Miracle-dz/UniFAR-FTUs huggingface dataset:
Fingers and Angles

Smartwatches present a unique touch input context: small, fixed to one wrist and approachable from a limited range of angles by the touching hand. Techniques to expand their input expressivity often involve variations in how a watch must be touched, such as with different fingers, poses or from specific angles. While objective performance with such systems is commonly reported, subjective qualities such as comfort remain overlooked. We argue that techniques that involve uncomfortable input will be of limited value and contribute the first data on the comfort of input on smartwatches via two studies that combine subjective ratings of comfort with objective performance data. We examine both static and dynamic touches and three finger poses. Based on the study results, we contribute a set of design recommendations for comfortable, effective smartwatch input. We close by instantiating the recommendations in interface prototypes that we evaluate in a final qualitative study.
A Comparison of Three Popular Source code Similarity Tools for Detecting Student Plagiarism

This paper investigates automated code plagiarism detection in the context of an undergraduate level data structures and algorithms module. We compare three software tools which aim to detect plagiarism in the students' programming source code. We evaluate the performance of these tools on an individual basis and the degree of agreement between them. Based on this evaluation we show that the degree of agreement between these tools is relatively low. We also report the challenges faced during utilization of these methods and suggest possible future improvements for tools of this kind. The discrepancies in the results obtained by these detection techniques were used to devise guidelines for effectively detecting code plagiarism.
EIS

American Sign Language (ASL) is widely used among hearing impaired individuals in English-speaking countries. Various technologies have been developed to perform ASL recognition, including optical signal sensing, electrical signal sensing, and mechanical signal sensing. However, wearable devices using those methods have bulky and complex sensing modules that lead to long-term discomfort as well as poor accuracy. In this paper, we present an epidermal-iontronic sensing (EIS)-based wearable device that wears on finger joints for 35 fingerspelling ASL recognitions (i.e., 26 alphabets from A to Z and 9 digits from one to nine). Compared to current on-market devices, such design is lighter, comfortable to wear and has better appearance according to user comments. When bending the finger, a physical contact forms between the ionic material and the epidermis of skin, leading to an electric double layer (EDL) established at the interface. Therefore, a significant capacitive change can be achieved with various finger gestures. By using Nafion as the ionic sensing material, we developed a sensing device to provide excellent flexibility and optical transparency. We used machine learning methods, such as neural networks to track and perform ASL recognition using the signals obtained from the designed device. The algorithm achieved a within-user accuracy of 99.6% and a cross-user accuracy of 76.1% when adapted the model to different users. This wearable device is low-cost and has broad potential to be integrated in future application of human-machine interactions (HMI), smart home controls, and nonverbal communications.
Detecting Door Events Using a Smartphone via Active Sound Sensing

Event detection of indoor objects, including doors, has a wide variety of applications, including intruder detection, HVAC control, and surveillance of independently living elderly people. Hence, this has been the focus of multiple research projects in the UbiComp research community. Herein, we propose a method to accurately detect door events in an indoor environment, without the installation and maintenance costs of using distributed ubiquitous sensors. In particular, we recognize the events of multiple doors existing in the environment via active sound probing using a disused smartphone installed in the environment. We perform event recognition by fusing the analysis of the Doppler shift caused by the moving doors with the acoustic characteristics describing the open/close states of the doors acquired via impulse response. To accurately distinguish between the events of different doors via sound probing, our method employs the time-series analysis of the Doppler shift as well as the active sound probing using directional high-frequency sine waves and stereo sound recording. In addition, by incorporating prior knowledge about the state transitions of a door object into a recognition model, we attempt to improve the accuracy of event recognition. Moreover, our method is capable of recognizing walking activities of a person related to door events in the environment, which are necessary information for applications such as HVAC control that require information about both door events and human presence.
The Nuts and Bolts of Deploying Process-Level IDS in Industrial Control Systems

Much research effort has recently been devoted to securing Industrial Control Systems (ICS) in response to the increasing number of adverse incidents targeting nation-wide critical infrastructures. Leveraging the static and regular nature of the behavior of control systems, various data-driven methods that monitor the process-level network have been proposed as a defensive measure. Although these methods have been evaluated through offline analysis of ICS-related datasets, in absence of documented live experiments in real environments, a complete and global understanding of the applicability and efficiency of process-level monitoring is still lacking. In this work, we describe our experience of running a fully fledged intrusion detection system in an operational paper factory for 75 days. We discuss the nuts and bolts of running such systems in real environments and underline several practical challenges in meeting ICS-specific requirements. This work essentially aims at bridging the gap between ICS intrusion detection research and practice, and empirically validating the increasingly adopted data-driven approach to process-level monitoring.
A Performance Study of Big Spatial Data Systems

With the accelerated growth in spatial data volume, being generated from a wide variety of sources, the need for efficient storage, retrieval, processing and analyzing of spatial data is ever more important. Hence, spatial data processing system has become an important field of research. In recent times a number of Big Spatial Data systems have been proposed by researchers around the world. These systems can be roughly categorized into Apache Hadoop-based and in-memory systems based on Apache Spark. The available features supported by these systems vary widely. However, there has not been any comprehensive evaluation study of these systems in terms of performance, scalability and functionality. To address this need, we propose a benchmark to evaluate Big Spatial Data systems. Although, Spark is a very popular framework, its performance is limited by the overhead associated with distributed resource management and coordination. The Big Spatial Data systems that are based on Spark, are also constrained by these. We introduce SpatialIgnite, a Big Spatial Data system that we have developed based on Apache Ignite. We investigate the present status of the Big Spatial Data systems by conducting a comprehensive feature analysis and performance evaluation of a few representative systems with our benchmark. Our study shows that SpatialIgnite performs better than Hadoop and Spark based systems that we have evaluated.
Color matrix display simulation based on luminance and chromatic contrast sensitivity of early vision

This paper describes the design and operation of a new simulation model for color matrix display development. It models the physical structure, the signal processing, and the visual perception of static displays, to allow optimization of display design parameters through image quality measures. The model is simple, implemented in the Mathematica computer language, and highly modular. Signal processing modules operate on the original image. The hardware modules describe backlights and filters, the pixel shape, and the tiling of the pixels over the display. Small regions of the displayed image can be visualized on a CRT. Visual perception modules assume static foveal images. The image is converted into cone catches and then into luminance, red-green, and blue-yellow images. A Haar transform pyramid separates the three images into spatial frequency and direction-specific channels. The channels are scaled by weights taken from human contrast sensitivity measurements of chromatic and luminance mechanisms at similar frequencies and orientations. Each channel provides a detectability measure. These measures allow the comparison of images displayed on prospective devices and, by that, the optimization of display designs.
Room-Wide Wireless Charging and Load-Modulation Communication via Quasistatic Cavity Resonance

The rise of the Internet of Things (IoT) has led to a significant increase in the number of connected devices that stream data in our homes, offices and industrial spaces. However, as the number of these devices increases, the costs of actively maintaining and replacing batteries becomes prohibitive at scale. Recent work on Quasistatic Cavity Resonance (QSCR), offers the possibility of seamless wireless power transfer (WPT) to receivers placed anywhere inside large indoor spaces. This work aims to solve two unexplored and critical missing pieces needed to realize this vision of ubiquitous WPT. First, we demonstrate a full end-to-end QSCR-based WPT system that is capable of simultaneously charging multiple custom designed nodes nearly anywhere in the 4.9 m x 4.9 m x 2.3 m test room. Second, this work utilizes the WPT mechanism as a communication channel, where nodes communicate with a centralized reader and to each other via load modulation. Through analysis and experiments, the proposed system shows that 10 receiver nodes can be safely and efficiently wirelessly charged and the end node to end node communication rate can achieve from 1 kbps without occurring any errors, up to 5 kbps with 6% BER while the end node to central unit can achieve 10 kbps without occurring any errors.
Textual data mining applications for industrial knowledge management solutions

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.
Bridget Jones: The Edge of Reason

First published in 1999, The Edge of Reason is the sequel to Helen Fielding's number one best-selling Bridget Jones's Diary. It has been turned into a film starring Renee Zellweger, Colin Firth and Hugh Grant. Bridget Jones: The Edge of Reason is followed by Bridget Jones: Mad About the Boy. The Wilderness Years are over! But not for long. At the end of Bridget Jones's Diary, Bridget hiccuped off into the sunset with man-of-her-dreams Mark Darcy. Now, in The Edge of Reason, she discovers what it is like when you have the man of your dreams actually in your flat and he hasn't done the washing-up, not just the whole of this week, but ever. Lurching through a morass of self-help-book theories and mad advice from Jude and Shazzer, struggling with a boyfriend-stealing ex-friend with thighs like a baby giraffe, an 8ft hole in the living-room wall, a mother obsessed with boiled-egg peelers, and a builder obsessed with large reservoir fish, Bridget embarks on a spiritual epiphany, which takes her from the cappuccino queues of Notting Hill to the palm- and magic-mushroom-kissed shores of . Bridget is back. V.g.
Crossbills (Loxia curvirostra) feeding on cypress seeds

El trencapinyes s'alimenta habitualment de llavors de coniferes pero no existia cap observacio menjant-ne de xiprer. En aquesta nota es presenten dues observacions, una a la tardor de 1989 i l'altra a la primavera de 1994. Aquestes observacions es relacionen amb la davallada, que en aquests dos anys hi va haver en la produccio de llavors de pinassa, la qual es una de les seves principals fonts d'alimentacio en aquesta area.
Models of Lithuanian Grapheme Frequencies

Abstract While the study of grapheme counts dates from ancient Greeks, that of their frequencies dates from the ninth century AD. These studies moved beyond the comparison of means after almost a millennium. Because of their use in cryptanalysis and elsewhere grapheme frequencies continued to be studied. Published work in the area has been concentrating on the more widely spoken languages and comparatively little has been done with minority languages. An alphabet for Lithuanian was first developed in the sixteenth century and finalized in the twentieth. Twenty models of its grapheme frequencies were examined here. A relative simple model was found to be the best fit. This model may also be applicable to other languages.
Clinical profile of dengue infection in a tertiary care hospital

Background: Dengue is a major international health concern that is prevalent in tropical and sub-tropical countries. The total burden of febrile illnesses that presents to primary health facilities due to dengue in different parts of Karnataka is largely unknown. Objective: The objective was to study the different clinical manifestations, trend, and outcome of dengue cases admitted in a tertiary care hospital. Materials and Methods: Prospective study conducted over a period of 6 months (July-December 2013) in a tertiary care hospital at Bangalore. Dengue serology was done for all suspected subjects by enzyme-linked immunosorbent assay method. Detailed clinical and demographic details were taken at admission and serial close monitoring for signs and symptoms was done to watch for the progression of the disease. Results: Of the 100 children admitted with probable dengue fever, 57 had confirmed dengue illness. Of these 57 cases, most of the cases were male, and 34 (60%) cases were in age group of 5 months to 15 years with mean age of 7.5±4.7 years. Among the serology confirmed cases, the most common presentation was fever in 57 (100%), followed by abdominal pain in 37 (65%), rashes in 31 (54%), myalgia in 26 (46%), and vomiting in 23 (40%) cases. The most common hemorrhagic manifestation was cutaneous bleeding manifestations like petechiae. Of 57 cases, 46 patients had dengue with no warning signs, 8 had dengue fever with warning signs, and 3 had severe dengue. Conclusion: Clinical manifestations of dengue seem to be changing. Fever, rash, and body ache are still the common manifestations; however, clinicians in the endemic areas should be aware of unusual and novel presentations such as fulminant hepatic failure, splenomegaly, and myocarditis also. Key words: Clinical manifestations, Dengue, Tertiary care hospital
Computer-Based Learning Using LiveMath for Secondary Four Students

The effect of computer-based learning using LiveMath, an interactive computer algebra system, on Singapore Secondary Four students' conceptual and procedural knowledge of exponential and logarithmic curves, was investigated in this study. Sixty-five students from two middle-ability Express classes in an independent school were taught using a guided discovery approach to explore the characteristics of the graphs of exponential and logarithmic functions. The experimental class used LiveMath for their exploration whereas the control class used worksheets that contained pre-printed graphs. The findings show that the students in the experimental group performed significantly better than the students in the control group in both the conceptual and the procedural knowledge tests.
Proof Theory for Authorization Logic and its Application to a Practical File System

Abstract : In most computer systems, users' access to resources is controlled using authorization policies. Logic is an appropriate medium for representing, understanding, and enforcing authorization policies, yet despite several years of pragmatic work on the subject, the foundations of relevant logics remain unexplored and poorly understood. It is in this realm that the work of this thesis lies; the thesis explores the theory of logics for expressing authorization policies as well as applications of the theory in practice. In doing so, it makes three foundational and technically challenging contributions. First, the thesis introduces proof theory and metatheory in the context of authorization logics, illustrated through a new logic BL. In particular, structural proof-theoretic systems of natural deduction and sequent calculus are investigated and their importance explained. Pragmatic problems like proof verification and automatic proof search are then addressed using the sound foundations of proof theory. Second, the thesis considers a logical treatment of dynamism in authorization policies and, in particular, logical constructs for representing authorizations depending on system state, consumable credentials, and explicit time are presented. Further, a practical, efficient, and provably correct mechanism for their enforcement is developed. The mechanism is based on a combination of proofs and cryptographic capabilities. Third, the practical usefulness of the proof theory and the enforcement mechanism is demonstrated through an implementation of the same in a file system, PCFS. It is shown through measurements that file access in PCFS is very efficient. In addition, the thesis includes a detailed case study that formalizes in BL policies used to control access to classified information in the U.S., and explains how the policies may be enforced using PCFS.
USING HIGH LEVEL PETRI NETS TO MODEL THE INTER-SESSION NETWORK CODING

In this paper, we model an acyclic communication network as a net graph, and we use this net graph to define inter session-network coding as high level Petri graph. We then model inter-session network coding as a set of streaming high level Petri net graphs. Finally, we find a particular binding for the variables sets of these streaming high level Petri graphs in such a way the sink places in the net graph which models the acyclic communication network can be marked with the requested marking. AMS Subject Classification: 94A99
La servitude et le volontaire : les usages politiques du travail invisible dans les parcs de la ville de New York

En s’appuyant sur une recherche sur les transformations de la main-d’œuvre chargee de l’entretien des parcs municipaux a New York, cet article se propose de montrer combien le travail des benevoles d’une part et celui des allocataires de l’aide sociale contraints de travailler d’autre part (workfare), s’averent, depuis la crise budgetaire des annees 1970, necessaires au bon fonctionnement, voire a la survie du departement des parcs et jardins de la ville. Si tout eloigne ces deux categories de travailleurs atypiques qui appartiennent a des classes sociales souvent diametralement opposees et s’inscrivent dans des rapports au travail d’entretien, des formes de reconnnaissance sociale et des regimes de mobilisation pour le moins differents, travailleurs benevoles et workfare workers se ressemblent pourtant dans les usages que la municipalite a pu faire de leur travail, un travail non reconnu comme tel et non remunere, invisibilise, au nom de la citoyennete qu’il exprime ou permettrait de regagner.
Kilivila : The Language of the Trobriand Islanders

The series builds an extensive collection of high quality descriptions of languages around the world. Each volume offers a comprehensive grammatical description of a single language together with fully analyzed sample texts and, if appropriate, a word list and other relevant information which is available on the language in question. There are no restrictions as to language family or area, and although special attention is paid to hitherto undescribed languages, new and valuable treatments of better known languages are also included. No theoretical model is imposed on the authors; the only criterion is a high standard of scientific quality.
URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES

Abstract. Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2 % precision, 58.5 % recall and 73.4 % harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4 %), recall (68.8 %) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.