Miracle-dz/UniFAR-FTUs huggingface dataset:
A Clustering-Based Approach for Discovering Places in Trajectories: A spatio-temporal clustering technique

This work is a clustering point of view about how deduce interesting places considering the points and its speed in a trajectory context. The author explain a clustering trajectory technique and associates the discovered clusters with the trajectory context. The objective is to find out interesting places related to the person/object that generated the points of the trajectory looking mainly at the physical features of the trajectory, i.e. its speed. The clustering technique is extended to consider the spatio-temporal data instead use only the position (spatial) approach.
Sistema de Colonia de Hormigas Autoadaptativo para el Problema de Direccionamiento de Consultas Semánticas en Redes P2P

In this paper, we present a new algorithm to route text queries within a P2P network, called Neighboring-Ant Search (NAS) algorithm. The algorithm is based on the Ant Colony System metaheuristic and the SemAnt algorithm. More so, NAS is hybridized with local environment strategies of learning, characterization, and exploration. Two Learning Rules (LR) are used to learn from past performance, these rules are modified by three new Learning Functions (LF). A Degree-Dispersion-Coefficient (DDC) as a local topological metric is used for the structural characterization. A variant of the well-known one-step Lookahead exploration is used to search the nearby environment. These local strategies make NAS self-adaptive and improve the performance of the distributed search. Our results show the contribution of each proposed strategy to the performance of the NAS algorithm. The results reveal that NAS algorithm outperforms methods proposed in the literature, such as Random-Walk and SemAnt.
Community Perception towards Impact of Development in Sungai Terengganu

Objective of this study is to identify community perception based on development change at Terengganu River, Malaysia. Development alongside of riverside of Terengganu river give major changes towards physical and environmental impact it has given positive or negative impact towards the community that lives along the riverside. The aim of this research shows that the effect of Terengganu River bank development can be grouped into four principle impacts that were improvement on the riverside design and facility, tourist spot attraction, sustainable environment development and preservation of the natural environment. The discoveries of this article additionally demonstrate the riverbank advancement and natural changes give new move to neighborhood society advantages to produce higher quality and dynamic life. Questionnaire been used in this study are Impact Development River Side on Physical Environment and Human. Questionnaire been divided with 3 section with 10 question in for each question. Data analyzed using Descriptive Statistic on answering objective that been determined which is focus on the impact of the river basin erosion towards the physical changes on the Terengganu River and how it had been impacted to the community. Results from this demonstrates the riverbank advancement and natural changes give new move to neighborhood society advantages to produce higher quality and dynamic life. Researcher suggests that everybody must take part to ensure less negative impact towards the environmental side that will affect to the community later.
Modelling the average behaviour of a sample of curves with Bayesian regression splines

This paper is concerned with modelling longitudinal data nonparametrically in a Bayesian context. We develop a methodology, based on free-knot regression splines, in which the observations arise from a class of distributions that includes the continuous distributions of the exponential family. Approximations of the marginal distribution of the data are considered since this distribution plays a key role in the MCMC algorithm used to explore the space of knot con gurations. A strategy is proposed to discriminate which statistical distribution is the most adequate for a given data set. The methodology is applied to a hydrological sample of curves. Cet article étudie la modélisation non-paramétrique de données longitudinales dans un contexte bayésien. Nous développons une méthodologie, basée sur les splines de régression, selon laquelle les observations proviennent d'une classe de distributions continues qui inclut celles de la famille exponentielle. Di érentes approximations de la distribution marginale des observations sont étudiées puisque celle-ci joue un rôle important dans l'algorithme MCMC utilisé pour explorer l'espace des con gurations nodales. Une stratégie pour déterminer la distribution statistique la plus adéquate pour une collection d'observations donnée est aussi mise de l'avant. La méthodologie est appliquée à un échantillon de courbes hydrologiques.
Cross-Domain Sentence Modeling for Relevance Transfer with BERT

Standard bag-of-words term-matching techniques in document retrieval fail to exploit rich semantic information embedded in the document texts. One promising recent trend in facilitating context-aware semantic matching has been the development of massively pretrained deep transformer models, culminating in BERT as their most popular example today. In this work, we propose adapting BERT as a neural re-ranker for document retrieval to achieve large improvements on news articles. Two fundamental issues arise in applying BERT to “ad hoc” document retrieval on newswire collections: relevance judgments in existing test collections are provided only at the document level, and documents often exceed the length that BERT was designed to handle. To overcome these challenges, we compute and aggregate sentence-level evidence to rank documents. The lack of appropriate relevance judgments in test collections is addressed by leveraging sentence-level and passage-level relevance judgments fortuitously available in collections from other domains to capture cross-domain notions of relevance. Our experiments demonstrate that models of relevance can be transferred across domains. By leveraging semantic cues learned across various domains, we propose a model that achieves state-of-the-art results on three standard TREC newswire collections. We explore the effects of cross-domain relevance transfer, and trade-offs between using document and sentence scores for document ranking. We also present an end-to-end document retrieval system that integrates the open-source Anserini information retrieval toolkit, discussing the related technical challenges and design decisions.
EVALUATING TOTAL CONSUMER INVOLVEMENT WITH ONLINE INFORMATION- SEEKING: A FLOW STUDY

The construct of flow reflects a holistic experience in task involvement, a mental state characterized by deep concentration, enjoyment and no concerns about losing control or feeling under pressure. In the present research we have set out to investigate the probability of total emotional-cognitive involvement during the act of online information-seeking. We posit that flow is important to the study of informational behaviors in the digital age because it may serve as a key antecedent to more consumer generated value. Our belief is that expanding the body of work on online flow in the information search area may improve marketers understanding of informational behaviors in the digital age and provide inspiration for more efficient, co-creative communication strategies. Investigating factors that facilitate flow experience in online information search is of both theoretical importance and practical relevance. An in-depth understanding of how consumers experience information seeking online is critical to marketing programs. Our research investigates the flow potential of online information-seeking activities, and we have therefore developed and validated a context-specific flow measurement instrument. Each flow dimension has been evaluated separately and the overall measurement has been then calculated as a simple arithmetic mean of the four sub-scales. The analysis of scales reliability and validity revealed positive results and the significance level between the four flow factors confirmed the existence of a superior construct. Descriptive statistical analysis showed a moderate flow potential of informationseeking activities, with time distortion being the most pronounced dimension and focused attention the least evident one.
1 Second Industrial Revolution in Italy ( 1860-1913 )

The paper deals with two topics largely debated in economic history of the Italian “catch up” in the last quarter of 19 century. Some economic historians emphasize the discontinuity (Mori, 1992; Giannetti, 1998; Vasta, 1999) analyzing the Italian case in the context of the Second Industrial Revolution, when new “science based” sectors, like chemicals and electricity, drove the pattern of economic growth. Others (Bonelli, 1979; Cafagna, 1989; Federico, 1995) emphasize the continuity of the Italian growth, which was due essentially to the comparative advantage of a late comer in manufacturing sectors like textile (silk). We use the time series approach of cointegration and common trends to give a new perspective about these topics searching which were the leading sectors and the extent of interindustry linkages. We find that the new sectors – chemicals and electricityrepresenting the new “technological regime” of the Second Industrial Revolution, were the leading ones even in Italy, showing a common trend with the aggregate industrial production. A Granger causality approach, both among the cointegrated sectors and between them and all the others, confirms finally that electricity and chemicals where the leading sectors, suggesting that the new “science-based” industries were at the origins of the Italian “catch up” of the Giolitti’s Age (1890-1913).
Monetary Policy and the Wealth Effects of M&As

We highlight three channels through which the stance of the monetary policy at the time of both the design and the announcement of Mergers and Acquisitions (M&As) affects the acquirer value. The channel of ‘expected financing cost’ predicts that firms devoting financial resources to takeovers during periods of monetary tightening are likely to face significant financing costs during the post-acquisition period. As a result, M&As announced during periods of monetary tightening are associated with significant reductions in acquirer value. We show that this is more apparent for small merging firms that often face higher financing constraints. The channel of ‘monetary policy uncertainty’ predicts via a real options framework that M&As announced during periods of monetary uncertainty are poorly timed and hence lead to lower acquirer value. Lastly, the ‘informational’ channel predicts that the deviation of monetary policy from the natural interest rate depreciates the informational content of asset prices, which limits the ability of the acquirer to accurately evaluate the synergies of the deal and increase firm value. Our time series analysis further suggests that the real options channel that links the monetary policy uncertainty to the M&A activity is not functional during periods of monetary easing.
ULSAna: Universal Language Semantic Analyzer

We present a live cross-lingual system capable of producing shallow semantic annotations of natural language sentences for 51 languages at this time. The domain of the input sentences is in principle unconstrained. The system uses single training data (in English) for all the languages. The resulting semantic annotations are therefore consistent across different languages. We use CoNLL Semantic Role Labeling training data and Universal dependencies as the basis for the system. The system is publicly available and supports processing data in batches; therefore, it can be easily used by the community for the following research tasks.
Flexibility of light-harvesting in iron-stressed cyanobacteria Flexibility of light-harvesting in iron-stressed cyanobacteria

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Factors determining crop farmers willingness to pay for agricultural extension services in Tanzania: A case of Mpwapwa and Mvomero Districts

This study assessed crop farmers’ willingness to pay for AESs and identified factors influencing their willingness to pay for AESs. Data were collected from 292 randomly selected crop farmers’ households between December 2017 and February 2018 using a questionnaire through face-to-face interviews. Data were analyzed using frequency counts, percentages and Tobit regression model. The study found that 92% of the respondents are willing to pay for AESs. It was also found that farmer’s age, education attainment, farming experience, distance from farm to the nearest important road, income (both farm and nonfarm) and attitude towards AESs are significant determinants of farmers willingness to pay for AESs. The study recommends that these variables be given proper policy consideration by the government and other stakeholders in the design and the implementation of a workable fashion of privatizing extension services for the expected impact of improving extension services and farmers’ productivity hence improved quality of life. Key words: Extension services, willingness to pay, crop farmers, Mpwapwa, Mvomero
Genetic polymorphism of STAT1 and STAT5A genes in Holstein, jersey, and indigenous cattle breeds in Turkey.

This study aimed to determine genetic polymorphism in STAT1 and STAT5A genes for dairy cattle and some native cattle breeds in Turkey. 283 Jersey and a total of 472 Holstein cows from two different herds and 93 Grey Steppe, 85 Anatolian Black Cattle, and 66 East Anatolian Red cattle were used in this research. Generally, C allele gene frequency was higher than T allele for STAT1 in all breeds whereas C allele gene frequency was detected higher than G allele for STAT5A in Jersey and East Anatolian Red. On the other hand, G allele gene frequency was higher than C allele in Holstein, Grey Steppe, and Anatolian Black Cattle breeds. The expected deviations from the Hardy-Weinberg Equilibrium were significant only for Jersey breeds for STAT1 gene. Meanwhile, the expected deviation from equilibrium was also significantly different for Holstein in Black Sea Region (BSR), Anatolian Black Cattle and Grey Steppe for the STAT5A gene. FIS values were determined to STAT1 gene as negative for all breeds except for Holstein in Marmara Region (MR). Similarly, this value was determined to STAT5A gene as positive for all breeds except for Holstein in BSR. The genetic distances for two loci were calculated between 0.0029 and 0.1599 among all populations. Depending on the cluster analysis, Holstein in BSR and MR, Anatolian Black Cattle, East Anatolian Red were closely clustered to each other, while Grey Steppe and Jersey were located in completely different clusters. As a conclusion, based on the detected genetic diversity in STAT1 and STAT5A genes, it is possible to make a genetic improvement among bovine breeds raised in Turkey.
CryptoMining : Energy Use and Local Impact ∗

Cryptomining gives rise to negative externalities through consumption of scarce electricity. Thus why do local governments pursue cryptominers and what are the broader effects of cryptomining on the local economy? Testimonial evidence supports cryptomining as a source of tax revenues and purported local economy spillovers. We assemble a novel panel dataset for cities in China and New York State to take these claims to the data. First, we estimate that in China cryptomining operations lead to 10% higher energy consumption, which is mainly derived from fossil fuels. Second, we study the local effects of cryptomining on the public, household and business sectors. We find that cryptomining substantially increases business tax revenues relative to GDP in Chinese cities, thus providing a strong incentive for local governments to attract this type of business. However, we also find a negative impact on local wages and value added taxes (as a fraction of GDP). In New York State, we find that cryptomining results in substantially higher electricity prices for both households and businesses. These findings suggest that cryptomining leads to crowding-out of other economic activities and point to possibly unintended consequences that local governments should factor in their decisions. PRELIMINARY AND INCOMPLETE COMMENTS WELCOME ∗We are grateful to Aiting Kuang, Tanvi Shinkre, George Bao Lee, David Bian, Suyash Gupta, Jerry Song, Ethan Teo, and Yuan Xie for excellent research assistance. †University of California, Berkeley. Email: benetton@berkeley.edu. ‡University of California, Berkeley. Email: gcompiani@berkeley.edu. §University of California, Berkeley and NBER. Email: morse@haas.berkeley.edu.
Automatic Detection of Translation Direction

Parallel corpora are crucial resources for NLP applications, most notably for machine translation. The direction of the (human) translation of parallel corpora has been shown to have significant implications for the quality of statistical machine translation systems that are trained with such corpora. We describe a method for determining the direction of the (manual) translation of parallel corpora at the sentence-pair level. Using several linguistically-motivated features, coupled with a neural network model, we obtain high accuracy on several language pairs. Furthermore, we demonstrate that the accuracy is correlated with the (typological) distance between the two languages.
University of Birmingham Transcranial direct current stimulation can enhance working memory in Huntington's disease

Transcranial direct current stimulation (tDCS) combined with a cognitive task can enhance targeted aspects of cognitive functioning in clinical populations. The movement disorder Huntington’s disease (HD) is associated with progressive cognitive impairment. Deficits in working memory (WM) can be apparent early in the disease and impact functional capacity. We investigated whether tDCS combined with cognitive training could improve WM in patients with HD, and if baseline clinical or cognitive measures may predict efficacy. Twenty participants with HD completed this crossover trial, undergoing 1.5mA anodal tDCS over left dorsolateral prefrontal cortex and sham stimulation on separate visits. Participants and assessor were blinded to condition order, which was randomised across participants. All participants completed baseline clinical and cognitive assessments. Preand post-stimulation tasks included digit reordering, computerised n-back tests and a Stroop task. During 15 minutes of tDCS/sham stimulation, participants practiced 1and 2-back WM tasks. Participants exhibited an increase in WM span on the digit re-ordering span task from preto post-stimulation after tDCS, but not after sham stimulation. Gains in WM were positively related to motor symptom ratings and negatively associated with verbal fluency scores. Patients with more severe motor symptoms showed greatest improvement, suggesting that motor symptom ratings may help identify patients who are most likely to benefit from tDCS. Conclusions: Dorsolateral prefrontal tDCS appears well tolerated in HD and enhances WM span compared to sham stimulation. Our findings strongly encourage further investigation of the extent to which tDCS combined with cognitive training could enhance everyday function in HD. ClinicalTrials.gov; NCT02216474 Brain stimulation in Movement Disorders; https://clinicaltrials.gov/ct2/show/NCT02216474
THE RELATIONSHIP BETWEEN SERVICE CULTURE AND SERVICE INNOVATION: THE MEDIATING ROLE OF SERVICE ORIENTATION

The objective of the study is to investigate the inter-relationship between service culture, service orientation and service innovation in the small-medium enterprises (SMEs) in the city of Bengkulu, Indonesia. A sample of 300 small-medium enterprises in the service industry in the city was employed in the study. Mediated regression analysis was applied to the dataset to examine the proposed model. The result of the study reveals that service culture has a positive effect on service orientation and service innovation of the small-medium enterprises. The study also concludes that service orientation is indeed mediating the relationship between service culture and service innovation. Thus, in implementing a service innovation strategy, small-medium enterprises should build strong service orientation and service culture. Keywords: 1 Service Orientation · 2 Service Culture · 3 Service Innovation, SMEs.
Implications of Sustainability toward Green Innovation Investment : Livestock Industry Perspective

This research study examines the effect of sustainability on green innovation in the livestock industry of Pakistan. The main concern is related to the different dimensions of sustainability that influence green innovation. The cross-sectional analysis conducted was based on primary data collected from the livestock industry that works in the Pakistani districts. The empirical findings are based on structural equation modeling and propose that the economic, institutional and social aspects comprehensively affected green innovation. The environmental aspect has shown a comparatively less-contributing effect, which means a significant relationship exists between environmental sustainability and the investment in green innovation. The results recommend that economic and institutional sustainability could be a good source of innovation that affects social sustainability.
The “Jump and Stay” Method to Discover Proper Verb Centered Constructions in Corpus Lattices

The research presented here is based on the theoretical model of corpus lattices. We implemented this as an effective data structure, and developed an algorithm based on this structure to discover essential verbal expressions from corpus data. The idea behind the algorithm is the “jump and stay” principle, which tells us that our target expressions will be found at such places in the lattice where the value of a suitable function (defined on the vertex set of the corpus lattice) significantly increases (jumps) and then remains the same (stays). We evaluated our method on Hungarian data. Evaluation shows that about 75% of the obtained expressions are correct, actual errors are rare. Thus, this paper is 1. a proof of concept concerning the corpus lattice model, opening the way to investigate this structure further through our implementation; and 2. a proof of concept of the “jump and stay” idea and the algorithm itself, opening the way to apply it further, e.g. for other languages.
THE PRICING OF SWAPTIONS AND CAPS UNDER THE GAUSSIAN MODEL OF INTEREST RATE

In this paper, we use Jarnshidian's [8] approach to express the swaption, often priced as an interest rate option, as a bond option. We derive a simple closed-form solution for the swaption pricing formula. Modifications so as to fit the current term structure of the interest rate level and volatility are introduced. In addition, the sensitivity of this option, and bond options in general, with respect to the volatility of the interest rate is analyzed. Furthermore, we outline the difference between swaptions and caps, floors and collars. Adapting Longstaff's [10] analysis to the Ornstein-Uhlenbeck process, we express these contracts as options on yields. This analysis outlines the misuse of the Black Scholes model that consists of simply plugging the forward rate in the payoff function as an approximation.