Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent Schema Management for P2P-based Applications

In p2p based data mangement applications, it is unrealistic to rely upon a centralized schema or ontology. The p2p paradigm is more than a new underlying infrastructure. It supports an emergent approach to data management where the data is generated and inserted into the network in a decentralized fashion. Thus, each peer or group of peers will have its own schema to store the data. Moreover, the user querying the data will use yet another schema to formulate the request. The vision of emergent schema management is to resolve these heterogeneities automatically in a self-organizing, emergent way by taking advantage of overlaps and mediators scattered over the network. The emerging schema information can be used in various ways, i.e. to drive the construction of an overlay network, and to route queries through the network. In this article, we start by explaining the various challenges. We look at the problem both from the viewpoint of the database community describing schemas as entity-relationship models, and from the viewpoint of the knowledge representation community using logic-based formalisms. We then survey existing p2p based approaches dealing with semantics, schemas, and mediation. After describing our own approach to p2p schema management, we conclude with an outlook to open problems in the ﬁeld.
Classification for Crisis-Related Tweets Leveraging Word Embeddings and Data Augmentation

This paper presents University College Dublin’s (UCD) work at TREC 2019-B Incident Streams (IS) track. The purpose of the IS track is to find actionable messages and estimate their priority among a stream of crisis-related tweets. Based on the track’s requirements, we break down the task into two sub-tasks. One is defined as a multi-label classification task that categorises upcoming tweets into different aid requests. The other is defined as a single-label classification task that estimates these tweets with four different levels of priority. For the track, we submitted four runs, each of which uses a different model for the tasks. Our baseline run trains classification models with hand-crafted features through machine learning methods, namely Logistic Regression and Naïve Bayes. Our other three runs train classification models with different deep learning methods. The deep methods include a vanilla bidirectional long short-term memory recurrent neural network (biLSTM), an adapted biLSTM, and a bi-attentive classification network (BCN) with pre-trained contextualised ELMo embedding. For all the runs, we apply different word embeddings (in-domain pre-trained, word-level pre-trained GloVe, character-level, or ELMo embeddings) and data augmentation strategies (SMOTE, loss weights, or GPT-2) to explore the influence they have on performance. Evaluation results show that our models perform better than the median for most situations.
Michael D. Adams: Research Statement

In my vision of an ideal future, programmers would be able to communicate their intent to a computer (i.e., write programs) as quickly as and at the same high level that they can communicate to other programmers, and the resulting software artifacts would be clear and concise enough that they obviously have no bugs instead of having no obvious bugs. My research area is programming languages, and my research is aimed at achieving this future. As such, the thread that connects all of my research is language tools and features that enable programmers to write clear, concise and elegant code and do so without sacrificing other things like performance. I view this as key to improving programmer productivity and code comprehension and allowing programmers to more effectively design and implement programs. These also have positive effects on other research areas. For example, better languages and tools can make it easier to detect security vulnerabilities or prevent them in the first place, and better languages and tools can also make programming more approachable and aid in computer-science education. The future I describe is a ways off, but it is one that I am excited to work towards.
WaterlooClarke at the TREC 2021 Conversational Assistant Track

The three runs were based on raw utterances, canonical response, and manually rewritten utterances respectively. This report describes the generation and the results of each of these runs. The overall approach consists of three steps: 1) query reformulation, 2) passage retrieval, and 3) passage reranking. We did not apply the query reformulation step for the clarke-manual run as manually rewritten utterances were used. In order to improve our performance, this year we focused our effort on maximizing the total system recall at the first stage by employing both dense and sparse retrievers. Research has shown that sparse retrievers and dense retrievers can retrieve complementary information [3]. We merged the retrieved passages into a single pool, then reranked this pool using a two-stage reranking pipeline with monoT5 and duoT5 [4]. In the next section, we will explain the details of our methodology.
CCNU_IRGroup @ TREC 2019 Deep Learning Track

The deep learning track consists of two tasks: passage ranking and document ranking. The former focuses on long text retrieval, while the latter focuses on short text retrieval. Both tasks use a large human-labeled set, which is from the MS-MARCO dataset. For different emphases of the two tasks, we adopt two different BERT-based retrieval models. In Section 2 and 3, we will introduce our methods in details. In Section 4 and 5, we will discuss the experiments setting and results.
Exploiting User Interaction and Object Candidates for Instance Retrieval and Object Segmentation

This thesis addresses two of the main challenges nowadays for computer vision: object segmentation and visual instance retrieval. The methodologies proposed to solve both problems are based on the use of object candidates and human computation in the computer vision loop. In the object segmentation side, this work explores how human computation can be useful to achieve better segmentation results, by combining users’ traces with a segmentation algorithm based on object candidates. On the other hand, the instance retrieval problem is also addressed using object candidates to compute local features, and involving the user in the retrieval loop by applying relevance feedback strategies
A Bird’s Eye View on the Quantitative Semantics of Linear Logic

Since the inception of Linear Logic [6], there has been an increasing interest in applying linear algebra to the formal methods approach to computation. Data types are interpreted as vector spaces, the addition expressing a kind of superposition of atomic states and the scalars a quantitative estimation of such a superposition. Programs using their inputs exactly once correspond then to linear functions, while typical programs, using their inputs several (or none) times are represented by power series. These are the original intuitions of Girard’s quantitative semantics, and date back to [7]. Depending on the field of scalars, these semantics can model quantitative behaviors of programs, such as runtime estimations or resource analysis. Moreover, vectors are able to model overlapping of “inconsistent” information, a feature which becomes crucial when one wants to interpret non-deterministic computations or quantum data types. Indeed, recently we succeeded in developing very solid and precise models of probabilistic and quantum functional languages [2, 5, 11], filling rather big gaps in the literature. The goal of this presentation is to gently introduce some of the main ideas and most recent results in quantitative semantics.
A development of two-dimensional birefringence distribution measurement system with a sampling rate of 1.3MHz

A two-dimensional birefringence distribution measurement system with a sampling rate of 1.3 MHz is proposed. A polarization image sensor is developed as core device of the system. It is composed of a pixelated polarizer array made from photonic crystal and a parallel read out circuit with a multi-channel analog to digital converter specialized for two-dimensional polarization detection. By applying phase shifting algorism with circularly-polarized incident light, birefringence phase difference and azimuthal angle can be measured. The performance of the system is demonstrated experimentally by measuring actual birefringence distribution and polarization device such as Babinet–Soleil compensator. & 2013 Elsevier B.V. All rights reserved.
Orientability evaluation of concentric tube robots deployed in natural orifices

Concentric tube robot (CTR) is a promising class of continuum robot for medical interventions [1] which require to operate through natural orifices, such as trans-nasal skull base surgery [2], middle-ear surgery [3] and trans-urethral surgery [4]. During these applications, the access to the surgical site is difficult due to the narrowness of the natural orifices. In addition, capability to orient the surgical tool at the surgical site is of importance. CTR consists in a telescopic assembly of pre-curved tubes. The elastic interaction between the tubes creates distributed forces and torques which deform the robot backbone. The rotation and translation of the tubes allow then to control the shape of the robot during its deployment through natural orifices, as well as its tip pose during the operation. The capability of CTR to orient their tip while maintaining the same tip position at the surgical site, that is referred as orientability in [5], is of interest and actually limited in the abovementioned applications. Orientability is indeed not yet a criterion for CTR tube selection, and it is even today still difficult to assess. This work is dedicated to the development of a numerical evaluation of CTR orientability.
Dynamics of protest and electoral politics in the Great Recession

This article links the consequences of the Great Recession on protest and electoral politics. It innovates by combining the literature on economic voting with social movement research and by presenting the first integrated, large-scale empirical analysis of protest mobilisation and electoral outcomes in Europe. The economic voting literature offers important insights on how and under what conditions economic crises play out in the short-run. However, it tends to ignore the closely connected dynamics of opposition in the two arenas and the role of protests in politicising economic grievances. More specifically, it is argued that economic protests act as a ‘signalling mechanism’ by attributing blame to decision makers and by highlighting the political dimension of deteriorating economic conditions. Ultimately, massive protest mobilisation should, thus, amplify the impact of economic hardship on the electoral losses of incumbents and mainstream parties more generally. The empirical analysis to study this relationship relies on an original semi-automated protest event dataset combined with an updated dataset of electoral outcomes in 30 European countries from 2000 to 2015. The results indicate that the dynamics of economic protests and electoral punishment are closely related and point to a destabilisation of European party systems during the Great Recession.
Contrarian Strategy after Testing Overreaction Hypothesis in Cement Sector Companies Listed in Karachi Stock Exchange

The proposition that stock markets are efficient is expected to prevail in every stock exchange. It means that existing stock prices are determined with the help of complete information available. The unnatural changes in stock prices are basically due to biasness in human nature. Investors tend to overreact strongly to extreme news. Stock prices in the beginning of extremely bad news fall below their justified equilibrium price in the market and extremely good news has the reversal impact on the stock prices. This study investigates the overreaction hypothesis in the listed companies of Cement sector of KSE listed companies. Data for this empirical work is collected from the period January 2001 to December 2012. The data relates to the unadjusted stock prices of five cement companies. The secondary data is collected from AKD securities portfolios. The stocks selected for the study are those which have the complete data of the prices. This study assumes that current stock prices of the cement sector of the company are efficient. The study found evidence of existence of overreaction in stocks of cement sector as it is the case for all developing and developed countries stocks markets. However almost all the stated all the overreactions were statistically insignificant without eleventh and twelfth months. According to study the contrarian strategy may be adopted by the investors in the eleventh month. It is recommended that such studies may be conducted on the overall stocks of KSE 100 Index to give us the idea of the existence of the overreaction in the Market.
DeepMV

Recently, significant efforts are made to explore device-free human activity recognition techniques that utilize the information collected by existing indoor wireless infrastructures without the need for the monitored subject to carry a dedicated device. Most of the existing work, however, focuses their attention on the analysis of the signal received by a single device. In practice, there are usually multiple devices "observing" the same subject. Each of these devices can be regarded as an information source and provides us an unique "view" of the observed subject. Intuitively, if we can combine the complementary information carried by the multiple views, we will be able to improve the activity recognition accuracy. Towards this end, we propose DeepMV, a unified multi-view deep learning framework, to learn informative representations of heterogeneous device-free data. DeepMV can combine different views' information weighted by the quality of their data and extract commonness shared across different environments to improve the recognition performance. To evaluate the proposed DeepMV model, we set up a testbed using commercialized WiFi and acoustic devices. Experiment results show that DeepMV can effectively recognize activities and outperform the state-of-the-art human activity recognition methods.
Multi-domain Task Oriented Dialogue System Using Word Constraints

We introduce a new modeling method for multi-domain dialogue modeling. The proposed method infers domain and dialogue acts from utterance for a multi-domain dialogue model. With the inferred domain and dialogue act information, the model constrains the system utterance dictionary to enable the generation of utterances corresponding to the domain. In the experiments set up for the MultiWOZ dataset provided by the DSTC8 Track1 Multi-domain task completion track, the proposed method achieves 0.032 higher BLEU-1 score than the baseline method. We found that the modeling method and the results in this paper show that the domain estimation and dialogue act estimation in dialog modeling improve the performance. Also, when we implement the proposed dictionary constraint method, we could improve the domain cohesion performance and the utterance generation performance by limiting system utterance words.
Long-term hydrologic and stream chemistry responses of southern Appalachian catchments following conversion from mixed hardwoods to white pine

We summarize nearly 40 years of research on changes in water yield and timing of streamflow, and over 20 years of stream chemistry data, following conversion of two mixed deciduous hardwood forests to plantations of eastern white pine. Only 10 years after planting, annual streamflow from the pine-covered watersheds was less than expected from mixed hardwoods and by age 15, water yield reductions were about 20 cm. Subsequently, annual streamflow reductions frequently exceeded 25 cm. Significant flow reductions occurred in every month, with the greatest absolute changes in the high flow months and the largest percentage reductions (40%) in the low flow months. Based on hydrologic modelling and experimental data, greater evapotranspiration from pine is due to a much higher leaf area index throughout the year, and, consequently, greater interception loss in the dormant season and more transpiration loss in early spring and late fall. Flow duration analysis showed that conversion to pine reduced the frequency of both high and low flows by 33 to 60 percent. Concentrations of most streamwater solutes were similar between the pine covered watersheds and mature hardwoods. However, nutrient budget comparisons (input-output) showed net accumulations of 1.2 to 4.4 kg ha' yr for Ca, Mg, K, and Na in pine ecosystems compared to hardwoods. The conservation of nutrients in pine ecosystems is due to reduced discharge and also greater accretion of nutrients in the vegetation compared to hardwoods.
Synthesis and characterization of the mechanical properties of high-density polyethylene based composites reinforced with animal fibers. Leonardo J Sci 2016;29:99-112

In order to enhance mechanical properties of polymeric materials for structural applications, brown chicken feather fibers which are considered as waste materials were characterized and utilized to reinforced high density polyethylene. Brown chicken feather fibers (BCFF) were removed from brown chicken feathers by trimming after which part of the fibers were mercerized with 0.1 M KOH solution. The chicken feather fibers were then characterized to ascertain their elemental, crystallinity index and the morphological properties by using Atomic Absorption Spectrophotometer, XRays diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques, respectively. The composites were compounded by varying the fibers within the HDPE matrix. Compression moulding method was used to produce the composite samples. The neat and composite samples were characterized to ascertain their tensile and flexural properties in accordance with ASTM D3038M-08 and ASTM D7264M-07 standards. Morphological properties of the developed composites were also analyzed and it revealed that chemical treatment of the chicken feather fibers are potential means of 99 http://ljs.academicdirect. org/ Synthesis and characterization of the mechanical properties of high-density polyethylene based composites reinforced with animal fibers Avwerosuoghene M. OKORO, Isiaka O. OLADELE, Moshibudi C. KHOATHANE enhancing the reinforcement efficiency of chicken feather fibers for composites productions. The composite samples from the chemically modified chicken feather fibers exhibited the best mechanical properties in terms of tensile properties and flexural strength.
The Recognition System of Machine Parts' Characters Based On Stm32

: Intelligent identification of characters is widely used in many ways, such as license plate recognition, train number identification, container number identification and so on. The recognition system of machine parts' characters is an important component of anassembly line, which can greatly increase the production efficiency.In this paper, the system was designed based on an embedded development platform. The machine parts’ imageswere acquired by an ov7670 camera, then they were preprocessed by grayscale changing, denoising, morphological filtering, positioning and segmenting. At last, a template matchingmethod was used to recognize the segmented character blocks. The results were displayedon the LCD in real time
COEXISTENCE AND EXTINCTION FOR STOCHASTIC KOLMOGOROV SYSTEMS BY ALEXANDRU HENING

In recent years there has been a growing interest in the study of the dynamics of stochastic populations. A key question in population biology is to understand the conditions under which populations coexist or go extinct. Theoretical and empirical studies have shown that coexistence can be facilitated or negated by both biotic interactions and environmental fluctuations. We study the dynamics of n populations that live in a stochastic environment and which can interact nonlinearly (through competition for resources, predator– prey behavior, etc.). Our models are described by n-dimensional Kolmogorov systems with white noise (stochastic differential equations—SDE). We give sharp conditions under which the populations converge exponentially fast to their unique stationary distribution as well as conditions under which some populations go extinct exponentially fast. The analysis is done by a careful study of the properties of the invariant measures of the process that are supported on the boundary of the domain. To our knowledge this is one of the first general results describing the asymptotic behavior of stochastic Kolmogorov systems in non-compact domains. We are able to fully describe the properties of many of the SDE that appear in the literature. In particular, we extend results on two dimensional LotkaVolterra models, two dimensional predator–prey models, n dimensional simple food chains, and two predator and one prey models. We also show how one can use our methods to classify the dynamics of any two-dimensional stochastic Kolmogorov system satisfying some mild assumptions.
Do Educational Differences and Their Impact on Earnings Hamper Immigrants?

This paper analyzes the differences in educational rates of return between the foreignborn and native workers in France and Austria, and asks to what extent these differences result from a potential mismatch between the actual and required years of schooling typical for a certain occupation. The decomposition of the education variable into required education, overeducation and undereducation allows for better understanding of the reasons behind the lower payoff to schooling among migrants. The findings of the paper are generally in accordance with the existing studies, since overeducation contributes positively to earnings and undereducation lowers the expected payoff. However, what mainly accounts for the lower returns to education of immigrants in comparison to natives, is the substantial difference in returns to overeducation in favour of native workers. Undereducated migrants are penalized with respect to earnings by slightly higher negative returns, to undereducation than natives. JEL Classification: F22, I21, J24, J31, J61
NBER WORKING PAPER SERIES WINNERS AND LOSERS? THE EFFECT OF GAINING AND LOSING ACCESS TO SELECTIVE COLLEGES ON EDUCATION AND LABOR MARKET OUTCOMES

Selective college admissions are fundamentally a question of tradeoffs: Given capacity, admitting one student means rejecting another. Research to date has generally estimated average effects of college selectivity, and has been unable to distinguish between the effects on students gaining access and on those losing access under alternative admissions policies. We use the introduction of the Top Ten Percent rule and administrative data from the State of Texas to estimate the effect of access to a selective college on student graduation and earnings outcomes. We estimate separate effects on two groups of students. The first--highly ranked students at schools which previously sent few students to the flagship university--gain access due to the policy; the second--students outside the top tier at traditional “feeder” high schools--tend to lose access. We find that students in the first group see increases in college enrollment and graduation with some evidence of positive earnings gains 7-9 years after college. In contrast, students in the second group attend less selective colleges but do not see declines in overall college enrollment, graduation, or earnings. The Top Ten Percent rule, introduced for equity reasons, thus also seems to have improved efficiency. Sandra E. Black Department of Economics and School of International and Public Affairs Columbia University 1022 International Affairs Building 420 West 118th Street New York, NY 10027 and IZA and also NBER sblack@columbia.edu Jeffrey T. Denning Department of Economics Brigham Young University 165 FOB Provo, UT 84602 jeffdenning@byu.edu Jesse Rothstein Goldman School of Public Policy and Department of Economics University of California, Berkeley 2607 Hearst Avenue #7320 Berkeley, CA 94720-7320 and NBER rothstein@berkeley.edu