Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Search Federation System Based on Online User Feedback

Today’s popular web search engines expand the search process beyond crawled web pages to specialized corpora (“verticals”) like images, videos, news, local, sports, finance, and shopping etc., each with its own specialized search engine. Search federation deals with problems of the selection of search engines to query and merging of their results into a single result set. Despite a few recent advances, the problem is still very challenging. First, due to the heterogeneous nature of different verticals, how the system merges the vertical results with the web documents to serve the user’s information need is still an open problem. Moreover, the scale of the search engine and the increasing number of vertical properties requires a solution which is efficient and scaleable. In this paper, we propose an unified framework for the search federation problem. We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. KDD’13, August 11–14, 2013, Chicago, Illinois, USA. Copyright 2013 ACM 978-1-4503-2174-7/13/08 . $15.00.
Comparative proteomic analysis of the lactating and nonlactating bovine mammary gland

The objective of this study was to determine how bovine mammary protein profiles vary during lactation and the dry period. Three lactating and 3 nonlactating cows were selected for mammary gland tissue sampling. Compared with the mammary proteins in nonlactating cows, a total of 60 differentially expressed proteins (DEP, including 57 upregulated and 3 downregulated) were identified in lactating cows using 2-dimensional difference gel electrophoresis combined with mass spectrometry. These DEP included enzymes and proteins associated with various macromolecular metabolic processes, and appeared to promote the increased metabolic activity associated with milk synthesis and secretion. The increased DEP were primarily related to initiation, maintenance, and involution of lactation, and included proteins involved in glycolysis/gluconeogenesis, the tricarboxylic acid cycle, the pentose phosphate pathway, oxidative phosphorylation, aminoacyltransfer RNA biosynthesis, and fatty acid biosynthesis. Identified DEP were further validated by real-time, reverse-transcription PCR and Western blot. Five new DEP associated with lactation were uniquely identified. This work provided some protein-associated insights to facilitate further investigation of the mechanisms underlying lactation in dairy cows.
EasyChair Preprint No 389 Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks

In a smart manufacturing system involving workers, recognition of the worker’s activity can be used for quantification and evaluation of the worker’s performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband. The raw 10-channel IMU signals are stacked to form a signal image. This image is transformed into an activity image by applying Discrete Fourier Transformation (DFT) and then fed into a Convolutional Neural Network (CNN) for feature extraction, resulting in a high-level feature vector. Another feature vector representing the level of muscle activation is evaluated with the raw 8-channel sEMG signals. Then these two vectors are concatenated and used for work activity classification. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab tool/part, hammer nail, use power-screwdriver, rest arm, turn screwdriver, and use wrench. The developed CNN model is evaluated on this dataset ∗Corresponding author E-mail address: w.tao@mst.edu (Wenjin Tao). Preprint submitted to EasyChair August 1, 2018 and achieves 98% and 87% recognition accuracy in the half-half and leave-oneout experiments, respectively.
Roving Mind: a balancing act between open–domain and engaging dialogue systems

Open–domain dialogue systems should be able to cover a very large set of domains and at the same time keep the user engaged in the interaction. Current approaches to dialogue modeling are divided between domain–independent, non–modular approaches using sequence–to–sequence models and the domain–speciﬁc modular systems developed for task–based dialogue. Furthermore, user engagement in dialogue, addressed in this bot challenge, is a rather new research area. In this work we describe Roving Mind, a dialogue system which combines domain– independence with a modular architecture for open–domain spoken conversation, with a speciﬁc module for user engagement. Our architecture takes a balanced approach between human expert design and data–driven approaches, adapting the traditional task–based architecture relying on slots and intents to open–domain conversation using entities and domain-independent dialogue acts. The system was tested extensively during the Alexa Prize semiﬁnals and received ratings from a large number of users, which allowed us to draw statistically relevant facts on how users interact with an open domain, non task-based social bot. Our experiments show that on average users give higher ratings to task-driven conversations (using user–entertaining strategies) compared to completely open–topic conversations. We also ﬁnd a correlation between cumulative sentiment (using sentiment and dialogue acts) and user ratings and argue that this could be investigated to estimate an error–signal for strategy computation.
Monitoring Data Quality Based on Conditional False Discovery Rate

In this paper, we develop a new multivariate exponentially weighted moving average (MEWMA) scheme to on-line monitor the data quality of high-dimensional data streams. With thousands of input data streams, typical metrics like the probability of a false alarm and average run length (ARL) lose their usefulness because one will likely have out-of-control (OC) signals at each time period. Hence, we via a bootstrap approach to control the conditional false discovery rate (CFDR) at each time point given that there is no alarm among all the ongoing streams before the current time point. Numerical results show that the performance of the proposed MEWMA scheme is quite satisfactory.
Real-time Serverless : Cloud Resource Management for Bursty , Real-time Workloads

Today’s cloud resource offerings provide no guarantees for resource allocation, so bursty application must reserve, and pay for resources they do not use to achieve real-time guarantees. We propose a new type of cloud resource, Realtime Serverless (RTS) with a new service-level objective – guaranteed allocation rate. This guarantee enables timely resource allocation, enabling applications to achieve real-time performance efficiently. With a simple burst model, we study real-time serverless analytically, exploring their effect on application quality, guarantees, and cost. Next, we simulate statistically varying bursts and higher loads (multi-application), to study the impact of real-time serverless. In both analytic and simulation studies, adding guaranteed allocation rate enables bursty, real-time applications to achieve guaranteed high quality cost-effectively. Specifically, for a desired application quality, the required allocation rates can be determined. In addition, for duty factors from 0.025 to 0.25, the value of real-time serverless to the application is > 4x than traditional. Further results show that multiple applications can share real-time serverless efficiently, supporting duty factor increases of 25x with only a 1.6x increase in allocation rate (provider resource cost). Finally, we present a case study of a traffic monitoring application. Despite more complex burst statistics, our results show major benefits for cost and application quality. Application benefit makes realtime serverless worth nearly 16x virtual machine resources for delivering application value.
Keyaki Treebank: phrase structure with functional information for Japanese

This paper describes our project of building a Treebank for Japanese following the Annotation manual for the Penn Historical Corpora and the PCEEC (Santorini 2010) with adaptations appropriate for Japanese. In addition to other motivations for building treebanks (to investigate syntactic phenomena, evaluate theories, extract grammars, train and test parsers, etc.), we have as a key goal the ambition to build a syntactic base able to support automatically deriving meaning representations for formal semantics research on a corpus linguistics scale. The paper is structured as follows. Section 2 offers background on existing treebanks that have directly influenced our decisions. Section 3 introduces and illustrates the adopted parsing scheme. Section 4 sketches our method of treebank construction. Section 5 outlines current and planned content for our treebank. Section 6 considers maintaining compatibility with existing treebank resources for Japanese. Section 7 concludes.
The Valuation of Photovoltaic Power Generation Based on Real Options

This paper provides a decision analysis of photovoltaic power generation investment based on real option theory. The main purpose is to evaluate the investment of photovoltaic power generation under different uncertainties. We consider investment cost, electricity price, carbon price and government subsidies in this model. The result shows that photovoltaic power generation investment values are negative under the existing investment environment in China, and enterprises should not invest in photovoltaic power generation project. The research results would be useful for photovoltaic power generation project evaluation and the related decision-marking.
Use Satellite Infrared Brightness Temperature Data to Evaluate

Satellite GOES-13 IR brightness temperature data were used to evaluate HWRF's forecast of tropical cyclones (TCs) using the Ferrier Aligo (FA) microphysics scheme with the total condensate advection (operational) versus the FA scheme with separate hydrometeors advection (FA-adv). Three real-case hurricanes and an idealized one were used as the study cases. Comparing the IR brightness temperature images from the model and the observation, we found that the FA-adv method forecasted significantly larger-sized storms with a weaker maximum wind speed than the operational FA method. Besides the satellite images, this larger size and weaker maximum wind speed by FA-adv were also confirmed by other metrics such as the modeled hydrometeor fields, pressure-wind scatter plots, multi-cycle intensity statistics, and 10-m wind and MSLP patterns. The idealized HWRF TC simulation revealed that the FA-adv method produced a larger upward cloud water advection than FA. The difference in the cloud water upward advection between FA and FA-adv led to more diabatic heating in FA-adv than FA, and this difference in diabatic heating caused more angular momentum to be imported into the FA-adv vortex, thus expanding the size of its simulated storm. An analysis using the cyclostrophic balance showed that the diabatic heating radial profile causes the maximum 10-m wind to be weaker in FA-adv than FA, but in regions of the storm away from the vortex center, the surface winds are stronger in FA-adv than FA. In summary, although in theory FA-adv should be more realistic, the hurricane sizes and structures forecasted by FA agree with the observations better than those forecasted by FA-adv. It should be noted that HWRF, like other numerical weather prediction (NWP) models, is a very complex system, and the tuning in other parts of the model system could have masked the errors introduced by the total condensate advection. In this study we focused on understanding the mechanisms that are responsible for the forecasted discrepancies between FA and FA-adv, instead of seeking to improve the HWRF performance with FA-adv. Future work is needed to identify those tunings so that the separate hydrometeors advection can achieve better forecast performance.
Product quality of hydrochar from sewage sludge in terms of micropollutants

Sewage sludge and hydrochar were analyzed in terms of organic micropollutants to assess if hydrothermal carbonization affects the product quality, when the material is used for agricultural purpose. In fact, the typical sewage sludge contaminants polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls (PCB) and polychlorinated dibenzodioxins and furans (PCDD/F) were investigated. Further, the behavior of pharmaceuticals and contrast media was studied. We conducted hydrothermal carbonization (HTC) in laboratory scale and transferred established analytical methods from sewage sludge to hydrochar. Accelerated solvent extraction (ASE) and liquid chromatography tandem mass spectrometry (LC-MS/MS) methods were developed to analyze pharmaceuticals and contrast media while PAK, PCB and PCDD/F analyzes were regulatory procedures. Metoprolol, propranolol and roxithromycin degraded during HTC. Residual amounts depended on initial load and compound class. Sulfamethoxazole and the contrast media were neither detected in the sewage sludge nor in the hydrochar. The sewage sludge and the corresponding hydrochar contained marginal amounts of PCB. PAH contamination occurred in sewage sludge to an amount that the initial fraction covered a potential additional origin from the HTC. In contrast, PCDD/F increased considerably from 4.5 ± 2.3 ng/kgDM I-TEQ in sewage sludge to 19.2 ± 1.7 ng/kgDM I-TEQ in hydrochar. The results show, that legislative regulations are required, if hydrochar is applied in agriculture. Environmental effects of hydrochar need further comprehensive evaluation.
Mean-variance Theory with Imprecise Accounting Information * Gady

In this paper, we examine the impact of imprecise accounting information on optimal portfolio choice in the mean-variance sense. We provide a theoretical platform illustrating the exact way in which imprecise return errors affect portfolio choice and alter the optimal vector of weights. We demonstrate that the covariance between actual return and return error could partly offset the impact of low-quality information on variance-covariance matrix. This is in agreement with empirical evidence suggesting that optimal portfolio weights are highly sensitive to small estimation errors in expected returns, but they are less sensitive with respect to errors in return variance estimates. JEL classification: G11.
Grammar Checker for Asian Languages : A Survey Misha Mittal

Grammar checker is one of the natural language processing applications that is used with most of the word processors. Various grammar checkers have been developed for Indian and foreign languages. In this research article author has performed a survey on the grammar checkers developed for Asian languages. In Asian languages mainly developed grammar checker are for Punjabi language, Urdu language, Nepali language and Bangla language. The architecture of all these grammar checker has been discussed in this paper.
Wavelet-b ased Algorithms for Speckle Removal from B-Mode Images

In this work we present a comparative study between two wavelet-based algorithms for speckle suppression in medical ultrasound images. The former is based on an adaptive, data-driven soft thresholding procedure, the latter performs an adaptive rescaling of the wavelet detail coefficients with a minimum mean square error (MMSE) filtering approach. These methods were firstly evaluated in terms of reconstruction accuracy and computational cost, then compared with reference to denoising B-mode images of prostate affected by carcinoma.
Ju l 2 01 9 Low-Shot Classification : A Comparison of Classical and Deep Transfer Machine Learning Approaches

Despite the recent success of deep transfer learning approaches in NLP, there is a lack of quantitative studies demonstrating the gains these models offer in low-shot text classification tasks over existing paradigms. Deep transfer learning approaches such as BERT and ULMFiT demonstrate that they can beat state-of-the-art results on larger datasets, however when one has only 100-1000 labelled examples per class, the choice of approach is less clear, with classical machine learning and deep transfer learning representing valid options. This paper compares the current best transfer learning approach with top classical machine learning approaches on a trinary sentiment classification task to assess the best paradigm. We find that BERT, representing the best of deep transfer learning, is the best performing approach, outperforming top classical machine learning algorithms by 9.7% on average when trained with 100 examples per class, narrowing to 1.8% at 1000 labels per class. We also show the robustness of deep transfer learning in moving across domains, where the maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross domain, compared to classical machine learning which loses up to 20.6%.
A fast stitching method for container images using texture and weighted speed

With the rapid development of oceans economy, a huge number of shipping containers are transported all around the world. To reduce the risk of container damages during the transportation, existing solution relies mainly on human beings to observe the container appearance before or after it enters a dock, which is time-consuming and inaccurate. To solve this problem, one intelligent approach is to develop an automatic container damage detection framework based on computer vision techniques. But how to obtain the panorama images for container damage detection is a challenging issue. In this paper, a real-time container panorama producing system is developed based on container surveillance videos, which is implemented by container image stitching with texture features. When there is no reliable offset, the weighted speed for splicing is used. Experimental results indicate that the developed system could achieve approving results in a real-time manner.
Spin Waves and Supercritical Motion in Superfluid 3

Helium is the second most abundant element in the Universe. It is the only known substance that can exist in liquid state at absolute zero. There are two stable isotopes of helium, fermionic 3He and bosonic 4He. At sufficiently low temperatures, both isotopes undergo a phase transition into a superfluid state. These superfluids are usually characterised by their ability to flow without resistance, but this is by no means their only remarkable property. In this thesis, we study theoretically superfluid 3He. The work consists of two separate projects. First, we study the effect of a quantised vortex line to spin dynamics of the superfluid. We find that the interplay between the vortex and the magnetisation of the liquid generates spin waves, dissipating energy. We find that the theoretically predicted energy dissipation is in agreement with experimental data, implying that spin-wave radiation can be an important mechanism of magnetic relaxation in superfluid 3He. Second, we study the drag force acting on an object moving through zerotemperature superfluid at a constant velocity. The drag arises if momentum is transferred from the object to the fluid. At low velocities, no such mechanism exist and thus the drag vanishes. If the velocity exceeds the Landau velocity vL, it becomes possible for the object to create quasiparticle excitations that could, in principle, transfer momentum away from the object. Thus, vL has been generally assumed to be the critical velocity, that is, the velocity above which the drag force starts to increase rapidly towards the normal-state value. We find that this is not necessarily the case. Objects much larger than the superfluid coherence length modify the superfluid flow field around them. The spatial variation of the flow field can shield the object, preventing quasiparticles from transferring momentum away from the object. This leads to a critical velocity greater than vL.
Carbon fluxes and emission from the Red River ( Vietnam and China ) : human activities and climate change

quynhltp@yahoo.com 2. Dr DUONG Thi Thuy, Institute of Environmental Technology, Vietnam, dthuy@yahoo.com 3. Dr HO Tu Cuong, Institute of Environmental Technology, Vietnam, hotucuong@gmail.com 4. Mrs TRAN Thi Bich Nga, Agency of Meteorology, Hydrology and Climate Change, Vietnam, bich_nga208@yahoo.com 5. Prof LU XiXi, National University of Singapore, Singapore, geoluxx@nus.edu. sg 6. Dr GARNIER Josette, University of Pierre and Marie Curie, France, josette.garnier@upmc.fr 7. Dr BILLEN Gilles, University of Pierre and Marie Curie, France, gilles.billen@upmc.fr 8. Dr MARCHAND Cyril, IRD, France, cyril.marchand@ird.fr 9. Prof ZHOU Yue, Yunnan University of Finance and Economics, China, zhouyue00000@163.com Final Report
The hypercube and other hash-based partitioning schemes are sensitive to skew

Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of events. Existing state-of-the-art monitors can process streams of modest velocity in real-time: a few thousands events per second. We scale up monitoring to higher velocities by slicing the stream, based on the events’ data values, into substreams that can be independently monitored. Because monitoring is not data parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement the resulting automatic data slicer in Apache Flink and use the MonPoly tool to monitor the substreams. We empirically evaluate this setup, demonstrating a substantial scalability improvement.
Design and Implementation of an End-to-End Speech Assistant

Efficient communication between robots and humans represent an important field of research. Recent advances in machine learning, specifically in the field of natural language processing, enable smart robots to benefit from voice interaction interfaces. This interface allows customers to communicate with robots naturally and conveniently over spoken utterances by employing a personal component at the same time. However, most of the available systems ,for example, require a stable internet connection to work, which interferes with standard requirements of service robots. This thesis, therefore, aims to (a) provide thorough research of current opensource frameworks with an emphasis on clearly defined objectives of a service robot within a hotel environment; (b) design and implement a speech assistant application; (c) evaluate the developed system by comparing it to other commercial state-of-the-art systems.