Miracle-dz/UniFAR-FTUs huggingface dataset:
Say it with Emojis: Co-designing Relevance Cues for Searching in the Classroom

Search Engine Result Pages (SERP) include snippets of retrieved resources as a means to help searchers select the ones that satisfy their information needs. This way, result relevance can be determined by scanning through snippets, an exercise that requires experience with reading, understanding, and assessing the value of a document. These are skills that primary school children are still developing and thus are not yet proficient with. As web search tools are essential to support children learning at school and home, we explore how to help young searchers in making informed relevance assessments while conducting searches in a classroom. In this paper, we describe a collaborative design exercise involving primary school children as co-designers: we asked them to examine interfaces with combinations of different emojis to help them assess the usefulness of results in SERP–a crucial factor to determine relevance for the classroom. This activity made our child experts engage with the design exercise while enabling us to collect their judgments so as to get a better sense of the user requirements for this age group. Here we discuss the main design issues emerging from the analysis of children’s preferences, the rationale behind them, comments and concerns raised, and alternative proposals children sketched.
Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey

Abstract The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values.
A template for the arxiv style

Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. The performance of learning-to-rank methods is commonly evaluated using rank-sensitive metrics, such as average precision (AP) and normalized discounted cumulative gain (nDCG). Unfortunately, how to effectively optimize ranksensitive objectives is far from being resolved, which has been an open problem since the dawn of learning-to-rank over a decade ago. In this paper, we introduce a simple yet effective framework for directly optimizing information retrieval (IR) metrics. Specifically, we propose a novel twin-sigmoid function for deriving the exact rank positions of documents during the optimization process instead of using approximated rank positions or relying on the traditional sorting algorithms (e.g., Quicksort [1]). Thanks to this, the rank positions are differentiable, enabling us to reformulate the widely used IR metrics as differentiable ones and directly optimize them based on neural networks. Furthermore, by carrying out an in-depth analysis of the gradients, we pinpoint the potential limitations inherent with direct optimization of IR metrics based on the vanilla sigmoid. To break the limitations, we propose different strategies by explicitly modifying the gradient computation. To validate the effectiveness of the proposed framework for direct optimization of IR metrics, we conduct a series of experiments on the widely used benchmark collection MSLRWEB30K. The experimental results demonstrate that: (1) Direct metric optimization is a more appropriate choice than the commonly used surrogate loss functions, such as ListMLE, ListNet and WassRank. (2) Regarding direct metric optimization, the proposed methods significantly outperform the baseline approach ApproxNDCG. Compared with the state-of-the-art tree-based approach LambdaMART, the proposed methods building upon a simple feed-forward neural network, such as AP-type3 and nDCG-type3, can achieve comparable results.
REXTOR : A System for Generating Relations from Natural Language

This paper argues that a nite-state language model with a ternary expression representation is currently the most practical and suitable bridge between natural language processing and information retrieval. Despite the theoretical computational inadequacies of nitestate grammars, they are very cost effective (in time and space requirements) and adequate for practical purposes. The ternary expressions that we use are not only linguistically-motivated, but also amenable to rapid large-scale indexing. Rextor (Relations EXtracTOR) is an implementation of this model; in one uniform framework, the system provides two separate grammars for extracting arbitrary patterns of text and building ternary expressions from them. These content representational structures serve as the input to our ternary expressions indexer. This approach to natural language information retrieval promises to signi cantly raise the performance of current systems.
Estimating Climate Feedbacks Using a Neural Network

A nonlinear method has been developed to estimate climate feedbacks based on the Neural Network (NN) taking advantage of its self‐learning skills. The NN model developed here is trained using a reanalysis data set and predicts radiation flux globally from atmospheric and surface variables. The radiative feedbacks of temperature, water vapor, surface albedo, and cloud in the interannual climate variations estimated from the NN method are in agreement with those from a broadly used kernel method. However, the NN method demonstrates significant advantages: (1) it withdraws the linearity assumption of the kernel method and accounts for the nonlinear effects of the feedbacks. In the case of large climate perturbations, such as that in the Arctic caused by sea ice melt, the NN method achieves better radiation closure. (2) The method can directly calculate the radiative feedback of cloud and its components. We find that the high, middle, and low cloud feedback components analyzed from the NN method are linearly additive in the interannual climate variations, although there is a considerable nonlinear effect arising from the interactions between cloud and noncloud variables.
Risk Matters: The Real Effects of Volatility Shocks

This paper shows how changes in the volatility of the real interest rate at which small open emerging economies borrow have a quantitatively important effect on real variables like output, consumption, investment, and hours worked. To motivate our investigation, we document the strong evidence of time-varying volatility in the real interest rates faced by a sample of four emerging small open economies: Argentina, Ecuador, Venezuela, and Brazil. We postulate a stochastic volatility process for real interest rates using T-bill rates and country spreads and estimate it with the help of the Particle filter and Bayesian methods. Then, we feed the estimated stochastic volatility process for real interest rates in an otherwise standard small open economy business cycle model. We calibrate eight versions of our model to match basic aggregate observations, two versions for each of the four countries in our sample. We find that an increase in real interest rate volatility triggers a fall in output, consumption, investment, and hours worked, and a notable change in the current account of the economy.
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This paper presents two high-speed interconnect schemes for a pipelined FPGA utilizing a locally synchronized postcharging technique. By avoiding a global synchronized clock, we reduce the power consumption signi(cid:12)cantly. Through postcharging the interconnect and overlapping the postcharging delay with the logic delay, we successfully hide the postcharge time. The long channel devices reduce the area penalty due to delay elements signi(cid:12)cantly. The timing simulation is done using Hspice for a TSMC 0.35 (cid:22)m and area is measured by drawing key elements in MAGIC and using the area model developed in[2]. The postcharge scheme shows a 30% delay reduction over the precharge scheme and up to 310% and 230% delay reductions over the conventional NMOS pass transistor scheme and the tri-state bu(cid:11)er scheme.
Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks

MOTIVATION Elementary flux mode (EFM) is a fundamental concept as well as a useful tool in metabolic pathway analysis. One important role of EFMs is that every flux distribution can be decomposed into a set of EFMs and a number of methods to study flux distributions originated from it. Yet finding such decompositions requires the complete set of EFMs, which is intractable in genome-scale metabolic networks due to combinatorial explosion. RESULTS In this article, we proposed an algorithm to decompose flux distributions into EFMs in genome-scale networks. It is an iterative scheme of a mixed integer linear program. Unlike previous optimization models to find pathways, any feasible solutions can become EFMs in our algorithm. This advantage enables the algorithm to approximate the EFM of largest contribution to an objective reaction in a flux distribution. Our algorithm is able to find EFMs of flux distributions with complex structures, closer to the realistic case in which a cell is subject to various constraints. A case of Escherichia coli growth in the Lysogeny broth (LB) medium containing various carbon sources was studied. Essential metabolites and their syntheses were located. Information on the contribution of each carbon source not obvious from the apparent flux distribution was also revealed. Our work further confirms the utility of finding EFMs by optimization models in genome-scale metabolic networks. CONTACT joshua.chan@connect.polyu. hk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Altering Document Term Vectors for Classification - Ontologies as Expectations of Co-occurrence

Document Classification, the process of classifying documents into pre-defined categories is one of the most popular tasks aimed at grouping and retrieving similar documents. Like many Information Retrieval (IR) tasks, classification techniques rely on using content independent metadata e.g. author, creation date etc. or content dependent metadata i.e. words in the document. One of the most common challenges is that classifiers tend to be inherently limited by the information that is present in the documents. While past research has made use of external dictionaries and topic hierarchies to augment the information that classifiers work with, there is still considerable room for improvement. This work is an investigative effort towards exploring the use of external semantic metadata available in Ontologies in addition to the metadata central to documents, for the task of supervised document classification. The aim is to go beyond 'word co-occurrence / synonym / hierarchical' similarity based classification systems to a one where the semantic relatedness between terms explicated in Ontologies is exploited. We study the effectiveness of our approach on categories in the national security domain trained using documents from the Homeland Security Digital Library. Preliminary evaluations and results indicate that the technique generally improves precision and recall of classifications in some cases and illustrates when the use of Ontologies for such a task does not add significant value.
AIS Electronic

Achieving customer agility is key to organizational performance and market satisfaction. It is an emerging research area where only few studies have been conducted so far. This research-in-progress aims to go beyond the dominating conceptual view on customer agility to provide in-depth understanding on how IT contributes to achieving customer agility in practice. Customer agility is about sensing and responding to customers’ changing demands, patterns and opportunities and the alignment between sensing and responding. The literature review suggests a relationship between customer agility and organizational dynamic capability. The dynamic capability model of Pavlou and El Sawy (2011) is adopted as an initial sensitizing device to aid data collection and analysis. A longitudinal case study has been conducted in a large European bank (EuroBank). In this paper, we present an initial theoretical framework to examine how ICT could be leveraged in achieving customer agility and aligning customer sensing and responding capabilities.
Software process modeling: socio-technical perspectives

In this chapter we describe how the socio-technical systems (STS) approach has been applied to the software process, as well as attempts that have been made to simulate and model the process as a whole. We also outline previous attempts to use socio-technical criteria and guidelines in order to make improvements to the process of constructing software. We first provide a broad outline of the STS approach followed by a number of examples drawn from the areas of COTS-based selection, the People Capability Maturity Model (P-CMM), competency programmes and process simulation. We conclude the chapter with a set of future research issues that are most likely to occupy researchers in the coming years. These issues are drawn partly from the theoretical literature within software engineering, as well as recent developments within industrial practice.
Clouds Sensitivity Analysis Based on Limb Radiance Model

In this paper , we choose SCITRAN 3.2 model simulates Limb radiance. Study on SCIATRAN3.2 model structure and the atmospheric radiation theory, choose clouds as the object of the study. From the geometry of spaceborne Limb scattering observation technique we set radiance simulation environment. On this basis, select the range of the values and develop simulation programs, radiance simulation and make sure the results are correct. According to the simulation results, we analyze the sensitivity of Limb radiance. Results show that tangent height is Limb radiance sensitive factor. Radiance with band and tangent point height varies. The results show that cloud is Limb radiance sensitive factor. Water clouds and ice clouds effect differently, vary with the bands and the tangent point height.
Compacting cuts: A new linear formulation for minimum cut

For a graph (<i>V</i>,<i>E</i>), existing compact linear formulations for the minimum cut problem require Θ(|<i>V</i>||<i>E</i>|) variables and constraints and can be interpreted as a composition of |<i>V</i>| − 1 polyhedra for minimum <i>s</i>-<i>t</i> cuts in much the same way as early approaches to finding globally minimum cuts relied on |<i>V</i>| − 1 calls to a minimum <i>s</i>-<i>t</i> cut algorithm. We present the first formulation to beat this bound, one that uses <i>O</i>(|<i>V</i>|<sup>2</sup>) variables and <i>O</i>(|<i>V</i>|<sup>3</sup>) constraints. An immediate consequence of our result is a compact linear relaxation with <i>O</i>(|<i>V</i>|<sup>2</sup>) constraints and <i>O</i>(|<i>V</i>|<sup>3</sup>) variables for enforcing global connectivity constraints. This relaxation is as strong as standard cut-based relaxations and has applications in solving traveling salesman problems by integer programming as well as finding approximate solutions for survivable network design problems using Jain's iterative rounding method. Another application is a polynomial-time verifiable certificate of size <i>n</i> for for the NP-complete problem of <i>l</i><sub>1</sub>-embeddability of a rational metric on an <i>n</i>-set (as opposed to a certificate of size <i>n</i><sup>2</sup> known previously).
Demystifying Energy Consumption Dynamics in Transiently-powered Computers

SAAD AHMED, Lahore University of Management Sciences (LUMS), Pakistan MUHAMMAD NAWAZ, Lahore University of Management Sciences (LUMS), Pakistan ABU BAKAR, Lahore University of Management Sciences (LUMS), Pakistan NAVEED ANWAR BHATTI, Air University, Pakistan MUHAMMAD HAMAD ALIZAI, Lahore University of Management Sciences (LUMS), Pakistan JUNAID HAROON SIDDIQUI, Lahore University of Management Sciences (LUMS), Pakistan LUCA MOTTOLA, Politecnico di Milano, Italy and RI.Se SICS, Sweden
The Use of MMR and Diversity-Based Reranking for Reodering Documents and Producing Summaries

This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary re-sults indicate some bene(cid:12)ts for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection.
Socio-Economic Characteristics of Female Population in Usilampatti Taluk, Madurai District Using Multivariate Analysis and Structural Equation Model

The female population characteristic of any area or region is a significant reflection of physical, economic, social justice conditions and availability of the resources. Women’s status is often described in terms of their level of education, employment, income, and health as well as their roles within the family and the society. In view of this chapter has been analyzed using systematic socio spatial analysis by means of a structural eduation modeling and factor analysis to extract various aspects of the female population characteristic in the Usilampatti Taluk.
Reinforcement Learning in Newcomblike Environments

Newcomblike decision problems have been studied extensively in the decision theory literature, but they have so far been largely absent in the reinforcement learning literature. In this paper we study value-based reinforcement learning algorithms in the Newcomblike setting, and answer some of the fundamental theoretical questions about the behaviour of such algorithms in these environments. We show that a value-based reinforcement learning agent cannot converge to a policy that is not ratiﬁable , i.e., does not only choose actions that are optimal given that policy. This gives us a powerful tool for reasoning about the limit behaviour of agents – for example, it lets us show that there are Newcomblike environments in which a reinforcement learning agent cannot converge to any optimal policy. We show that a ratiﬁable policy always exists in our setting, but that there are cases in which a reinforcement learning agent normally cannot converge to it (and hence cannot converge at all). We also prove several results about the possible limit behaviours of agents in cases where they do not converge to any policy.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

In this chapter, we highlight relevant characteristics of BCI software, discuss the importance of these characteristics in different contexts of BCI research, and document the important impact of BCI software on productivity. The central purpose of BCI software is to facilitate implementation, verification, and dissemination of a wide range of BCI approaches. To do this, BCI software needs to satisfy complex technical demands across a wide array of scientific, clinical, and commercial investigations. BCI software can be implemented either from scratch, using low-or high-level programming environments and toolboxes, or on top of pre-existing BCI software platforms. We assign contemporary BCI software into these categories and investigate their impact on the field of BCI research. Our results demonstrate that only BCI2000 and OpenViBE have enjoyed sustained development and widespread dissemination and have had a strong impact on productivity of the whole field of BCI research.
The second wave of SARS-CoV-2 infections and COVID-19 deaths in Germany – driven by values, social status and migration background? A county-scale explainable machine learning approach

There is a general consensus that SARS-CoV-2 infections and COVID-19 deaths have hit lower social groups the hardest, however, for Germany individual level information on socioeconomic characteristics of infections and deaths does not exist. The aim of this study was to identify the key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. We considered information on COVID-19 diagnoses and deaths from 1. October to 15. December 2020 on the county-level, differentiating five two-week time periods. We used 155 indicators to characterize counties in nine geographic, social, demographic, and health domains. For each period, we calculated directly age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates with the 155 characteristics of the counties for each period. To explore the importance and the direction of the correlation of the regional indicators we used the SHAP procedure. We categorized the top 20 associations identified by the Shapley values into twelve categories depicting the correlation between the feature and the outcome. We found that counties with low SES were important drivers in the second wave, as were those with high international migration and a high proportion of foreigners and a large nursing home population. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. We concluded that risky working conditions with reduced opportunities for social distancing and a high chronic disease burden put populations in low-SES counties at higher risk of SARS-All
Tracking the Pixels: Detecting Unknown Web Trackers via Analysing Invisible Pixels

Web tracking has been extensively studied over the last decade. To detect tracking, previous studies and user tools rely on filter lists. However, there has always been a suspicion that lists miss many trackers. In this paper, we propose an alternative method to detect trackers inspired by analyzing behavior of invisible pixels. By crawling 84,658 webpages from 8,744 domains, we detect that third-party invisible pixels are widely deployed: they are present on more than 94.51% of domains and constitute 35.66% of all third-party images. We propose a fine-grained behavioral classification of tracking based on the analysis of invisible pixels. We use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 4, 216, 454 third-party requests. We demonstrate that two popular methods to detect tracking, based on EasyList&EasyPrivacy and on Disconnect lists respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists, 379, 245 requests originated from 8,744 domains still track users on 68.70% of websites.