Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability Guidelines for Preference-Based Product Recommenders

In this chapter, we survey the state of the art and present successful methods for preference-based product recommenders in three important facets of user interaction: preference elicitation, preference revision, and search result presentation. Each method was carefully selected based on an evaluation framework we call ACE. It relies on three criteria: how a system increases users’ decision accuracy (A) and their confidence (C) in choosing items, and the amount of interaction effort (E) required of them. After synthesizing and presenting the main results for each facet, we develop a set of best-practice design guidelines applicable in general to recommenders used today. The chapter concludes with a theoretical model to clarify the role of the guidelines in the ACE evaluation and thus their application in the interaction process. The ACE evaluation framework presented here is the first in the field to validate the performances of preference-based recommenders based on a set of user-centric criteria.
Optimal sizing and positioning of grid integrated distributed generator: a review

The ever-growing population and globalization have resulted into an increase in demand for power. Additionally, ecological concerns like an unnatural weather change have constrained us to locate new and different substitutes to the current conventional sources of energy. To provide a solution to this crisis of energy in a way that is environment friendly, we have focused our attention towards making renewable energy resources as an alternative to currently employed sources of power. This paper contains extensive study on different Distributed Generation (DG) sources, comparing their merits and demerits also comprising of the impact when added to an already existing AC power grid. Accordingly, the planning methodologies have been discussed which will help us in determining which type of DG suits best to our requirement. This paper also contains the review of optimal placement techniques and sizing methodologies for DG system in grid networks, which are together known as Hybrid Energy Systems (HES).
MOZART: Design and Deployment of Advanced IoT Applications

The Internet of Things (IoT) aims at sensing and altering our surrounding environment through connected objects to improve everyday life. IoT applications are built using interconnected objects with a goal to provide added-value services. However, there are still challenges in providing a secure, robust and easy-to-use end-user platform for development of such applications. In this paper, we present a end-user tool for supporting the design and deployment of smart home IoT applications. The tool first provides a graphical user interface to specify an IoT application using a rule-based composition language. Automated analysis techniques can then be called for verifying that the designed application is correct (e.g., free of deadlocks). Finally, the tool provides a rule execution engine to support application deployment. The tool is built by implementing a set of components on top of Mozilla WebThings platform, which is a concrete implementation of W3C’s Web of Things specification.
Extraction of an Explanatory Graph to Interpret a CNN

This paper introduces an explanatory graph representation to reveal object parts encoded inside convolutional layers of a CNN. Given a pre-trained CNN, each filter1 in a conv-layer usually represents a mixture of object parts. We develop a simple yet effective method to learn an explanatory graph, which automatically disentangles object parts from each filter without any part annotations. Specifically, given the feature map of a filter, we mine neural activations from the feature map, which correspond to different object parts. The explanatory graph is constructed to organize each mined part as a graph node. Each edge connects two nodes, whose corresponding object parts usually co-activate and keep a stable spatial relationship. Experiments show that each graph node consistently represented the same object part through different images, which boosted the transferability of CNN features. The explanatory graph transferred features of object parts to the task of part localization, and our method significantly outperformed other approaches.
HOOP: Efficient Hardware-Assisted Out-of-Place Update for Non-Volatile Memory

Byte-addressable non-volatile memory (NVM) is a promising technology that provides near-DRAM performance with scalable memory capacity. However, it requires atomic data durability to ensure memory persistency. Therefore, many techniques, including logging and shadow paging, have been proposed. However, most of them either introduce extra write traffic to NVM or suffer from significant performance overhead on the critical path of program execution, or even both.In this paper, we propose a transparent and efficient hardware-assisted out-of-place update (HOOP) mechanism that supports atomic data durability, without incurring much extra writes and performance overhead. The key idea is to write the updated data to a new place in NVM, while retaining the old data until the updated data becomes durable. To support this, we develop a lightweight indirection layer in the memory controller to enable efficient address translation and adaptive garbage collection for NVM. We evaluate HOOP with a variety of popular data structures and data-intensive applications, including key-value stores and databases. Our evaluation shows that HOOP achieves low critical-path 1atency with small write amplification, which is close to that of a native system without persistence support. Compared with state-of-the-art crash-consistency techniques, it improves application performance by up to $ 1.7\times$, while reducing the write amplification by up to $ 2.1\times$. HOOP also demonstrates scalable data recovery capability on multi-core systems.
Only Time Will Tell: Credible Dynamic Signaling

This paper explores a model of dynamic signaling without commitment. It is known that separating equilibria do not exist if the sender cannot commit to future costly actions, since no single action can have enough weight to be an effective signal. This paper, however, shows that informative and payoff-relevant signaling can occur even without commitment and without resorting to unreasonable off-path beliefs. Such signaling can only happen through attrition, when the weakest type mixes between revealing own type and pooling with the stronger types. The possibility of full information revelation in the limit hence depends crucially on the assumptions about the state space. We illustrate the results by exploring a model of dynamic price signaling and show that prices may be informative of product quality even if the seller cannot commit to future prices, with both high and low prices being able to signal high quality.
Effects of power-law correlated disorders in a many-body localized XXZ spin chain

Effects of correlations in disorders in MBL systems have not been clarified so far. We study effects of power-law-correlated disorders in a typical manybody-localized (MBL) system: s = 1 2 spin chain in a random magnetic field. We numerically investigate the localization properties of the system rather in detail. The power-law-correlated disorders induce localization properties essentially different from those of a uniform white-noise disorder, as we show for Anderson localization as well as MBL. Notably, we find that the powerlaw-correlated disorders tend to enhance a critical phase. In addition, we also systematically investigate entanglement properties.
Cross Display Mouse Movement in MDEs

Multi-display environments are becoming more common in both homes and offices. Movement between displays is common in these settings and we should strive to optimize the action. The stitching technique causes the cursor to jump form one display to another, instantly, regardless of the displayless space between displays. In this paper we examine three techniques that attempt to provide benefits for the standard stitching technique used in most operating systems today. Two of the techniques apply a pseudo-haptic effect to the cursor, slowing the movement, when moving from one display to another. The last technique applies a visual cue to the cursor when crossing from one display to another. Each of these techniques is examined for different distances between displays. We carried out an experiment attempting to discover which technique is optimal for cross-display movement. We found that the visual cue technique was equivalent to stitching at all distance for task completion time, but both pseudo-haptic effects were significally slower. We also found that at medium and larger distances between displays all three techniques were significant in their reduction of target overshoot. Target overshoot occurs when the cursor is moved beyond the desired target location. These finding suggest that there is a practical advantage for providing a visual cue to users when passing from one display to another.
Intelligibility Enhancement of Cleft Lip and Palate Speech

Cleft lip and palate (CLP) is a craniofacial condition which leads to various speech-related disorders. CLP interferes with the speech intelligibility resulting in communication impairments. Even after surgical repair, the speech disorders persists due to the associated velopharyngeal dysfunction (VPD) or oronasal fistula [1, 2, 4]. The speech-related disorders are broadly categorized into hypernasality, hyponasality, nasal air emission, consonant production errors, and voice disorders [3]. Clinically, the main concern is to enhance the intelligibility of CLP speech. Improvement of CLP speech intelligibility is necessary to help the CLP speakers communicate effectively.
Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
Life-Cycle Consumption and Wealth Paths at Older Ages

Life-Cycle Consumption and Wealth Paths at Older Ages Michael Hurd RAND and NBER Susann Rohwedder RAND According to the simplest version of the life-cycle model, consumption should decline in old age: increasing mortality risk increases the effective discount rate which will increase the attractiveness of consumption today relative to consumption in the future. Yet, we have not had good panel data on consumption with which to test this fundamental prediction. Rather we have had to rely on partial measures of consumption or on wealth change. Partial consumption is only valid if the composition of consumption does not change with age or with the level of consumption. Panel wealth is subject to considerable measurement error and to unanticipated capital gains and losses, both of which can obscure any life-cycle effects. This paper is based on two waves of the Consumption and Activities Mail Survey, which has complete measures of spending by a random sample of 5000 Health and Retirement Study households in 2001 and 2003 and up to seven waves of the HRS. We estimate life-cycle consumption paths of single people in their late 60s and beyond, and compare the slopes with predictions from theoretical models of life-cycle consumption. We compare evidence about life-cycle models based on consumption with evidence based on wealth change calculated over the same households over the same time period. For couples we report consumption and wealth change and compare these changes with predictions from a previously estimated model.
Relevance of Soils for Gross National Happiness THOMAS CASPARI Introduction “Think how difficult life would be without soil to grow food crops. ” This sentence from a Bhutanese geography schoolbook for class VII students

“Think how difficult life would be without soil to grow food crops. ” This sentence from a Bhutanese geography schoolbook for class VII students (RGoB 1994) may sound trivial in our ears. But sometimes I believe that it is useful to remind ourselves of the very basic things in life. Each of us has seen soil (at least its surface), smelled and touched it and very physically used it for planting our food. Its presence is so obvious and yet its fertility so essential for all agricultural activities as the main source of our livelihood. The four Buddhist means to avoid dissatisfaction (food, shelter, clothing, medicine) are directly or indirectly related to it. However, soil is not only part of our household (economy), but also integral part of nature’s household (ecology). Therefore, e. g. talking about soil means looking at environmental conservation as well as agricultural production. And where the spheres of Man and Nature meet, the domains of spirits and local deities are located. Being aware of these few aspects, it may not surprise that there are multiple points of contact between the down-to-earth subject of soil and the high-minded goal of Gross National Happiness (Table 1):
Bridging the gap between stochastic methods and deep learning

Subject Many complex real-world phenomena can be described through probabilistic models that characterize available data, and possibly relate them to other unknown quantities of interest. Fields where such systems can be found include environmental science, biology, econometrics, astronomy, among many others. Unfortunately, for most probabilistic models of practical interest, exact inference is intractable, and so we have to resort to some form of approximation. Monte Carlo methods are stochastic methods that allow to approximate distributions with a set of random samples [1]. Then, any moments or conﬁdence region with respect to this distribution could be empirically approximated by a discrete sum over these generated samples. Unfortunately, in most cases, sampling directly from the desired distribution is not possible due to its complex nature (multi-modality, high-dimensionality,etc.) or since this distribution is only known up to a normalizing constant (e.g. in Bayesian inference). This has led to the development in recent years of much more advanced algorithms which allow one to obtain the required samples from this target distribution by using either (a) the Markov Chain Monte Carlo (MCMC) methods, which generate a Markov chain whose stationary distribution is the target distribution, or (b) importance sampling (IS) algorithms, where samples are generated from simple proposal densities and are then properly weighted. Despite the existence of theoretical guarantees, convergence speed of such techniques strongly depends on choosing an appropriate proposal distribution, which in practice is quite challenging in practice especially in high-dimensional spaces [2]. On
On Public Crowdsource-Based Mechanisms for a Decentralized Blockchain Oracle

Blockchain technology has created an excitement that was last seen two decades ago when the Internet was entering the mainstream. An appealing feature of blockchain technology is smart contracts. A smart contract is an executable code. It runs on top of the blockchain facilitating an agreement between untrusted parties. These smart contracts have a major limitation, namely they cannot operate on information external to the blockchain. The inability to query such information has paved the need for trusted entities called “oracles. ” These oracles attest to facts without the robust security guarantees that blockchains generally provide. This can potentially harm the integrity of the network and lead to centralized points-of-failure. To address this concern, this article proposes a decentralized oracle which is based on a voting-based game that decides the truth or falsity of queries. In the context of this article, we are only interested in <italic>binary markets</italic>, i.e., queries which can be <inline-formula><tex-math notation="LaTeX">$\text{True}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{False}$</tex-math></inline-formula>, or <inline-formula><tex-math notation="LaTeX">$\text{Unknown}$</tex-math></inline-formula>. When requesting facts from an oracle, a user submits binary queries. Reporters (or certifiers) respond to the queries by placing monetary stake. A formal analysis of the system parameters is presented, which shows that the proposed platform incentivizes a Nash equilibrium for truthful reporting. An extension to the base protocol is also described and profiled against the original framework. Finally, we discuss a prototype architecture, along with additional features to be considered during implementation.
Short communication Measurement of plumage badges: an evaluation of methods used in the Great Tit Parus major

Figure 1. Photography of Great Tit ready for analysis. The tip of the scissors marks the position of the clavicle, and number eight indicates the position of the posterior end of the sternum. There is an increasing interest in the variation in plumage coloration and a number of theories have been proposed to explain its evolution (Butcher & Rohwer 1989, Savalli 1995). Birds have been considered to signal through plumage their dominance status, ability to evade predators, parasite resistance or for example their parental care abilities (reviewed in Butcher & Rohwer 1989, Andersson 1994, Savalli 1995, Johnstone 1997). However, the measurement of plumage badges and its accuracy have received little attention (Savalli 1995). In the case of the Great Tit Parus major, for instance, several studies have analysed the importance of the breast stripe size as an indicator of dominance status in winter flocks (Järvi & Bakken 1984, Pöysa 1988, Lemel 1989, Wilson 1992) and its importance in mate choice (Norris 1990, 1993), but the badge has been estimated using very different indices, even in studies performed by the same authors (Norris 1990, 1993). We compare the performance of different indices in estimating Great Tit stripe size, by comparing them with badge surface area obtained from image analysis of digital photographs. We propose that digital photography provides a reliable and inexpensive method to measure plumage badges in birds and we demonstrate its performance in the Great Tit.
Robust estimation of breeding values in a random regression test‐day model

Summary Two robust estimation procedures were applied to a random regression test-day model to reduce the effects of test-day records that are generally labelled as outliers. One robust procedure consisted of estimating residuals (actual observation minus predicted) from the genetic evaluation model, computing the standard deviation of residuals across all records, and restricting the outlier residuals to be within k standard deviations. Thus, a new observation is created for use in the genetic evaluation model. The process is part of the iterations on data to obtain solutions to mixed model equations until no more outliers beyond k standard deviations exist. Four different values of k were examined. The second robust procedure utilizes different weights with each test-day record. Weights are estimated from the residuals for all observations. Outliers tend to receive smaller weights, and thus, their influence tends to be reduced. In this study, eleven different weight formulas were compared. The objectives were to apply the robust procedures to test-day milk yield records of Canadian Jersey cattle and to determine the effects on estimated breeding values (EBVs) and rankings of animals. Results were compared to usual best linear unbiased prediction (BLUP) ignoring the outlier problem. Zusammenfassung Zwei robuste Schatzverfahren wurden in einem zufalligen Regressions-Testtagsmodell angewandt, um die Effekte der Testtags-Datensatze zu vermindern, die im allgemeinen als Ausreiser gelten. Ein robustes Verfahren besteht aus der Schatzung von Residualeffekten (aktueller Beobachtungswert minus erwarteter) des genetischen Auswertungsmodells, dem Berechnen der Standardabweichung der Residuen uber alle Datensatze hinweg und Einschrankung der Ausreiser-Residuen auf solche innerhalb k Standardabweichungen. Somit ist eine neue Beobachtung zum Gebrauch in genetischen Auswertungsmodellen kreiert. Der Prozess ist Teil der Iterationen uber die Daten, um Losungen des gemischten Gleichungssystems zu erhalten, bis keine Ausreiser jenseits von k Standardabweichungen mehr existieren. Vier verschiedene Werte fur k wurden untersucht. Das zweite robuste Verfahren benutzt verschiedene Gewichtungen mit jedem Testtags-Datensatz. Die Gewichtungen werden geschatzt aus den Residualwerten fur alle Beobachtungen. Ausreiser neigen dazu, geringere Gewichtungen zu erhalten, und daher nimmt ihr Einfluss tendenziell ab. In der vorliegenden Arbeit wurden elf verschiedene Formeln zur Berechnung der Gewichtungsfaktoren verglichen. Die Ziele waren die Anwendung der robusten Verfahren auf Testtags-Gemelke kanadischer Jersey-Rinder und deren Effekte auf geschatzte Zuchtwerte und die Rangierung der Tiere. Die Ergebnisse wurden verglichen mit der gewohnlichen BLUP-Zuchtwertschatzung, wobei das Ausreiserproblem ignoriert wurde.
Loki: Streamlining Integration and Enrichment

Data scientists frequently transform data from one form to another while cleaning, integrating, and enriching datasets. Writing such transformations, or “mapping functions" is time-consuming and often involves significant code re-use. Unfortunately, when every dataset is slightly different from the last, finding the right mapping functions to re-use can be just as difficult as starting from scratch. In this paper
Visual Signals of Individual Quality in a European Solitary Founding Paper Wasp

Conventional signals are maintained via social costs and commonly used in the animal kingdom to assess conspecifics' agonistic ability during disputes over resources. In the last decade, some experimental studies reported the existence of visual conventional signals in several social wasp species, being good rank predictors in different social contexts. Females of the social wasp Polistes gallicus do not cooperate to start nests but they often try to usurp conspecific nests. Here, we showed that the reproductive females of this species have variable facial colour patterns that function as conventional signals. Wasps with larger black spots on their clypeus are more likely to successfully overwinter, are larger, and are better at fighting and at holding a nest. Furthermore, in field experiments, resident foundresses rely on facial pattern to assess usurpers' fighting abilities, modulating their defence reaction accordingly, so that rivals with larger black spot receive more aggression than rivals with smaller or no black spots on the clypeus. Our study reveals that visual recognition abilities are widespread among paper wasps that, regardless of their social biology, face similar selective pressures within competitive contexts.