Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Lottery Systems in School Admissions

This product is part of the RAND Europe working paper series. RAND working papers are intended to share researchers' latest findings and to solicit additional peer review. This paper has been peer reviewed but not edited. Unless otherwise indicated, working papers can be quoted and cited without permission of the author, provided the source is clearly referred to as a working paper. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors. is a registered trademark. iii Preface The Sutton Trust has commissioned RAND Europe to undertake a brief review of the use of lottery/ballot systems in admissions to schools. Evidence in the United Kingdom suggests that schools are [becoming] socially selective. Trends in school admissions on the basis of catchment areas might exacerbate social inequality in the United Kingdom. The new draft School Admissions Code coming into force in September 2008 allows schools to allocate places through the use of a lottery system. The underlying reason is that lotteries are seen as a fair and transparent way to distribute school places and promote equal access to educational opportunities. This raises questions on how and why lotteries have been used, how lottery schemes have been designed, and what the outcomes associated with lottery schemes have been. This brief review, drawing mainly on international examples, presents preliminary findings on the use of lottery schemes in several countries. The RAND Europe study team undertook a detailed literature review to arrive at these preliminary findings. The report consists of: a summary, which outlines the main themes and findings; an introductory chapter; a chapter on the selected examples of school lottery systems that we identified; and a chapter outlining some of the implications for the UK. This report will be of particular interest to policy makers, as well as a wider audience concerned with education policy and social inequality. RAND Europe is an independent not-for-profit policy research organisation that aims to serve the public interest by improving policymaking and informing public debate. Its clients are European governments, institutions, and firms with a need for rigorous, impartial, multidisciplinary analysis. This report has been peer-reviewed in accordance with RAND's quality assurance standards. Summary The Sutton Trust asked RAND Europe to carry out a short study of the use of lottery/ballot systems in admissions to schools, drawing on international evidence from both school and university sectors. The context of this research is the …
AN OCTA-CORE PROCESSOR WITH SHARED MEMORY AND MESSAGE-PASSING

This being the era of fast, high performance computing, there is the need of having efficient optimizations in the processor architecture and at the same time in memory hierarchy too. Each and every day, the advancement of applications in communication and multimedia systems are compelling to increase number of cores in the main processor viz., dual-core, quadcore, octa-core and so on. But, for enhancing the overall performance of multi processor chip, there are stringent requirements to improve inter-core synchronization. Thus, a MPSoC with 8-cores supporting both message-passing and shared-memory intercore communication mechanisms is implemented on Virtex 5 LX110T FPGA. Each core is based on MIPS III (Microprocessor without interlocked pipelined stages) ISA, handling only integer type instructions and having six-stage pipeline with data hazard detection unit and forwarding logic. The eight processing cores and one central shared memory core are inter connected using 3x3 2-D mesh topology based Network-on-chip (NoC) with virtual channel router. The router is four stage pipelined supporting DOR X-Y routing algorithm and with round robin arbitration technique. For verification and functionality test of above fully synthesized multi core processor, matrix multiplication operation is mapped onto the above said. Partitioning and scheduling of multiple multiplications and addition for each element of resultant matrix has been done accordingly among eight cores to get maximum throughput. All the codes for processor design are written in Verilog HDL.
Integrated Learning of Words and Their Underlying Concepts

Abstract : Models of learning word meanings have generally assumed prior knowledge of the concepts to which the words refer. However, novel natural language text or discourse can often present both unknown concepts and words and their concepts frequently occurs concurrently instead of concept learning proceeding word learning. This paper presents an integrated computational model for acquiring both word meanings and their underlying concepts concurrently. This model is implemented as a word learning component added to the GENESIS explanation-based schema acquisition system for narrative understanding. A detailed example is described in which GENESIS learns provisional definitions for the words kidnap, kidnapper, and ransom as well as a kidnapping schema from a single narrative. Keywords: Explanation-Based Learning, Concept Acquisition.
Bones, behaviour and belief : the zooarchaeological evidence as a source for ritual practice in ancient Greece and beyond

The importance of the osteological evidence as a source for ritual practices in ancient Greece is gradually becoming widely recognized. Animal bones form the only category of evidence for Greek cult which is constantly increasing, and they can complement and elucidate the information provided by texts, inscriptions and images. This volume brings together sixteen contributions exploring ritual practices and animal bones from different chronological and geographical perspectives, foremost ancient Greece in the historical period, but also in the Bronze Age and as early as the Neolithic period, as well as Anatolia, France and Scandinavia, providing new empirical evidence from a number of major sanctuaries and cult-places. On a methodological level, the complexity of identifying ritual activity from the osteological evidence is a recurrent theme, as is the prominence of local variation visible in the bone material, suggesting that the written sources and iconography may offer simplified or idealized versions of the rituals actually performed. Although osteology needs to and should be integrated with other kinds of sources, the independent study of the bones in an unbiased manner is of utmost importance, as the bones can provide a different “reality” than that encountered in our other sources.
Doing your thing? Trisha Brown's object lesson

It is absolutely clear that, by his activity, man changes the forms of the materials of nature in such a way as to make them useful to him. The form of wood, for instance, is altered if a table is made out of it. Nevertheless, the table continues to be wood, an ordinary, sensuous thing. But as soon as it emerges a commodity, it stands on its head, and evolves out of its wooden brain grotesque ideas, far more wonderful than if it were to begin dancing of its own free will. —Karl Marx, Capital
Investigation of vascular dysfunction in pre-clinical models of sepsis

Sepsis is an overwhelming inflammatory response to infection that can progress to septic shock, characterised by refractory hypotension and insufficient organ perfusion. It is predominantly associated with nosocomial infections, and tends to occur in extremes of age, where it is associated with very high mortality rates. Sepsis and sepsis-associated multi-organ failure represent a tremendous healthcare burden and a significant cause of morbidity and mortality worldwide. Incidence has continued to increase over several decades, and septic shock is now the leading cause of death in intensive care units, claiming an estimated 20,000 lives per day worldwide. Despite decades of endeavour, research has not produced any specific pharmacological treatments shown definitively to improve survival in septic patients. Cardiovascular collapse plays a key role in mortality, and correspondingly, pre-clinical and clinical assessment of the impact of interventions on disease progression has been based upon systemic haemodynamics and achieving target blood pressures. In recent years, however, it has become increasingly clear that systemic stabilisation does not necessarily prevent the onset of organ failure, and may actually be achieved at the expense of visceral perfusion. Sepsis is now increasingly understood to be a ‘disease of the microcirculation’, though pre-clinical models rarely assess this parameter, perhaps accounting for the lack of research translatability. There is thus a critical need to develop more clinically relevant pre-clinical models, which focus on clinically prognostic endpoints, in order to identify novel drug targets for the treatment of sepsis. This thesis will describe the development of a novel approach to monitoring cardiovascular dysfunction in two widely used pre-clinical models of sepsis: lipolysaccharide (LPS)-induced endotoxaemia, and cecal ligation and puncture (CLP). This approach comprises a multiparameter monitoring system in which all levels of the cardiovascular system – global haemodynamics, cardiac function, microcirculatory blood flow and haematological markers – can be evaluated in a single animal. Using a novel application of laser speckle contrast imaging technology, in combination with gold standard haemodynamic monitoring techniques, we demonstrate that mesenteric blood flow is severely and time-dependently decreased following the induction of sepsis, despite stabilisation of arterial pressure, heart rate and cardiac output. Decreased mesenteric perfusion is shown to correlate with the development of metabolic acidosis and organ dysfunction, suggesting that global haemodynamic indices are insufficiently predictive of syndrome progression. Mesenteric blood flow, on the other hand, appears to be a sensitive and clinically relevant prognostic marker of disease severity, correlating with
Mobility management in Multihops Wireless Access Networks

Ad hoc networks are spontaneous networks of mobile nodes which use wireless communications. The nodes must collaborate to route and forward data packets from a source to a destination. We consider multihops wireless access networks which are ad hoc networks connected to the Internet via Access Points (AP). A terminal can send and receive data packets to and from the Internet. To achieve this goal, we propose here a micro-mobility management solution. The solution uses a virtual backbone to disseminate information and to limit the overhead. The proposition mixes the reactive and the proactive approaches to propose a trade-off between the delay and the overhead. In upload, each node has a default proactive route toward the AP introducing no latency. In download, the AP initiates a reactive localization to find a route to the destination inside the ad hoc area, reducing the overhead. A solution of paging with several AP and a solution of power-energy saving are also proposed.
A PC based distributed environment for simulation of dynamic and intelligent systems with real-time I/O support

A microcomputer based, cost effective, distributed simulation environment (DYNASIM) is developed. The environment allows simulation of intelligent dynamic systems with real time I/O support. The paper describes the available features of the developed environment. The communication protocol implemented to allow the integration of different distributed hardware and software systems is explained. Applications demonstrating salient features and capabilities of the developed environment are discussed.
GymCam

Worn sensors are popular for automatically tracking exercises. However, a wearable is usually attached to one part of the body, tracks only that location, and thus is inadequate for capturing a wide range of exercises, especially when other limbs are involved. Cameras, on the other hand, can fully track a user's body, but suffer from noise and occlusion. We present GymCam, a camera-based system for automatically detecting, recognizing and tracking multiple people and exercises simultaneously in unconstrained environments without any user intervention. We collected data in a varsity gym, correctly segmenting exercises from other activities with an accuracy of 84.6%, recognizing the type of exercise at 93.6% accuracy, and counting the number of repetitions to within ± 1.7 on average. GymCam advances the field of real-time exercise tracking by filling some crucial gaps, such as tracking whole body motion, handling occlusion, and enabling single-point sensing for a multitude of users.
HeadGesture

We propose HeadGesture, a hands-free input approach to interact with Head Mounted Display (HMD) devices. Using HeadGesture, users do not need to raise their arms to perform gestures or operate remote controllers in the air. Instead, they perform simple gestures with head movement to interact with the devices. In this way, users' hands are free to perform other tasks, e.g., taking notes or manipulating tools. This approach also reduces the hand occlusion of the field of view [11] and alleviates arm fatigue [7]. However, one main challenge for HeadGesture is to distinguish the defined gestures from unintentional movements. To generate intuitive gestures and address the issue of gesture recognition, we proceed through a process of Exploration - Design - Implementation - Evaluation. We first design the gesture set through experiments on gesture space exploration and gesture elicitation with users. Then, we implement algorithms to recognize the gestures, including gesture segmentation, data reformation and unification, feature extraction, and machine learning based classification. Finally, we evaluate user performance of HeadGesture in the target selection experiment and application tests. The results demonstrate that the performance of HeadGesture is comparable to mid-air hand gestures, measured by completion time. Additionally, users feel significantly less fatigue than when using hand gestures and can learn and remember the gestures easily. Based on these findings, we expect HeadGesture to be an efficient supplementary input approach for HMD devices.
Towards a Framework for Teaching Debugging

Debugging is an important component of software development, yet most novice programmers are not explicitly taught to apply systematic strategies or processes for debugging. In this paper we adapt a framework developed for teaching troubleshooting to the debugging domain, and explore how the literature on teaching debugging maps to this framework. We identify debugging processes that are fundamental for novices to learn, aspects of debugging that novices typically struggle to develop, and shortcomings of tools designed to support teaching of debugging.
ObstacleWatch

Walking while using a smartphone is becoming a major pedestrian safety concern as people may unknowingly bump into various obstacles that could lead to severe injuries. In this paper, we propose ObstacleWatch, an acoustic-based obstacle collision detection system to improve the safety of pedestrians who are engaged in smartphone usage while walking. ObstacleWatch leverages the advanced audio hardware of the smartphone to sense the surrounding obstacles and infers fine-grained information about the frontal obstacle for collision detection. In particular, our system emits well-designed inaudible beep signals from the smartphone built-in speaker and listens to the reflections with the stereo recording of the smartphone. By analyzing the reflected signals received at two microphones, ObstacleWatch is able to extract fine-grained information of the frontal obstacle including the distance, angle and size for detecting the possible collisions and to alert users. Our experimental evaluation under two real-world environments with different types of phones and obstacles shows that ObstacleWatch achieves over 92% accuracy in predicting obstacle collisions with distance estimation errors at about 2 cm. Results also show that ObstacleWatch is robust to different sizes of objects and is compatible to different phone models with low energy consumption.
DeepType

Mobile users spend an extensive amount of time on typing. A more efficient text input instrument brings a significant enhancement of user experience. Deep learning techniques have been recently applied to suggesting the next words of input, but to achieve more accurate predictions, these models should be customized for individual users. Personalization is often at the expense of privacy concerns. Existing solutions require users to upload the historical logs of their input text to the cloud so that a deep learning predictor can be trained. In this work, we propose a novel approach, called DeepType, to personalize text input with better privacy. The basic idea is intuitive: training deep learning predictors on the device instead of on the cloud, so that the model makes personalized and private data never leaves the device to externals. With DeepType, a global model is first trained on the cloud using massive public corpora, and our personalization is done by incrementally customizing the global model with data on individual devices. We further propose a set of techniques that effectively reduce the computation cost of training deep learning models on mobile devices at the cost of negligible accuracy loss. Experiments using real-world text input from millions of users demonstrate that DeepType significantly improves the input efficiency for individual users, and its incurred computation and energy costs are within the performance and battery restrictions of typical COTS mobile devices.
A Virtual Environment for Industrial Control Systems: A Nonlinear Use-Case in Attack Detection, Identification, and Response

The integration of modern information technologies with industrial control systems has created an enormous interest in the security of industrial control, however, given the cost, variety, and industry practices, it is hard for researchers to test and deploy security solutions in real-world systems. Industrial control testbeds can be used as tools to test security solutions before they are deployed, and in this paper we extend our previous work to develop open-source virtual industrial control testbeds where computing and networking components are emulated and virtualized, and the physical system is simulated through differential equations. In particular, we implement a nonlinear control system emulating a three-water tank with the associated sensors, PLCs, and actuators that communicate through an emulated network. In addition, we design unknown input observers (UIO) to not only detect that an attack is occurring, but also to identify the source of the malicious false data injections and mitigate its impact. Our system is available through Github to the academic community.
Why Are They Collecting My Data?

Many smartphone apps collect potentially sensitive personal data and send it to cloud servers. However, most mobile users have a poor understanding of why their data is being collected. We present MobiPurpose, a novel technique that can take a network request made by an Android app and then classify the data collection purposes, as one step towards making it possible to explain to non-experts the data disclosure contexts. Our purpose inference works by leveraging two observations: 1) developer naming conventions (e.g., URL paths) of ten offer hints as to data collection purposes, and 2) external knowledge, such as app metadata and information about the domain name, are meaningful cues that can be used to infer the behavior of different traffic requests. MobiPurpose parses each traffic request body into key-value pairs, and infers the data type and data collection purpose of each key-value pair using a combination of supervised learning and text pattern bootstrapping. We evaluated MobiPurpose's effectiveness using a dataset cross-labeled by ten human experts. Our results show that MobiPurpose can predict the data collection purpose with an average precision of 84% (among 19 unique categories).
coSense

The real-time vehicle sensing at urban scale is essential to various urban services. To date, most existing approaches rely on static infrastructures (e.g., traffic cameras) or mobile services (e.g., smartphone apps). However, these approaches are often inadequate for urban scale vehicle sensing at the individual level because of their static natures or low penetration rates. In this paper, we design a sensing system called coSense to utilize commercial vehicular fleets (e.g., taxis, buses, and trucks) for real-time vehicle sensing at urban scale, given (i) the availability of well-equipped commercial fleets sensing other vehicles by onboard cameras or peer-to-peer communication, and (ii) an increasing trend of connected vehicles and autonomous vehicles with periodical status broadcasts for safety applications. Compared to existing solutions based on cameras and smartphones, the key features of coSense are in its high penetration rates and transparent sensing for participating drivers. The key technical challenge we addressed is how to recover spatiotemporal sensing gaps by considering various mobility patterns of commercial vehicles with deep learning. We evaluate coSense with a preliminary road test and a large-scale trace-driven evaluation based on vehicular fleets in the Chinese city Shenzhen, including 14 thousand taxis, 13 thousand buses, 13 thousand trucks, and 10 thousand regular vehicles. We compare coSense to infrastructure and cellphone-based approaches, and the results show that we increase the sensing accuracy by 10.1% and 16.6% on average.
Performance Characterization of Deep Learning Models for Breathing-based Authentication on Resource-Constrained Devices

Providing secure access to smart devices such as smartphones, wearables and various other IoT devices is becoming increasingly important, especially as these devices store a range of sensitive personal information. Breathing acoustics-based authentication offers a highly usable and possibly a secondary authentication mechanism for secure access. Executing sophisticated machine learning pipelines for such authentication on such devices remains an open problem, given their resource limitations in terms of storage, memory and computational power. To investigate this challenge, we compare the performance of an end-to-end system for both user identification and user verification tasks based on breathing acoustics on three type of smart devices: smartphone, smartwatch and Raspberry Pi using both shallow classifiers (i.e., SVM, GMM, Logistic Regression) and deep learning based classifiers (e.g., LSTM, MLP). Via detailed analysis, we conclude that LSTM models for acoustic classification are the smallest in size, have the lowest inference time and are more accurate than all other compared classifiers. An uncompressed LSTM model provides an average f-score of 80%-94% while requiring only 50--180 KB of storage (depending on the breathing gesture). The resulting inference can be done on smartphones and smartwatches within approximately 7--10 ms and 18--66 ms respectively, thereby making them suitable for resource-constrained devices. Further memory and computational savings can be achieved using model compression methods such as weight quantization and fully connected layer factorization: in particular, a combination of quantization and factorization achieves 25%--55% reduction in LSTM model size, with almost no loss in performance. We also compare the performance on GPUs and show that the use of GPU can reduce the inference time of LSTM models by a factor of 300%. These results provide a practical way to deploy breathing based biometrics, and more broadly LSTM-based classifiers, in future ubiquitous computing applications.