotavirus , Bangladesh

Approximately 20,000 stool specimens from patients with diarrhea visiting 1 urban and 1 rural hospital in Bangladesh during January 2001–May 2006 were tested for group A rotavirus antigen, and 4,712 (24.0%) were positive. G and P genotyping was performed on a subset of 10% of the positive samples (n = 471). During the 2001–2005 rotavirus seasons, G1P[8] (36.4%) and G9P[8] (27.7%) were the dominant strains, but G2[4] and G12P[6] were present in 15.4% and 3.1% of the rotavirus-positive patients, respectively. During the 2005–06 rotavirus season, G2P[4] (43.2%) appeared as the most prevalent strain, and G12P[6] became a more prevalent strain (11.1%) during this season. Because recently licensed rotavirus vaccines include only the P[8] specifi city, it is unknown how the vaccines will perform in settings where non-P[8] types are prevalent.
Influences of Body Composition, Multimorbidity and Polypharmacy on Physical Mobility of Older Adults

Background and Purpose: The prevalence of falls, and the resulting injuries and complications reflect a key concern in the older adult population. An increased risk of falls is highly correlated with decreases in physical mobility and related functions. The Timed Up-and-Go test (TUG) has an established reliability and validity in assessing physical function and the risk of falling in the older adult population. With age, a person typically has an increased number of health morbidities, which may be reflected in changing body composition and the number of medications associated with diseases. However, the influence of these factors on physical mobility in the older adult population has not been quantified. The purpose of this study was to investigate how common indicators of health, including body mass index (BMI), presence of multimorbidity and polypharmacy influence the TUG performance in the older adult population. Methods: A total of 222 individuals ≥ 65 years of age were recruited (68 men and 154 women, mean age = 75.1 yrs). Additional subjects (28 men and 54 women, mean age = 72.7 yrs) from previous research were included in the analysis of BMI on TUG performance with a total of 304 subjects (96 men and 208 women, mean age= 74.4 yrs). Height and weight were obtained in conjunction with a medical history survey. Subjects completed 3 trials of the TUG test at 3 and 9 m walking distances. Comparison was made between 3 participant groups according to their BMI (underweight: BMI 30 kg/m2). Multimorbidity was categorized into 2 groups (multimorbidity: ≥ 2 morbidities, non-multimorbidity: 0-1 morbidity). Polypharmacy was categorized into 2 groups (polypharmacy: ≥ 5 medications, non-polypharmacy: 0-4 medications). Non-parametric tests were run for all 3 variables (BMI, multimorbidity, and polypharmacy). Results: The underweight BMI group exhibited a trend of slower performance than normal weight BMI group for the 3 m (underweight = 8.8±5.3s, normal weight = 8.5±3.3s, p = 0.055) and the 9 m TUG distances (underweight = 17.3±8.9s, normal weight = 17.0±6.1s, p = 0.071). There was a trend toward the overweight BMI group having slower performance than the normal weight BMI group on the 9 m distance (normal weight = 17.0±6.1s, overweight = 18.0±5.5s, p = 0.069). The group with ≥ 2 morbidities had slower performance on the 3 m (multimorbidity = 9.2±3.9s, non-multimorbidity = 7.7±2.1s, p < 0.001) and 9 m TUG distances (multimorbidity = 18.3±6.8s, non-multimorbidity = 15.6±4.0s, p < 0.001). The polypharmacy group had slower performance on the 3 m (polypharmacy = 9.6±3.3s, non-polypharmacy = 8.3±3.4s, p = 0.001) and 9 m TUG distances (polypharmacy = 19.1 ± 5.9s, non-polypharmacy = 16.7±6.0s, p = 0.001). Discussion: Being underweight and overweight were shown to impact a person’s walking ability. We expected overweight individuals to have a slower performance on the TUG but underweight individuals did as well. This could be due to frail older adults having decreased…
Multimodal Techniques for the Study of A ect in Political Videos

An ever-increasing number of political events are being recorded and archived in video format, including real-time footage of parliamentary proceedings, candidate debates, media interviews and press conferences. While textual transcripts are oen used in analysis, political scientists have recently considered methods for modeling audio and video signals directly. is paper looks at what can be achieved using machine learning models trained on each type of input—textual transcripts, audio, and video signals—for the automated recognition of emotion in political speeches. We draw on a newly-collected dataset containing 3,000 video recordings of sentence-long clips from speeches made by Canadian and American politicians, annotated for two primary dimensions of emotion and for anxiety, an emotion of substantive relevance to political science. We present three sets of ndings. First, in line with previous work, we validate that methods using audio and visual signals improve upon the detection of emotion in political speeches when compared to methods that use only textual data. Second, we introduce a new approach to modeling audio signals that accounts for the unique characteristics of each politician’s speaking style. Finally, we propose a simple procedure to preprocess video data and target the politicians of interest, using tools from the eld of computer vision. Results from deep convolutional networks using images extracted with this procedure appear particularly promising for future research on emotion recognition. [Preliminary version prepared for the 2019 PolMeth Conference, MIT, Cambridge, MA, July 18-20, 2019. ] †Assistant Professor, Department of Political Science and Munk School of Global Aairs and Public Policy, University of Toronto. Email: ludovic.rheault@utoronto.ca ‡PhD Candidate, Department of Political Science, University of Toronto. Email: sophie.borwein@mail.utoronto.ca
Grand digital piano : multimodal transfer of learning of sound and touch

Musical instruments have evolved during thousands of years allowing performers to produce almost all imaginable musical sounds of various timbre, pitch, loudness and duration. Speaking about keyboard instruments, the current grand piano mechanism is a result of series of inventions and improvements and is a very complex mechanical system. During past decades an new area of research about creating digital instruments that have same feel and sound as original acoustic instruments have gained popularity [1]. There was a partial success in developing a digital piano with realistic sound and touch: most devices recreate basic properties of sound, however it can be noticed that the models are far from perfection due to the following reasons. Most digital pianos use sample-based synthesis, meaning that for every piano key a finite number of ”sample tones” of a real acoustic instrument are recorded, and then the resulting sound when a key is pressed is just one of the samples or a combination of the samples. As a result, an infinite variety of timbres of a real instrument is reduced to just a few, corresponding to number of samples per key that are prerecorded. There are also pianos that use mathematical models of the acoustic instrument to produce sounds without use of ”sample tones” [2]. Theoretically this allows to recreate an infinite variety of sounds of the original instrument. However, the models require a lot of assumptions about internal physics of piano action mechanism and about the sound production process and use hand-designed parts, which are not guaranteed to be equivalent to the mechanics of a real instrument. We are going to improve the methods described above, using machine learning to train a physically-based model of piano, which will allow to produce new, previously unheard sounds during the training, depending on how performer plays the instrument.
Characterization of GEMINI, a 16-channels programmable readout interface for Triple-GEM detectors in 180nm CMOS

The recent advances in GEM detector development has led to the requirement of a custom readout to fully exploit the advantages of this technology. GEM detectors can be realized with various shapes, also irregular, and high number of channels. GEMINI has been specifically designed to work with Triple-GEM detectors and it integrates 16 channels to perform readout with both analog and digital signal with Time over Threshold. GEMINI also allows to set a different threshold for every channel to compensate channel inhomogeneity. This work compares simulations with lab measurements and presents results of the imaging of an X-ray source performed with GEMINI.
Adoption of disaster risk reduction strategy in agriculture sector at Southkhali Union of Sharankhola Upazila, Bangladesh

Received: 26 March 2019 Revised received: 12 May 2019 Accepted: 23 May 2019 Climate change is contemporary global threat especially for coastal area of Bangladesh which drastically affects in agricultural sector. The purpose of the study was to determine the extent of adopting disaster risk reduction strategies by farmer towards agriculture sector in Southkhali union of Sharankhola upazila. Data were collected following a structured pretested interview schedule, FGD and KII and then verified with secondary sources of information. Coefficients of Correlation and regression analysis were used to find out the contribution of factors to the variation of adopting disaster risk reduction strategy by farmers in agriculture sector. The study found that the salinity was the major climate change effect in agriculture sector of this area that resulted soil salinity in agriculture and the most common adopted strategies are rain water harvesting (90%), tree plantation (89.16%) and homestead gardening (80.83%) in agriculture. Correlation analysis indicated that age (0.383**), farming experience (0.441**), communication exposure (0.271*) and organizational support (0.226*) had positive significant relationship and climate change effect (-0.266**) had negative significant relationship with adoption of disaster risk reduction strategy of farmers in agriculture. Regression analysis revealed that farming experience (β=0.546) has the strongest contribution and climate change effect (β=-0.139) had negative contribution to the adoption of disaster risk reduction strategies in agriculture. Based on this observation it can be concluded that the awareness and skill of farmer should be more developed through arranging different training program and providing necessary support to promote environmentally safe cultivation and to enhance climate change adaptation in agriculture sector.
BASED STEM EDUCATION AND MOTIVATION OF YOUNG STUDENTS 3 right content , connect with other people and motivate the learner to learn

Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. The US National Science Foundation considers that in order to succeed in this information-based technological era, students need to develop their capabilities in Science, Technology, Engineering and Math (STEM) to higher levels than those acceptable in the past. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. This can be mainly attributed to pedagogical issues, such as the lack of engaging hands-on activities utilized for science and math education in middle and high schools. We consider that to raise student’s interest in the technical career path, it is important that students establish a link between the theoretical knowledge and its application to solve real-life problems early in their learning experience. In this paper we report our work in the SciChallenge European project, which aims at increasing the interest of preuniversity students in STEM disciplines, through its distinguishing feature, the systematic use of social media for providing and evaluation of the student-generated content. A social media-aware contest and platform were thus developed and tested in a pan-European contest that attracted >700 participants. The statistical analysis and results revealed that the platform and contest positively influenced participants STEM learning and motivation, while only the gender factor for the younger study group appeared to affect the outcomes (confidence level – p<.05).
WORK CONTEXT SPECIALIZATION, INDIVIDUAL EXPERIENCE DIVERSITY AND CORPORATE VENTURING IN A LARGE AND ESTABLISHED FIRM

Common wisdom holds that the high degree of specialization among individuals and work groups in large and mature firms enhances operational efficiency but stifles individuals’ entrepreneurial initiatives. Yet it remains unclear how individuals could navigate in such work contexts to develop their own entrepreneurial initiatives. Drawing on theories of entrepreneurship and innovation in large and mature organizations, I suggest that one strategy for individuals is to accumulate diverse intra-firm experiences, which would allow them to overcome the constraints for entrepreneurship in their work contexts or even leverage their work contexts for entrepreneurial activities. Using unique data from a large and established firm in the home appliance industry, I find that while working at more specialized work groups dampens individuals’ chance of engaging in corporate venturing activities, the diversity of individuals’ own intra-firm experiences enhances the chance. Moreover, I find that the enhancing effect of individuals’ intra-firm experience diversity becomes stronger as the degree of specialization in individuals’ work groups increases. I suggest that this positive moderating effect of contextual specialization reflects its complementarity with individual experience diversity in shaping individuals’ capacity for orchestrating organizational resources for their entrepreneurial activities within large and mature organizations.
Microwave detection of surface breaking cracks in metallic structures under heavy corrosion and paint

We live in the world of “aging infrastructures”. In this environment, critical and heavily utilized infrastructure, i.e. ships, planes, bridges, etc., are operating at or beyond their designed age. Replacement is no longer an option and “retirement for cause” is the current approach to maintenance and replacement. Consequently, there is an everincreasing demand for efficient and robust nondestructive evaluation (NDE) methods that can determine the physical health of these structures. Large structures, which are primarily made of metals, either steel or aluminum, are susceptible to high-stress cracking and corrosion. Stress-induced cracks in heavily corroded steel, used in bridges, railroads, storage tanks, etc., are extremely difficult to detect. Current methods have limitations that render inspection to take longer either than it should or risk not detecting an existing crack. Microwave signals readily penetrate through dielectric materials such as paint and corrosion byproducts (i.e., rust), while conducting materials (i.e., metals) strongly reflect microwave signals. Therefore, interrogating a metal surface for surfacebreaking cracks is readily possible even in the presence of a relatively thick layer of corrosion or paint. Normally, surface-breaking cracks are very small and the perturbations they cause to an irradiating microwave signal are small in amplitude unless the detection is performed very close to the surface. In this thesis, the implementation of a microwave imaging system that utilizes a synthetic aperture radar (SAR) approach to detect surface-breaking cracks in metallic structures under heavy corrosion and corrosion preventive paints is investigated. The resulting SAR images were analyzed and compared to numerical simulations to identify real-world capabilities and theoretical limitations.
Backflashover Simulations of Multi-circuit Transmission Tower with AC / DC systems

ATP-EMTP simulations are performed to estimate backflashover performance of multi-circuit transmission tower. Multi-circuit transmission tower has several systems on the tower and combines DC transmission over long distances with more flexible AC transmission. The outcome of simulations should give the range of backflashover withstand level and backflashover outage level. Moreover subsequent strokes were included to lighting performance evaluations of line. Maximum lightning current amplitude that does not cause backflashover across insulator string is estimated in response to first and subsequent lightning strokes with three different flashover models.
Efficient Multi-Class Probabilistic SVMs on GPUs ( Extended Abstract )

Multi-class SVMs with the probabilistic output (MPSVMs) are important techniques in pattern recognition. Two key challenges for efficient GPU accelerations for MP-SVM are: (i) many kernel values are repeatedly computed as a binary SVM classifier is trained iteratively, resulting in repeated accesses to the high latency GPU memory; (ii) performing training or estimating probability in parallel requires a much larger memory footprint than the GPU memory. To overcome the challenges, we propose GMP-SVM to reduce high latency memory accesses and memory consumption through batch processing, computation/data reusing and sharing. Experimental results show that our solution (available in https://github.com/Xtra-Computing/thundersvm) outperforms LibSVM by 100 times while retaining the same accuracy.