Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer When is naive evaluation possible?

The term na¨ıve evaluation refers to evaluating queries over incomplete databases as if nulls were usual data values, i.e., to using the standard database query evaluation engine. Since the semantics of query answering over incomplete databases is that of certain answers, we would like to know when na¨ıve evaluation computes them: i.e., when certain answers can be found without inventing new specialized algorithms. For relational databases it is well known that unions of conjunctive queries possess this desirable property, and results on preservation of formulae under homomorphisms tell us that within relational calculus, this class cannot be extended under the open-world assumption. Our goal here is twofold. First, we develop a general framework that allows us to determine, for a given semantics of incompleteness, classes of queries for which na¨ıve evaluation computes certain answers. Second, we apply this approach to a variety of semantics, showing that for many classes of queries beyond unions of conjunctive queries, na¨ıve evaluation makes perfect sense under assumptions diﬀerent from open-world. Our key observations are: (1) na¨ıve evaluation is equivalent to monotonicity of queries with respect to a semantics-induced ordering, and (2) for most reasonable semantics, such monotonicity is captured by preservation under various types of homomorphisms. Using these results we ﬁnd classes of queries for which na¨ıve evaluation works, e.g., positive ﬁrst-order formulae for the closed-world semantics. Even more, we introduce a general relation-based framework for deﬁning semantics of incompleteness, show how it can be used to capture many known semantics and to introduce new ones, and describe classes of ﬁrst-order queries for which na¨ıve evaluation works under such semantics.
Water Vapor Contribution to Ceres' Exosphere From Observed Surface Ice and Postulated Ice‐Exposing Impacts

Telescopic observations have detected an exosphere around Ceres, composed of either water vapor or its photolytic products. Proposed mechanisms for its formation include sublimation or sputtering from solar energetic particles of buried ice, surface ice, or an optically thin seasonal polar cap. We estimate the amount of water vapor produced by known exposures of water ice, detected in Dawn spacecraft image and spectral data and by ice exposures from subresolution impact craters. We use thermal and sublimation modeling to take into account slope, orientation, and, in the case of water ice within craters, shadowing due to crater walls. We use a Monte Carlo approach to calculate the number of ice‐exposing impacts, where they occur on Ceres' surface, and how long the ice within the impact crater remains bright (e.g., less than one monolayer of sublimation lag). We find that the observed water ice patches on Ceres could account for ~0.06 kg/s of water vapor to (with Oxo crater as the main contributor) and that ice‐exposing impacts that remain bright in appearance after one Ceres year supply 0.08–0.56 kg/s of vapor, depending on the regolith volume fraction of the ice. While water ice has not been detected to date at Occator crater, if it were present we find that Occator is unlikely to be a major contributor of vapor. We find a typical background water vapor production rate from all of Ceres, combining surface and buried ice, of about a few tenths of a kilogram per second.
Impact analysis of database schema changes

When database schemas require change, it is typical to predict the effects of the change, first to gauge if the change is worth the expense, and second, to determine what must be reconciled once the change has taken place. Current techniques to predict the effects of schema changes upon applications that use the database can be expensive and error-prone, making the change process expensive and difficult. Our thesis is that an automated approach for predicting these effects, known as an impact analysis, can create a more informed schema change process, allowing stakeholders to obtain beneficial information, at lower costs than currently used industrial practice. This is an interesting research problem because modern data-access practices make it difficult to create an automated analysis that can identify the dependencies between applications and the database schema. In this dissertation we describe a novel analysis that overcomes these difficulties. We present a novel analysis for extracting potential database queries from a program, called query analysis. This query analysis builds upon related work, satisfying the additional requirements that we identify for impact analysis. The impacts of a schema change can be predicted by analysing the results of query analysis, using a process we call impact calculation. We describe impact calculation in detail, and show how it can be practically and efficiently implemented. Due to the level of accuracy required by our query analysis, the analysis can become expensive, so we describe existing and novel approaches for maintaining an efficient and computational tractable analysis. We describe a practical and efficient prototype implementation of our schema change impact analysis, called SUITE. We describe how SUITE was used to evaluate our thesis, using a historical case study of a large commercial software project. The results of this case study show that our impact analysis is feasible for large commercial software applications, and likely to be useful in real-world software development.
Advancing Flood Flow Prediction Models for Ungauged Basins

ADVANCING FLOOD FLOW PREDICTION MODELS FOR UNGAUGED BASINS Rachel Walton Advisor: University of Guelph, 2018 Dr. Bahram Gharabaghi Predicting peak flows at ungauged basins has been a notable focus in water resources research, however the majority of methods are only appropriate for a small area, cannot be translated to other jurisdictions, or require variables that are difficult to measure or obtain. Through the analysis of over 7000 stream gauges from the USA, this work presents a simplistic, unified equation to predict return period peak flows, incorporating influential and easy to obtain input variables. Two novel variables are introduced: the Land Use Soil (LUS) factor and the 2 – year return precipitation effect (PE2). The equation achieved an R of 0.95, 0.83 and 0.86 on the training, testing and southern Ontario data sets, respectively, demonstrating high predictive capabilities. This research presents a logical method for predicting return period peak flows while advancing insight on the implications of land use, soil and precipitation on the magnitude of peak flows.
Gearbox fault detection using static data and adaptive neurofuzzy inference system

Condition monitoring of a gearbox is a crucial activity due to its importance in power transmission for many industrial applications. Thus, there has always been a constant pressure to improve measuring techniques and analytical tools for early detection of faults in gearboxes. This study forces to develop the gearbox monitoring methods using the operating parameters obtained from machine control processes rather than the traditional measurements such as vibration and acoustics. To monitor the gearbox conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to captures the nonlinear connections between the electrical motor current and control parameters such as load settings and temperatures. The predicted values generated by ANFIS model are then compared with the measured values to indicate the abnormal condition in gearbox. The experimental results show that ANFIS model is adequate and is able to serve as an efficient tool for gearbox condition monitoring and fault detection.
A Coordinate Descent based approach to solving the Sparse Group Elastic Net - Supplemental Material

This document provides additional details on the Sparse Group Elastic Net (SGEN). Section S2 provides theoretical results and additional discussion on the connection of the SGEN model to the traditional elastic net is provided in Section S3. Note, all sections and equations in the Supplemental Material are preﬁxed with an “S”, any references to equations in the main document are not.
A hybrid speech enhancement system employing blind source separation and adaptive noise cancellation

This paper presents a new hybrid speech enhancement scheme employing the blind source separation (BSS) and the adaptive noise cancellers (ANC). Since BSS primarily exploits spatial information, it has its limitation in its separation quality. To compensate for that, the ANC acts as a temporal decorrelator to perform further noise cancellation. Initially, the BSS separates the target signal from the interference using the L observed inputs. A higher order statistical method is then used to distinguish the speech dominant signal from the L BSS output(s). The remaining L − 1 (interference dominant) outputs will serve as the reference signals for the ANC to perform further interference reduction on the speech dominant output. The novel structure bypasses a priori information such as the array geometry and source localisation needed by conventional beamforming based methods. Results show that the structure offers impressive enhancement capability with only a few microphones.
Designing computer-based rewards with and for children with Autism Spectrum Disorder and/or Intellectual Disability Computers in Human Behavior

Children with Autism Spectrum Disorder (ASD) tend to have an affinity for digital technologies, often preferring computer-assisted learning to human-assisted learning. Many children with ASD are also diagnosed with Intellectual Disabilities (ID), yet design studies involving children with ASD and ID are scarce. Rewards can have a positive impact on children’s learning and motivation, but little is known about the nature and impact of rewards for children with ASD, and/or ID. Digital technologies are well placed to provide task-based rewards, and in combination with a better understanding of the reward preferences of children with ASD and/or ID this has significant potential to enhance learning. This paper presents two robust participatory design (PD) studies involving children with: i) ASD; ii) ID; and iii) both ASD and ID. The studies aimed to identify: i) the reward preferences of children with ASD and/or ID (RQ1) and ii) how rewards might develop throughout a task as the child progresses (RQ2). Results revealed a number of reward categories that were common to all children, as well as children’s preferences for how rewards could develop as they progress through computer-based tasks, for the first time. Original implications for designing computer-based rewards embedded within digital intervention/educational technologies for children with ASD and/or ID, are discussed.
DEVELOPMENT OF A SOLAP PATRIMONY MANAGEMENT APPLICATION SYSTEM: FEZ MEDINA AS A CASE STUDY

Traditional association rules are mostly mining intra- transaction associations i.e., associations among items within the same transaction where the idea behind the transaction could be the items bought by the same customer. In our work, we utilize the framework of inter -transaction association rules, which associate events across a window of transaction. The new association relationship breaks the barriers of transaction and can be used for prediction. Typically, the rules generated are derived from the patterns in a particular dataset. A major issue that needs more attention is the soundness of the rules outside of the dataset from which they are generated. When new phenomenon happens, the change in set of rules generated from the new dataset becomes more significant. In this paper, we provide a model for understanding how the differences between different situations affect the changes of the rules based on the concept of groups what we call factions. Also we provide a technique called Coalescent dataset, to generate set of rules for a new situation. Various experimental results are reported by comparing with real life and synthetic datasets, and we show the effectiveness of our work in generating rules and in finding acceptable set of rules under varying conditions.
Mining Rare Periodic-Frequent Patterns Using Multiple Minimum Supports.

Recently, an approach has been proposed in the literature to extract frequent patterns which occur periodically. In this paper, we have proposed an approach to extract rare periodic-frequent patterns. Normally, the single minsup based frequent pattern mining approaches like Apriori and FP-growth suffer from “rare item problem”. That is, at high minsup, frequent patterns consisting of rare items will be missed, and at low minsup, number of frequent patterns explode. In the literature, efforts have been made to extract rare frequent patterns under “multiple minimum support framework”. It was observed that the periodic-frequent pattern mining approach also suffers from the “rare item problem”. In this paper, we have extended “multiple minimum support framework” to extract rare periodic-frequent patterns and developed a new algorithm to extract rare periodic-frequent patterns. Experiment results show that the proposed approach is efficient.
Night Vision Technology

Superimposed luminance racket is regular of imagery from devices used for low-light vision, for instance, picture intensifiers (i.e., night vision contraptions). In four examinations, we checked the ability to recognize and isolate development portrayed shapes as a component of lift movement to-clatter extent at a collection of shock speeds. Self-driving vehicles can change the way we travel. Their headway is at a basic point, as a creating number of mechanical and academic research affiliations are bringing these advancements into controlled however evident settings. An essential capacity of a self-driving vehicle is condition understanding: Where are the general population by walking, substitute vehicles, and the drivable space? In PC and robot vision, the errand of perceiving semantic classes at a for every pixel level is known as scene parsing or semantic division. While much progress has been made in scene parsing starting late, current datasets for getting ready and benchmarking scene parsing estimations revolve around apparent driving conditions: sensible atmosphere and generally daytime lighting. To supplement the standard benchmarks, we show the Rain cover scene parsing benchmark, which to the extent anybody is concerned is the principle scene parsin benchmark to base on testing tempestuous driving conditions, in the midst of the day, at dusk, and amid the night. Our dataset contains 30 minutes of driving video got in the city of Vancouver, Canada, and 326 edges with hand-remarked on pixel astute semantic imprints.
An Analysis of Reliable Delivery Specifications for Web Services

Reliable delivery of messages is now a key component of the Web Services roadmap, with two promising, and competing, specifications in this area viz. WS-Reliability from OASIS and WS-ReliableMessaging from IBM and Microsoft. In this paper we provide an analysis of these specifications. Our investigations have been aimed at identifying the similarities and divergence in philosophies of these specifications. We also include a gap analysis and a series of recommendations plugging the gaps identified by the gap analysis.
Object Interconnections Comparing Alternative Programming Techniques for Multi-threaded CORBA Servers (Column 6)

Modern OS platforms like UNIX, Windows NT, and OS/2 provide extensive library and system call support for multi-threaded applications. However, programming multithreaded applications is hard and programming distributed multi-threaded applications is even harder. In particular, developers must address sources of accidental and inherent complexity: Accidental complexity of multi-threaded programming arises from limitations with programming tools and design techniques. For example, many debuggers can’t handle threaded programs and can’t step across host boundaries. Likewise, algorithmic design [1] makes it hard to reuse application components because it tightly couples the structure of a threaded application to the functions it performs. Inherent complexity of multi-threaded programming arises from challenges such as avoiding deadlock and livelock, eliminating race conditions for shared objects, and minimizing the overhead of context switch, synchronization, and data movement. An inherently complex aspect of programming multi-threaded distributed applications (particularly servers) involves selecting the appropriate concurrency model, which is the focus of this column.
International Conference on Parallel Processing On Sorting Multiple Bitonic Sequences

Bitonic sorters sort a single bitonic sequence into an ascending sequence. A multi-bitonic sorter is pre­ sented here, which sorts k bitonic sequences of n keys each into an ascending sequence in at most time delay, where T(l,k) is the time delay needed to sort k keys in order; and k is any integer not restricted to 1.
Turbulent Simulation of the Flow around Different Positions of Mussel Shocks

This study presents the results of laboratory experiments that were performed to simulate the positions of mussel shocks which were selected aiming at the optimization of the quality of mussels’ production in mussel farming areas. The mussel shocks were studied in natural scale. Velocity measurements were taken upstream of two successive mussel shocks and for different positions in relation to the central axis of the channel and different distances between the shocks for three different mean velocities. Based on the results of several numbers of experiments, the main conclusion of this study was that the position and the distance between the mussel shocks play a significant role to the quality of mussels’ production in mussel farming areas. This is due to the fact that the different distances between the mussel shocks influence the velocities and the eddies around them.
Use What You Choose: Applying Computational Methods to Genre Studies in Technical Communication

This paper reports on the results of an intensive application development workshop held in the summer of 2015 during which a group of thirteen researchers came together to explore the use of machine-learning algorithms in technical communication. To do this we analyzed Amazon.com consumer electronic product customer reviews to reevaluate a central concept in North American Genre Theory: stable genre structures arise from recurring social actions ([1][2][3][4][5]). We discovered evidence of genre hybridity in the signals of instructional genres embedded into customer reviews. Our paper discusses the creation of a prototype web application, “Use What You Choose” (UWYC), which sorts the natural language text of Amazon reviews into two categories: instructionally-weighed reviews (e.g., reviews that contain operational information about products) and non-instructionally-weighed reviews (those that evaluate the quality of the product). Our results contribute to rhetorical genre theory and offer ideas on applying genre theory to inform application design for users of information services.
Security and Privacy Protection in Wireless Sensor Networks

Security and privacy protection are of extreme importance for many of the proposed applications of wireless sensor networks (WSNs). The list of potential applications that require protection mechanisms includes early target tracking and monitoring on a battlefield; law enforcement applications; automotive telemetric applications; room occupation monitoring in office buildings; measuring temperature and pressure in oil pipelines [1]; and forest fire detection. All these applications have unlimited benefits and potential; however, if the sensor information is not protected properly, possible compromises in user information, the environment, and even physical actuators could result. The primary driving impetus for the development of sensor networks has been military applications, where security requirements are at their highest [2]. Although a WSN deployed on a battlefield can offer a reliable assessment of battlefield conditions without risking lives, an inadequately protected network could become a powerful weapon for an enemy. Strong security requirements for such applications are often combined with an inhospitable and physically unprotected environment. For commercial applications of WSNs, the issue of privacy protection is as important as secure and reliable functioning of a network. The protection of personal physiological and psychological information is expected by any user. As the applications of WSNs become more complex and widespread, the ability to protect such systems from any unauthorized access will become increasingly important. Sensor networks operate in a variety of physical environments and under varieties of constraints. The limited resources of sensor nodes require the development of customized system architectures for each particular WSN application so that the sensor node resources are efficiently used. Because security and privacy protection mechanisms require a significant amount of computational and storage resources, Sasha Slijepcevic University of California at Los Angeles
ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Genetic Interactions

A Synthetic Lethal (SL) interaction is a functional relationship be-tween two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of SL. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits.