Miracle-dz/UniFAR-FTUs huggingface dataset:
Using and extending itemsets in data mining : query approximation, dense itemsets, and tiles

Frequent itemsets are one of the best known concepts in data mining, and there is active research in itemset mining algorithms. An itemset is frequent in a database if its items co-occur in sufficiently many records. This thesis addresses two questions related to frequent itemsets. The first question is raised by a method for approximating logical queries by an inclusion-exclusion sum truncated to the terms corresponding to the frequent itemsets: how good are the approximations thereby obtained? The answer is twofold: in theory, the worst-case bound for the algorithm is very large, and a construction is given that shows the bound to be tight; but in practice, the approximations tend to be much closer to the correct answer than in the worst case. While some other algorithms based on frequent itemsets yield even better approximations, they are not as widely applicable. The second question concerns extending the definition of frequent itemsets to relax the requirement of perfect co-occurrence: highly correlated items may form an interesting set, even if they never co-occur in a single record. The problem is to formalize this idea in a way that still admits efficient mining algorithms. Two different approaches are used. First, dense itemsets are defined in a manner similar to the usual frequent itemsets and can be found using a modification of the original itemset mining algorithm. Second, tiles are defined in a different way so as to form a model for the whole data, unlike frequent and dense itemsets. A heuristic algorithm based on spectral properties of the data is given and some of its properties are explored.
A Proposal for Scalable Internet Routing & Addressing

Our measurement studies of the global Internet routing system show that prefix de-aggregation has led to the DFZ routing table size growing at a rate which is much faster than the Internet itself. The main causes of prefix de-aggregation include user site multihoming and traffic engineering. We propose to move Internet service providers to a separate address space as an effective solution to the routing scalability problem. We discuss different means to provide the mapping service between user and provider address spaces and the pros and cons of each approach, as well as why we believe such an architectural change is necessary to solve the routing scalability problem.
Does the traditional thesaurus have a place in modern information retrieval

: The introduction (1.0) of this article considers the status of the thesaurus within LIS and asks about the future prospect for thesauri. The main following points are: (2.0) Any knowledge organization system (KOS) is today threatened by Google-like systems, and it is therefore important to consider if there still is a need for knowledge organization (KO) in the traditional sense. (3.0) A thesaurus is a somewhat reduced form of KOS compared to, for example, an ontology, and its “bundling” and restricted number of semantic relations has never been justified theoretically or empirically. Which semantic relations are most fruitful for a given task is thus an open question, and different domains may need different kinds of KOS including different sets of relations between terms. (4.0) A KOS is a controlled vocabulary (CV) and should not be considered a “perfect language” (Eco 1995) that is simply able to remove the ambiguity of natural language; rather much ambiguity in language represents a battle between many “voices” (Bakhtin 1981) or “paradigms” (Kuhn 1962). In this perspective, a specific KOS, e.g. a specific thesaurus, is just one “voice” among many voices, and that voice has to demonstrate its authority and utility. It is concluded (5.0) that the traditional thesaurus does not have a place in modern information retrieval, but that more flexible semantic tools based on proper studies of domains will always be important.
An Secure Hierarchical Key Agreement Scheme for Wireless Sensor Networks

In Wireless Sensor Networks (WSN), a key agreement scheme is an essential task for secure communications. Recently, Lee and Kim proposed a hierarchical key agreement scheme for the fresh key establishment in WSN. This scheme achieves a secure session key agreement. In this paper, we analyze the security of the scheme and illustrate that their scheme is unconfident against the insider attack in practice. Moreover, it is also difficult to resist the replay attack in this scheme. Then, we proposed a novel scheme inspired by their scheme. The entities of our scheme include a Sink besides the sensor nodes, which interrupts the construction of the insider attack. Our scheme adopts the timestamp mechanism to resist replay attack, which could decrease the storing requirement of the sensor nodes. Thus, it is more practical and realistic. We illustrate that our proposal can provide stronger security than Lee and Kim’s scheme.
The one clean qubit model without entanglement is classically simulable

Entanglement has been shown to be necessary for pure state quantum computation to have an advantage over classical computation. However, it remains open whether entanglement is necessary for quantum computers that use mixed states to also have an advantage. The one clean qubit model is a form of quantum computer in which the input is the maximally mixed state plus one pure qubit. Previous work has shown that there is a limited amount of entanglement present in these computations, despite the fact that they can efficiently solve some problems that are seemingly hard to solve classically. This casts doubt on the notion that entanglement is necessary for quantum speedups. In this work we show that entanglement is indeed crucial for efficient computation in this model, because without it the one clean qubit model is efficiently classically simulable.
Resonant assisted annihilation

Assisted annihilation is a novel mechanism to generate viable sub-GeV thermal dark matter, where a pair of stable dark matter annihilates with an assister to Standard Model states. Typically such 3 → 2 annihilation topologies are flux suppressed compared to 2 → 2 processes. In this paper, we explore the possibility of a resonant 3 → 2 assisted annihilation dominantly driving the freeze-out of dark matter. We demonstrate that in a simple multipartite scalar extension of the Standard Model this can be realized in certain regions of parameter space to provide viable dark matter relic density, in agreement with observation. We demonstrate that for photophilic assisters parts of the parameter space are already constrained by indirect detection experiments and the measurements of CMB anisotropies while substantial regions remain beyond the present limit.
Accurate IMU Preintegration Using Switched Linear Systems For Autonomous Systems

Employing an inertial measurement unit (IMU) as an additional sensor can dramatically improve both reliability and accuracy of visual/Lidar odometry (VO/LO). Different IMU integration models are introduced using different assumptions on the linear acceleration from the IMU. In this paper, a novel IMU integration model is proposed by using switched linear systems. The proposed approach assumes that both the linear acceleration and the angular velocity in the body frame are constant between two consecutive IMU measurements. This is more realistic in real world situation compared to existing approaches which assume that linear acceleration is constant in the world frame while angular velocity is constant in the body frame between two successive IMU measurements. Experimental results show that the proposed approach outperforms the state-of-the-art IMU integration model. The proposed model is thus important for localization of high speed autonomous vehicles in GPS denied environments.
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families

We consider the problem of computing the maximum likelihood multivariate log-concave distribution for a set of points. Specifically, we present an algorithm which, given $n$ points in $\mathbb{R}^d$ and an accuracy parameter $\epsilon>0$, runs in time $poly(n,d,1/\epsilon),$ and returns a log-concave distribution which, with high probability, has the property that the likelihood of the $n$ points under the returned distribution is at most an additive $\epsilon$ less than the maximum likelihood that could be achieved via any log-concave distribution. This is the first computationally efficient (polynomial time) algorithm for this fundamental and practically important task. Our algorithm rests on a novel connection with exponential families: the maximum likelihood log-concave distribution belongs to a class of structured distributions which, while not an exponential family, "locally" possesses key properties of exponential families. This connection then allows the problem of computing the log-concave maximum likelihood distribution to be formulated as a convex optimization problem, and solved via an approximate first-order method. Efficiently approximating the (sub) gradients of the objective function of this optimization problem is quite delicate, and is the main technical challenge in this work.
A Scalar Version of the Caflisch‐Luke Paradox

Consider an infinite cloud of hard spheres sedimenting in a Stokes flow in the whole space ℝd. Despite many contributions in fluid mechanics and applied mathematics, there is so far no rigorous definition of the associated effective sedimentation velocity. Calculations by Caflisch and Luke in dimension d = 3 suggest that the effective velocity is well‐defined for hard spheres distributed according to a weakly correlated and dilute point process, and that the variance of the sedimentation speed is infinite. This constitutes the Caflisch‐Luke paradox. In this contribution, we consider a scalar version of this problem that displays the same difficulties in terms of interaction between the differential operator and the randomness, but is simpler in terms of PDE analysis. For a class of hardcore point processes we rigorously prove that the effective velocity is well‐defined in dimensions d > 2 and that the variance is finite in dimensions d > 4, confirming the formal calculations by Caflisch and Luke, and opening a way to the systematic study of such problems . © 2020 Wiley Periodicals LLC
Interventional Effect Models for Multiple Mediators

In settings that involve multiple mediators, approaches focusing on fine-grained decompositions of natural (in)direct effects are only valid under strong assumptions. In particular, the assumptions are known to be violated when - as often - the structural dependence between the multiple mediators is unknown. In contrast, interventional (in)direct effects, introduced by VanderWeele et al. (2014), can be identified under much weaker conditions than natural (in)direct effects, but have the drawback of not adding up to the total effect. Vansteelandt and Daniel (2017) adapted their proposal to achieve an exact decomposition of the total effect, and generalized the interventional effects to the multiple mediator setting. In this article, we introduce interventional effect models that allow for simultaneous and parsimonious modeling of the interventional effects when there are multiple mediators. The parameters in the effect models encode the effects of a treatment on an outcome that are mediated by distinct mediators, even when the directions of the causal effects between the mediators are unknown, or when the mediators share hidden common causes. The mediators and outcome can be continuous or noncontinuous. Estimation proceeds via Monte Carlo integration and only requires specifying a joint distribution of the mediators and an outcome model.
Design of an antenna with reconfigurable band rejection for UWB cognitive radio

Ultra-wideband cognitive radio (UWB-CR) features the use of UWB transmission as the enabling technology for cognitive radio, in both the underlay and overlay spectrum sharing approaches. In this paper, we propose an antenna that is suitable for underlay and overlay UWB cognitive radio. The antenna can be operated as ultrawideband, for underlay CR and for sensing in the overlay CR, and is reconflgurable in terms of the ability to selectively have a band stop in the WiMAX and WLAN bands, to prevent interference to primary users operating in these bands, when used in the overlay CR mode.
Scalar fields and the FLRW singularity

The dynamics of multiple scalar fields on a flat FLRW spacetime can be described entirely as a relational system in terms of the matter alone. The matter dynamics is an autonomous system from which the geometrical dynamics can be inferred, and this autonomous system remains deterministic at the point corresponding to the singularity of the cosmology. We show the continuation of this system corresponds to a parity inversion at the singularity, and that the singularity itself is a surface on which the space-time manifold becomes non-orientable.
Four Small Planets Buried in K2 Systems: What Can We Learn for TESS?

The Kepler, K2, and Transiting Exoplanet Survey Satellite (TESS) missions have provided a wealth of confirmed exoplanets, benefiting from a huge effort from the planet-hunting and follow-up community. With careful systematics mitigation, these missions provide precise photometric time series, which enable detection of transiting exoplanet signals. However, exoplanet hunting can be confounded by several factors, including instrumental noise, search biases, and host star variability. In this Letter, we discuss strategies to overcome these challenges using newly emerging techniques and tools. We demonstrate the power of new, fast open-source community tools (e.g., lightkurve, starry, celerite, exoplanet), and discuss four high signal-to-noise ratio (S/N) exoplanets that showcase specific challenges present in planet detection: K2-43c, K2-168c, K2-198c, and K2-198d. These planets have been undetected in several large K2 planet searches, despite having transit signals with S/N ≥ 10. Two of the planets discussed here are new discoveries. In this work we confirm all four as true planets. Alongside these planet systems, we discuss three key challenges in finding small transiting exoplanets. The aim of this Letter is to help new researchers understand where planet detection efficiency gains can be made, and to encourage the continued use of K2 archive data. The considerations presented in this Letter are equally applicable to Kepler, K2, and TESS, and the tools discussed here are available for the community to apply to improve exoplanet discovery and fitting.
Exploring sentence informativeness

This study is a preliminary exploration of the concept of informativeness –how much information a sentence gives about a word it contains– and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers’ predictions to train word embeddings has an impact on embedding quality.
Modeling Interface Charge Traps in Junctionless FETs, Including Temperature Effects

In this article, an analytical predictive model of interface charge traps in symmetric, long-channel double-gate, junctionless transistors (JLTs) is proposed based on a charge-based model. Interface charge traps arising from exposure to chemicals, high-energy ionizing radiation, or aging mechanism could degrade the charge–voltage characteristics. The model is predictive in a range of temperatures from 77 to 400 K. The validity of the approach is confirmed by extensive comparisons with numerical technology computer-aided design (TCAD) simulations in all regions of operation from deep depletion to accumulation and from linear to saturation.
On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners

Background: Developers spend a significant amount of time and effort to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, and efficiency. The current models are not capable of meeting these criteria, making it harder to adopt these models in practice. Recently, deep-learning-based bug localization models have been proposed in the literature. They show a better performance than the state-of-the-art models. Aim: In this research, we would like to investigate whether deep learning models meet the expectations of practitioners or not. Method: We constructed a Convolution Neural Network and a Simple Logistic model to examine their effectiveness in localizing bugs. We train these models on five open source projects written in Java and compare their performance with the performance of other state-of-the-art models trained on these datasets. Results: Our experiments show that although the deep learning models perform better than classic machine learning models, they meet the adoption criteria set by the practitioners only partially. Conclusions: This work provides evidence that the practitioners should be cautious while using the current state of the art models for production-level use-cases. It also highlights the need for standardization of performance benchmarks to ensure that bug localization models are assessed equitably and realistically.
An approximate version of Jackson’s conjecture

Abstract A diregular bipartite tournament is a balanced complete bipartite graph whose edges are oriented so that every vertex has the same in- and out-degree. In 1981 Jackson showed that a diregular bipartite tournament contains a Hamilton cycle, and conjectured that in fact its edge set can be partitioned into Hamilton cycles. We prove an approximate version of this conjecture: for every ε > 0 there exists n0 such that every diregular bipartite tournament on 2n ≥ n0 vertices contains a collection of (1/2–ε)n cycles of length at least (2–ε)n. Increasing the degree by a small proportion allows us to prove the existence of many Hamilton cycles: for every c > 1/2 and ε > 0 there exists n0 such that every cn-regular bipartite digraph on 2n ≥ n0 vertices contains (1−ε)cn edge-disjoint Hamilton cycles.
Subtext of Decisions: Literacy Practices in the Context of Coding

In this dissertation I present findings from a qualitative case study of five early adolescents engaged in an online programming community. As a researcher, I was interested in how early adolescents designed digital media as they learned how to code within an online programming community known as Scratch. My research was guided by two questions: (1) What are the literacy practices and processes embedded in the design and collaboration of products created within an online programming community? (2) In what ways do participants make decisions in the design of their projects created in Scratch? The data collected for this descriptive case study included participant created digital media products, interviews, observations, and online community artifacts. Based upon a content analysis of the digital media products and an inductive analysis of the interviews, observations, and community artifacts data, I determined participants demonstrated decisions connected to the design of projects created, decisions focused on the function of projects, and decisions connected with meaning. I created a typography to represent the decisions made by participants as they created projects in Scratch. Additionally, participants expressed a sense of accomplishment and expertise in Scratch product development. Findings from this research provide a nuanced understanding of the literacy practices and processes enacted by early adolescents as they create digital media in an online programming community via the use of coding.
Incremental random forest classifiers in spark

The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely rebuilding the forests. I implement these two strategies-incrementally growing existing trees and replacing old trees-in Spark Machine Learning(ML), a commonly used library for running ML algorithms in Spark. My implementation draws from existing methods in online learning literature, but includes several novel refinements. I evaluate the two implementations, as well as a variety of hybrid strategies, by recording their error rates and training times on four different datasets. My benchmarks show that the optimal strategy for incremental growth depends on the batch size and the presence of concept drift in a data workload. I find that workloads with large batches should be classified using a strategy that favors tree regrowth, while workloads with small batches should be classified using a strategy that favors incremental growth of existing trees. Overall, the system demonstrates significant efficiency gains when compared to the standard method of regrowing the random forest. Thesis Supervisor: Samuel Madden Title: Professor, EECS
A Review of Studies on Web Accessibility Abroad

Web accessibility is necessary for the disabilities and the elderly to obtain information and study via Internet.The research on web accessibility is a hot topic in web design,web development,web evaluation and web maintenance internationally.This article introduces definition of web accessibility,foreign laws about web accessibility,and design and evaluation standards of web accessibility in order to make people pay attention to it in web design and development in our country.
Shape-based object extraction in high-resolution remote-sensing images using deep Boltzmann machine

ABSTRACT In this article, we proposed a novel method based on deep learning shape priors for object extraction in high-resolution (HR) remote-sensing images. Specifically, the deep Boltzmann machines (DBMs) are applied to model the shape priors via the unsupervised training process, which qualify for the advantages of deep learning method, especially the powerful feature learning and modelling ability. The deep shape model is integrated into a new energy function to eliminate the influence of disturbing background. The energy function combines image appearance information and region information. A new region term in the function is proposed to eliminate the influence of object shadow. The process of object extraction is achieved by minimizing the energy function with an iterative optimization algorithm and the Split Bregman method is applied to derive a global solution during the minimization process. Quantitative and qualitative experiments are conducted on the aircraft data set acquired by QuickBird with 60 cm resolution and the results demonstrate the effectiveness of the proposed method.
Database Versioning, a Transparent SQL Approach

In today’s times every piece of software no matter the complexity, produces data, redundant or otherwise, that needs or should be stored in a permanent storage. The simplest way is a file system, but where the need of complexity and flexibility is required, most system designers prefer to use some kind of Database Management System (DBMS). Even with such a management system, on large amounts of data, combined with information updates, it raises the need to retrieve and track the history of modifications of a specific information alongside the dependencies that makes it coherent. Considering the above the natural solution is to have a database revision or versioning system in which the existing algorithms will still perform without or little modifications and the cost of implementation of such a solution is minimal.