Miracle-dz/UniFAR-FTUs huggingface dataset:
Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing

Digital publishers often use real-time auctions to allocate their advertising inventory. These auctions are designed with the assumption that advertising exposures within a user’s browsing or app-usage session are independent. Raﬁeian (2019) empirically documents the interdependence in the sequence of ads in mobile in-app advertising, and shows that dynamic sequencing of ads can improve the match between users and ads. In this paper, we examine the revenue gains from adopting a revenue-optimal dynamic auction to sequence ads. We propose a uniﬁed framework with two components – (1) a theoretical framework to derive the revenue-optimal dynamic auction that captures both advertisers’ strategic bidding and users’ ad response and app usage, and (2) an empirical framework that involves the structural estimation of advertisers’ click valuations as well as personalized estimation of users’ behavior using machine learning techniques. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We document signiﬁcant revenue gains from using the revenue-optimal dynamic auction compared to the revenue-optimal static auction. These gains stem from the improvement in the match between users and ads in the dynamic auction. The revenue-optimal dynamic auction also improves all key market outcomes, such as the total surplus, average advertisers’ surplus, and market concentration.
Attack Detection for Cadaver Iris

This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Post-mortem iris recognition, despite being a potentially useful method that could aid forensic identification, can also pose challenges when used inappropriately, i.e. utilizing a dead organ of a person in an unauthorized way. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the WarsawBioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER≈1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of anteand post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of anteand post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of 978-1-5386-7180-1/18/$31.00 ©2018 IEEE the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a postmortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.
Temperature and Strain Measurements in Concrete using MicroStructure Optical Fiber Sensors

A recent test carried out to evaluate the construction feasibility of the Belgian supercontainer concept incorporated several types of state-of-the-art sensors and innovative monitoring techniques, including the use of different types of optical fiber sensors. One of these is a relatively new type of sensor developed by the Brussels Photonics Team (B-PHOT) of the Vrije Universiteit Brussel. The sensor uses highly birefringent microstructured optical fibers equipped with fiber Bragg gratings (MOFBGs) sensors. They were embedded in a carbon-fiber reinforced composite plate to provide protection against the concrete’s highly alkaline environment, facilitate installation in the concrete mould and allow the transfer of strain onto the fiber. The double reflection spectrum of the MOFBGs allows monitoring strain and temperature simultaneously. This paper presents results of temperature and strain measurements obtained with MOFBG sensors during a 1⁄2scale test performed in 2013. The results compare well with similar measurements obtained using conventional thermocouples and vibrating wire strain gauges.
Text To Speech Systems in South Indian Languages: A Survey

A Text To Speech system synthesizes text and converts it into speech. People who are blind or visually impaired can use Text To Speech systems for knowledge acquisition, and as a prompting tool to use computers. Compared to English and other European languages like French, Spanish etc. much research is not done in Indian languages. Especially in South Indian languages, the research works carried out are limited with only a few exceptions. In this paper, we attempt to give an overview about the research works conducted previously in four south Indian languages Malayalam, Kannada, Tamil
Comparison of Selection Strategies in Monte Carlo Tree Search for Computer Poker

 This paper discusses and compares two alternative approaches to the selection problem in Monte-Carlo Tree Search for computer poker. The first approach relies on conventional multi-armed bandit algorithms, which aim to balance exploitation vs exploration in order to perform a sequence of actions which maximizes the sum of collected gains. The second approach relies on a “selecting-the-best” strategy where the objective is not to maximize the sum of collected gains but to maximize the probability of a correct action after a finite number of steps. These two ways of solving a sequential selection problem lead to two alternative algorithms in the context of computer poker. This paper presents the two algorithms and assesses their impact on the performance of a Texas Hold’em poker bot.
Accuracy, Consistency, and Reproducibility of the Triaxial Accelerometer in the iPod Touch: A Pilot Study

Background The use of a mobile consumer communicative device as a motion analysis tool for patients has been researched and documented previously, examining the triaxial accelerometer embedded in such devices. However, there have been few reports in the literature testing the sensitivity of an embedded triaxial accelerometer. Objective Our goal in this study was to test the accuracy, consistency, and reproducibility of the triaxial accelerometer in the iPod Touch. Methods In this pilot study, we subjected the triaxial accelerometer in the iPod Touch to a free fall from a height of 100 cm in order to test its accuracy, consistency, and reproducibility under dynamic conditions. Results The resultant vectorial sum acceleration was mean 0.999 g (standard gravity; SD 1.51%; 95% CI 0.99-1.01), indicating very high accuracy and sensitivity under dynamic conditions. Conclusions Our results highlighted the reproducibility of the capability of the triaxial accelerometer in the iPod Touch to capture data accurately and consistently. Thus, the device has huge potential as a motion analysis tool for measuring gait and studying balance and mobility in patients before and after surgery.
SSD Aware File System Page Cache Algorithms Designs and Implementation to Increase Switch Merge and Reduce Full Merge

The NAND based SSD is a tremendous challenge for file system page cache management design as it possess inbuilt imbalance in reading and writing speeds. The present disk-based page cache management policies adopt strategies to maximize the page hit ratio and treat the cost of page read and writes are equal rather than fully exploiting the characteristics of the SSD. Therefore there exists slowness in SSD write which eventually affects the SSD endurance. In special cases like in random writes there exists a problem of augmented write amplification resulting in penalty of write performance. Therefore, a new page replacement algorithm is needed for storage systems based on NAND flash memory. The aim of this study is to propose a new page replacement algorithm for operating systems which focuses on reducing the replacement cost and I/O execution time. Trace-driven simulations show that the proposed algorithm performs better than existing algorithms in terms of the replacement cost and I/O execution time.
Sentence processing: A tutorial review.

Arc suppression, motor protection, and dynamic braking network for A.C. motors. Rectifying means are used to provide positive polarity half-cycle power or interrupted pulsating current to one side of an A.C. motor. The back EMF of the motor maintains the stationary magnetic field during the negative half cycle of power in the braking circuit. The electro-inertial characteristics of sudden braking and stopping action produce power surges in the motor circuits. The network reduces arcing and thermal loading when the braking circuit is opened at the moment the motor reaches a stopped condition. It is at the instant the motor stops that maximum current is passing through the stator windings and the need for arc suppression and motor protection is greatest.
aMUSE : Translating Text to Point and Click Games

In this demo we will show aMUSE — a system for automatically translating text, in particular children stories, to simple 2D point and click games. aMUSE consists of a pipeline of state-ofthe-art natural language processing tools to analyse syntax, extract actions and their arguments and resolve pronouns and indirect mentions of entities in the story. Analysed text serves as data the game mechanics operate on, while the story is represented graphically by images the system downloads from the Internet. The system can also merge multiple stories from a similar domain into a branching narrative. Users will be able to both play games created by aMUSE and create games from their own texts using the aMUSE editor.
Selecting Methods for the Analysis of Reliance on Automation

Human reliance on imperfect automation has been the subject of many laboratory experiments. Across these studies, a diverse set of indices of reliance have been used, even in studies with similar experiment settings. This inconsistency of reliance measures makes the meta-analysis of experimental findings difficult. Moreover, few researchers have rigorously defined the optimal level of automation reliance in their settings, making it difficult to judge the appropriateness of that reliance. This paper attempts to guide researchers in selecting and interpreting measures of automation reliance behavior. We review the reliance analysis methods in existing research and propose four criteria for selecting among them. It is recommended that, where possible, future studies define optimal reliance to make unequivocal judgment about the appropriateness of reliance. In addition, more reliable and insightful conclusions can be obtained through the use of multiple measures.
Investigating Emergent Behavior caused by Electric Vehicles in the Smart Grid using Co-Simulation

Ahstract- Today's electricity system faces major challenges by the ongoing integration of decentralized, renewable energy resources and individual participants benefitting from the Internet of Things (IoT), like Electric Vehicles (EVs) or Smart Meters. The interplay of these autonomous components forms the popular term of the so-called Smart Grid. Since social mannerism may result in simultaneous charging cycles of EVs in such a System of Systems (SoS), ominous peak loads are expected to emerge. Thus, to deal with this often unpredictable behavior before implementing the system, usually a simulation is applied. Therefore, this paper proposes a co-simulation approach using Mosaik, a framework tailored to the Smart Grid domain. By doing so, the power system including several EVs and their charging strategy is modeled according to the Smart Grid Architecture Model (SGAM) in the first step. Next, in order to simulate and validate the system's emergent behavior, an excerpt of a real-world case study is utilized. Based on the outcome of this co-simulation, the practical investigation of Smart Grids can be improved by applying protruded demand side response approaches.
Adaptive Packet Scheduling for Resource Interference Avoidance in LTE-Advanced Networks

Resource interference degrades the Quality of Service (QoS) in resource shared networks and this affects Device-to- Device (D2D) communications. This paper proposes an Adaptive Packet Scheduling Mechanism (APSM) to avoid resource interferences for (D2D) communication, which underlays Long-Term Evolution (LTE) networks. The proposed mechanism provides QoS-guaranteed service for D2D communications in all the three aspects of QoS, i.e. throughput, packet loss, and packet delay. The simulation results show that the proposed mechanism provides more efficient throughput compared to that of the regular cellular network.
Analyzing the Goal Structured Requirements Engineering and Traceability Model

The principal role of Data warehouses in making strategy decisions makes its quality crucial for organizations. Quality of data models for data warehouse has significant effect on the quality of data warehouse. Therefore, we need methods, models and tools to have quantitative and objective measurement of quality of data models for data warehouses. Lack of a set of defined measures made us to define a set of metrics that can be used to measure the complexity, understandability, believability, usability, accessibility, data currency and modifiability which are in turn defined as quality characteristics for data warehouse systems. The proposed metrics may act as objective indicators of the quality of data warehouse models and also help the developers to select quality data model among various semantically equivalent models. The other advantage of these metrics is that they are available during early phase of software development life cycle.
Extended fuzzy background modeling for moving vehicle detection using infrared vision

Running average is a simple and effective background modeling method that generates adaptive background image for moving object detection. Fuzzy Running Average (FRA) improves the selectivity of Standard Running Average (SRA). However, its background restoration rate is slow. This leads to false object detection when a static object becomes dynamic. To overcome this problem, an Extended Fuzzy Running Average (EFRA) is proposed. The results show that the EFRA not only retains the selectivity benefit of FRA, but also improves the restoration rate significantly.
Cluster-Based Multi-Path Routing for Multi-Hop Wireless Networks

Multi-path routing has been studied widely in wired networks. Multi-path routing is known to increase end-to-end throughput and provide load balancing in wired networks. However, its advantage is not obvious in wireless multi-hop network because the traffic along the multiple paths may interfere with adjacent paths. In the paper, we introduce a new multi-path routing scheme, Cluster-Based Multi-Path Routing for multi-hop wireless networks. The main idea of the proposed routing scheme is to extend the hop-by-hop multi-path to a cluster-by-cluster multi-path. In cluster network, each cluster can work independently from other clusters and hence reduce interference. The purpose of the proposed scheme is to find a less interfering path for wireless multi-hop networks. We also showed the throughput improvement of the proposed scheme through simulations.
Reducing cache energy consumption by tag encoding in embedded processors

This paper presents a new technique eliminating redundant cache tag and data accesses to reduce energy consumption. We assign a register to each tag in a cache to represent its state. Before starting an access, we can check the tag states in the target cache set to determine which way(s) should be accessed and which should not. Through this method, almost all the accesses in the I-cache can be directed to the target cache way immediately for most benchmark programs. For a 2-way set-associative cache, the energy consumption can be reduced by 76.6% compared with conventional cache architecture, and by 39.8% compared with Block Buffering, a simple but well-known technique. Besides, this approach does not require any special circuitry internal to the cache RAM such as row or column activation mechanisms. This is considered an important advantage in industry because of its easy implementation.
EFFICIENT IMPLEMENTATION OF PARTICLE IN CELL ALGORITHM ON GPUS

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science EFFICIENT IMPLEMENTATION OF PARTICLE IN CELL ALGORITHM ON GPUS By Rejith George Joseph May 2011 Chair: Dr Sanjay Ranka Major: Computer Engineering Particle in cell (PIC) algorithm is a widely used method in plasma physics to study the trajectories of charged particles under electromagnetic fields. The PIC algorithm is computationally intensive and its time requirements are proportional to the number of charged particles involved in the simulation. The focus of the thesis is to parallelize the PIC algorithm on Graphics Processing Unit (GPU). We present several performance trade-offs related to small shared memory and atomic operations on the GPU to achieve high performance. We have also discussed a better way to implement bucket Sort in GPU.
MULTI-PERIOD ARCHAEOLOGY AT WELLOW LANE PEASEDOWN ST JOHN: EXCAVATIONS 2004–5

Excavations in 2004–5 at Wellow Lane, Peasedown St John revealed two burials of human bone dated to the Middle Bronze Age. A deep ditch of uncertain function and part of a possible field system suggested activity in the Middle Iron Age. Part of an enclosure of late Roman date was also identified. Extending beyond the northern limit of the excavated area was a ‘D’-shaped enclosure with internal postholes. Radiocarbon dated charred plant remains recovered from the ditch fill indicated that the enclosure was in use in the Middle Saxon period. Undated postholes within the enclosure implied small non-domestic structures. A cluster of features dating to the 11th to 13th centuries suggested further activity on the site in this period. The excavation also demonstrated a southerly continuation of the burial ground identified by evaluation trenches located to the north-east of the excavated area. These burials could not be reliably dated but postdate the Iron Age field system and may be related to the Late Saxon and early medieval evidence for settlement at Eckweek to the north. Residual Mesolithic flints were also recovered from an excavated feature.
Facial Recognition Attendance Checker

This work presents a novel facial recognition framework for attendance checking, by using the student’s smartphone and Bluetooth-low-energy (BLE) beacons placed in the classroom. Facial recognition is used for authentication based on the Active Appearance Model (68-point facial landmarks) that is converted to a 128-dimensional vector space. One or more beacons are used to track the sitting position of each student. Then image processing is used to analyze the current facial sentiment of the student. Facial recognition models based on Naïve Bayes, Support Vector Machine, and Random Forest were compared. The best model was found to be Random Forest with an accuracy of about 98% on the test data. A web-based application has been deployed and tested on Android smartphones to connect with the API for the needed services.
Domain wall motion based magnetic adder

Presented is the first design of a multi-bit magnetic adder (MA) based on domain wall (DW) motion. All the input and output signals are stored in non-volatile DW shift registers instead of CMOS registers. One can turn off safely the logic circuits without data backup and power them on instantly. This new function promises to overcome completely the rising standby power issue. Moreover, the direct integration of the memory cell in logic circuits reduces greatly the dynamic power dedicated to data moving between logic and memory. An 8-bit MA has been successfully simulated based on a 65 nm node.
Whole-Genome Genetic Data Simulation Based on Mutation-Drift Equilibrium Model

Whole-genome data simulation is popular in genomic research, especially in methodology development for whole-genome data analysis. In this research, the frequently used mutation-drift equilibrium (MDE) model was employed to simulate whole-genome data. Based on the MDE model, we proposed a mixture distribution to sample the positions of loci and a rule to recode multi-allelic markers to bi-allelic markers. The effects of effective population size and mutation rate in historical generations were assessed. GPOPSIM, a program using the rules proposed in this paper, was developed to simulate whole-genome data. Data simulated via GPOPSIM was compared with data from the Illumina Bovine SNP chip. Results show that the MDE model is a recommendable model for the whole-genome data simulation. The proposed strategy effectively increased the efficiency of simulation program.
Analysing the Twitter social graph: Whom can we trust?

Twitter is a popular microblogging service that enables users to send short messages (“tweets”), most of which are public. Users can follow each other to receive these tweets, which creates a directed graph of followers. Therefore Twitter can be considered as a medium of information broadcast – where some people produce new information, and others consume it. Much like any other information source, the question of how trusted an information source is (users, in this case) is of primal importance. In this project, we study the Twitter social graph to look for ways to identify users who can be “trusted”, that is, users who are who they say they are and whose posts can be considered genuine. We do this by analysing characteristics of the users and their position on the social graph. There is a set of users who have been verified to be genuine by Twitter, which is typically done for celebrities and notable businesses. We analyse other users’ trust scores based on the assumption that verified users can be trusted. A key part of the work is to establish objective metrics that can serve as proxy for the subjective notion of “trust”. We first worked on a smaller data set of Twitter in 2009 and explored different metrics and found the metric of “trust score” to be the most efficient in classifying users as trusted. We then applied this method on the more recent and larger dataset of 2012 to support our claim that the trust score is a good metric in estimating how trusted a user is.
Assessing alignment of an e-government project in the context of a developing country: a Heideggerian perspective

E-Government has increasingly become an alternative for enabling public governance at various. The situation is especially poignant in developing countries where there are increasing calls to improve governance for improved national development. This study sought to investigate managerial processes in the alignment of e-government within Local Authorities in Kenya. A case study research design underpinned by critical realism assumptions was used. The analysis was grounded on empirical results of the study, and employed theoretical thematic analysis. Under the alienating conditions of social exclusion, the alignment of the e-government artifact espoused strong managerialist orientations of augmenting and reinforcing central governments control over its polity. A consequence of this alignment is a tendency towards technocratic evolution of the public service delivery system in local authorities in Kenya. However, it was also recognised that the form of e-government projects are constantly in flux, and that relying on a single case was therefore a limitation of the study. Categories and Subject Descriptors