Miracle-dz/UniFAR-FTUs huggingface dataset:
Quarterly Economic Commentary, Summer 2019

While a number of international concerns continue to cast a shadow on the domestic economy, both taxation receipts and labour market indicators suggest that the Irish economy continues to perform strongly in 2019. Output is still forecast to grow by 4.0 per cent in 2019 before moderating somewhat at 3.2 per cent in 2020. Unemployment is set to fall to 4.5 per cent by the end of the present year and to 4.1 per cent at the end of the next year. All forecasts, unless otherwise stated, maintain the Commentary’s baseline assumption that the trading status of the United Kingdom remains equivalent to that of a full European Union Member State.
A Study on the Effects of Soft Interference 1 JUN 2 1 20 Cancellation for Uplink WCDMA system

WCDMA system is an interference limited system. Interference cancellation (IC) is a technique that has been widely studied and implemented for WCDMA uplink system to achieve performance close to potential capacity. Most studies until now have been focused on hard interference cancellation. Hard IC occurs after each user has its cyclic redundancy check (CRC) passed, meaning it would not perform cancellation until the end of a transmission time interval(TTI), therefore causing potential delays in cancellation. Recently engineers have proposed a soft IC scheme where one would perform cancellation without passing CRC, therefore one would not have as much delay on cancellation but at the cost of lower cancellation efficiency because of bad estimation on signals. In my thesis I will attempt to build a framework that gives some insight to the tradeoff between hard and soft IC to serve as a reference for engineers when implementing IC into our WCDMA systems. M.I. T Thesis Supervisor: Lizhong Zheng Title: Associate Professor, MIT VI-A Company Thesis Supervisor: Mingxi Fan Title: Senior Director of Engineering, Qualcomm VI-A Company Co-Thesis Supervisor: Yisheng Xue Title: Senior Staff Engineer/Manager, Qualcomm VI-A Company Co-Thesis Supervisor: Jiye Liang Title: Senior Engineer, Qualcomm
ANALISIS PERKEMBANGAN SOSIAL-EMOSIONAL TERCAPAI SISWA USIA DASAR

Penelitian ini bertujuan (1) untuk mengetahui perkembangan sosial-emosional siswa di dalam pembelajaran (2) untuk mengetahui perkembangan sosial-emosional siswa diluar pembelajaran (3) untuk mengetahui upaya guru dalam mengembangkan sosial-emosional siswa. Penelitian ini menggunakan metode penelitian kualitatif, pengumpulan data digunakan dengan observasi, dokumentasi dan wawancara kepada guru kelas V. Kesimpulan Analisis data sebagai berikut : pertama, perkembangan sosial-emosional siswa di dalam pembelajaran yang tercapai, yaitu siswa menunjukan sikap Partisipasi, Komunikasi dan Interaksi, Mampu menyesuaikan diri dengan kelompok belajar, Menunjukan rasa percaya diri. Kedua, perkembangan sosial-emosional siswa di luar pembelajaran yang tercapai, yaitu siswa menunjukan sikap membantu teman lain saat membutuhkan pertolongan, tidak memaksakan kehendak sendiri, mampu menyelesaikan masalah saat bermain, dapat mengontrol emosi saat bermain. Ketiga upaya guru dalam mengembangkan sosial-emosional peserta didik yaitu sebagai berikut: menciptakan hubungan perkembangan sosial-emosional yang baik terhadap siswa, bersikap sebagai figur yang harus dicontoh oleh siswa, memberikan bimbingan, arahan, untuk mendorong tercapainya perkembangan sosial-emosional.
Exploiting cost-performance tradeoffs for modern cloud systems

The trade-off between cost and performance is a fundamental challenge for modern cloud systems. This thesis explores cost-performance tradeoffs for three types of systems that permeate today’s clouds, namely (1) storage, (2) virtualization, and (3) computation. A distributed key-value storage system must choose between the cost of keeping replicas synchronized (consistency) and performance (latency) or read/write operations. A cloud-based disaster recovery system can reduce the cost of managing a group of VMs as a single unit for recovery by implementing this abstraction in software (instead of hardware) at the risk of impacting application availability performance. As another example, run-time performance of graph analytics jobs sharing a multi-tenant cluster can be made better by trading of the cost of replication of the input graph dataset stored in the associated distributed file system. Today cloud system providers have to manually tune the system to meet desired trade-offs. This can be challenging since the optimal trade-off between cost and performance may vary depending on network and workload conditions. Thus our hypothesis is that it is feasible to imbue a wide variety of cloud systems with adaptive and opportunistic mechanisms to efficiently navigate the cost-performance tradeoff space to meet desired tradeoffs. The types of cloud systems considered in this thesis include key-value stores, cloud-based disaster recovery systems, and multi-tenant graph computation engines. Our first contribution, PCAP is an adaptive distributed storage system. The foundation of the PCAP system is a probabilistic variation of the classical CAP theorem, which quantifies the (un-)achievable envelope of probabilistic consistency and latency under different network conditions characterized by a probabilistic partition model. Our PCAP system proposes adaptive mechanisms for tuning control knobs to meet desired consistency-latency tradeoffs expressed in terms in service-level agreements.
Capturing Body Language in Engineering Design – Tools and Technologies

This paper presents an attempt to make three contributions to engineering design literature on the topic of body language. Firstly, through a brief overview of existing work on the role of body language in engineering design, we propose the need for alternative tools and technologies to manual video coding. Manual video coding is time and resource consuming, and we believe that certain parts of data collection and analysis could be automated. Secondly, common tools for body language analysis not limited to engineering design is presented. These are manual video coding, vision-based motion capture, reflector-based motion capture, and inertial sensor-based motion capture. Each is presented together with a discussion of strengths and limitations, and potentially relevant use cases. Lastly, a pilot study regarding the application of a few, simple inertia-based sensors to recognise gesturing activity is shown. Wrist-mounted accelerometers were used to measure gesturing activity. This activity was compared to video material of the test subjects. Results from the pilot indicates that acceleration above a certain threshold could be linked to gesturing activity.
Application of an informal program verification method to Ada

The QDA informal program verifcation method is a comments analysis technique in which an analyst's assumptions about a program are expressed in the form of structured comments in the program and are checked by an analyzer. Previous work has shown QDA to be effective for detecting errors in assembly language programs. An experiment was performed to determine how well QDA would scale to high-level languages. The implementation and use of a prototype analyzer for Ada indicated both the usefulness of QDA for high level languages and the desirability for further development of the prototype. <<ETX>>
Differential Bank Behaviors Around the Dodd-Frank Act Size Thresholds

Abstract The Dodd–Frank Act created differential regulatory requirements for banks above specified asset size thresholds. Event study results imply greater expected net regulatory costs for above-threshold banks. Consistent with hypotheses that near-below-threshold banks alter their behavior to attempt to avoid or delay the regulatory costs and/or to ensure growth that they do experience is highly beneficial, we find that near-below-threshold banks grow assets, risk-weighted assets, and total loans more slowly, and charge higher rates on commercial loans. The results suggest that the Dodd–Frank Act created costs that near-below-threshold banks attempt to avoid by altering their behaviors in economically important ways.
Real-time iris recognition by a rotation spreading neural network

We previously proposed a rotation spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The characteristic of the R-SAN net is to spread the information of axis orientation of the object on surrounding space by spreading weight that has similar tuning characteristics to axis orientation neurons in the parietal cortex. The R-SAN net is suitable for the shape and orientation recognition of a concentric circular pattern, because it uses polar conversion. Previously, the validity of this neural net for learning and recollection of human faces had been demonstrated by off-line processing. In the present study, we used iris images instead of face images for the input patterns of the neural network. We attempted real-time individual identification by the iris patterns. In the recognition experiment, the R-SAN net was able to simultaneously recognize the orientation and shape of iris images of learned persons. We suggest application of the R-SAN net as an iris recognition system.
An Improved Zigbee Routing Algorithm Based on Energy Level Partition

Based on the analysis of large temperature and humidity monitoring system, this paper proposes an improved AODVjr routing algorithm based on node energy level classification. The algorithm is based on the traditional AODVjr algorithm, the tree routing algorithm of node forwarding rules based on parent-child relationship to optimize the network layer treatment, so as to avoid invalid routing and redundant routing of traditional tree routing algorithm, improves the routing efficiency, save the overall energy consumption of the network The simulation results of the improved AODVjr routing algorithm based on node energy grade are simulated by NS2 software. The simulation results show that the new routing algorithm proposed in this paper is effective and feasible.
Visual Monitoring of Photovoltaic Systems

We developed a scalable visual analytics framework for managing and analyzing hundreds of geographically distributed photovoltaic systems. These systems deliver performance parameters at different levels as well as environmental parameters. Our web-based framework gathers the data in a central server and allows comparisons of production values across time and photovoltaic systems, analysis of component failures and reporting errors. It is in daily use by a regional energy provider for over one year and its capabilities for fluent interaction and versatile configuration of visualizations from overviews to performance parameters of detailed components of individual systems are highly appreciated.
Supervised and Active Learning for Recommender Systems

Supervised and Active Learning for Recommender Systems Laurent Charlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 Traditional approaches to recommender systems have often focused on the collaborative filtering problem: using users’ past preferences in order to predict their future preferences. Although essential, rating prediction is only one of the components of a successful recommender system. One important problem is how to translate predicted ratings into actual recommendations. Furthermore, considering additional information either about users or items may offer substantial gains in performance while allowing the system to provide good recommendations to new users. We develop machine learning methods in response to some of the limitations of current recommendersystems’ research. Specifically, we propose a three-stage framework to model recommender systems. We first propose an elicitation step which serves as a way to collect user information beneficial to the recommendation task. In this thesis we framed the elicitation process as one of active learning. We developed several active elicitation methods which, unlike previous approaches which exclusively focus improving the learning model, directly aim at improving the recommendation objective. The second stage of our framework uses the elicited user information to inform models that predict user-item preferences. We focus user-preference prediction for a document recommendation problem for which we introduce a novel graphical model over the space of user side-information, item (document) contents, and user-item preferences. Our model is able to smoothly tradeoff its usage of side information and of user-item preferences to make good document recommendations in both cold-start and non-coldstart data regimes. The final step of our framework consists of the recommendation procedure. In particular, we focus on a matching instantiation and explore different natural matching objectives and constraints for the paperto-reviewer matching problem. Further, we explore and analyze the synergy between the recommendation objective and the learning objective. In all stages of our work we experimentally validate our models on a variety of datasets from different domains. Of particular interest are several datasets containing reviewer preferences about papers submitted to conferences. These datasets were collected using the Toronto Paper Matching System, a system we built to help conference organizers in the task of matching reviewers to submitted papers.
When a robot is social: Spatial arrangements and multimodal semiotic engagement in the practice of social robotics

Social roboticists design their robots to function as social agents in interaction with humans and other robots. Although we do not deny that the robot’s design features are crucial for attaining this aim, we point to the relevance of spatial organization and coordination between the robot and the humans who interact with it. We recover these interactions through an observational study of a social robotics laboratory and examine them by applying a multimodal interactional analysis to two moments of robotics practice. We describe the vital role of roboticists and of the group of preverbal infants, who are involved in a robot’s design activity, and we argue that the robot’s social character is intrinsically related to the subtleties of human interactional moves in laboratories of social robotics. This human involvement in the robot’s social agency is not simply controlled by individual will. Instead, the human–machine couplings are demanded by the situational dynamics in which the robot is lodged.
Towards a dynamic systems approach to the development of language and music - Theoretical foundations and methodological issues

Music and language are considered as distinct auditory systems that serve different communicative uses. From the perspective of infants rather inexperienced with their native musical and linguistic systems, these differences are less apparent. One of the enduring puzzles related to human cognitive development is the question of how children acquire these complex systems with such effortlessness and speed, and how developmental change can be explained. Dynamic systems theory (DST) can make significant contributions to our understanding of cognitive development, and has already been successfully applied to first and second language development. By drawing together findings from various scientific fields, we will argue that our understanding of musical and linguistic development can be crucially enhanced by i) cross-domain research, ii) applying DST and iii) new methodologies in empirical research. Language and music are seen as complex non-linear dynamical systems that may interact at various stages of development, such as in syntactic development. Major developmental shifts occur around the same time up to the age of four in both systems. DST also has potential to shed new light on common methodological issues in developmental research: intra-subject variability in behavioural data receives a positive meaning and is not considered as noise but as sound.
Remote Sensing Image Classification Based on Support Vector Machines

Support Vector Machine(SVM) is a state-of-the-art machine learning algorithm based on statistical learning theory.It tries to find the optimal classification hyperplane in high dimensional feature space to handle complicated classification and regression problems by solving optimization problems.The SVM theory applied to remote sensing image classification is still in the initial stage.Traditional algorithms used in remote sensing image classification have some problems such as low computing rate,low accuracy and much difficulty for convergence.According to SVM theory,the classification model based on SVM was constructed.By experimenting with remote sensing data and comparing the resules with others,the results indicate that the radial basis kernel function of SVM has the highest accuracy.SVM classifier has more advantages in the classification in contrast with radial basis function neural network classifier and maximum likelihood classifier.As the in-depth research on SVM continues,it will be more widely used in remote sensing image classification.
Automatic acquisition of emotional vocabulary based on syntax

Affective computation has received more and more attentions in the field of artificial intelligence;however,the calculation of affective lexicon ontology is a requirement for affective computation of texts.At present,most emotional lexicons are obtained by manual methods.The automatic acquisition of emotional lexicons has become an urgent task that needs to be addressed.This paper presents an automatic acquisition method for emotional lexicons.The authors analyzed the general syntactical rules of emotional lexicons,such as the rules of overlapping words,then rules governing how these acquired emotional words were modified by privatives and degree adverbs.Then we used the conditional random fields(CRF) model to acquire emotional words based on the general rules.Finally,we analyzed the effects of various syntax rules on the automatic acquisition of emotional vocabulary.Experiments were done and the results showed that the proposed method is effective for automatic acquisition of emotional words.
Space and time registration of vehicle-borne 3D measurement system based on muti-sensor fusion

In the vehicle-borne 3D measurement system,in order to acquire 3D space information needed by digital city quickly,in real-time and completely,global positioning system(GPS),laser scanner(LS) and multiple sensors so on are integrated.The Multi-sensor fusion theory is applied in these sensors'data processing.Space registration and time registration are keys to fuse multi-sensor's data information effectively.Problems of multi-sensor's space registration and time registration are solved via computing the coordinate relationship among these sensors and using LS's time record and GPS's time marker.Presently it is successfully applied to close range target 3D measurement technology.The expected purposes are achieved and the experimental results are demonstrated.
SPEST - A TOOL FOR SPECIFICATION-BASED TESTING

SPEST A Tool for Specification-Based Testing Corrigan Johnson This thesis presents a tool for SPEcification based teSTing (SPEST). SPEST is designed to use well known practices for automated black-box testing to reduce the burden of testing on developers. The tool uses a simple formal specification language to generate highly-readable unit tests that embody best practices for thorough software testing. Because the specification language used to generate the assertions about the code can be compiled, it can also be used to ensure that documentation describing the code is maintained during development and refactoring. The utility and effectiveness of SPEST were validated through several experiments conducted with students in undergraduate software engineering classes. The first experiment compared the understandability and efficiency of SPEST generated tests against student written tests based on the Java Modeling Language (JML)[25] specifications. JML is a widely used language for behavior program specification. A second experiment evaluated readability through a survey comparing SPEST generated tests against tests written by well established software developers. The results from the experiments showed that SPEST’s specification language is at least understandable as JML, SPEST’s specification language is more readable than JML, and strongly suggest that SPEST is capable of reducing the effort required to produce effective tests.
A restoration algorithm for rain video image

In this paper,the spatio-temporal property and the chromatic property of rain in the video are analyzed.Based on the two properties,a simple and effective algorithm is proposed to detect and remove the rain in the video by using sequential image.Firstly the time complexity of K-means is optimized by the algorithm.Then each pixel is classified by the optimized K-means according to the spatio-temporal property of rain and the rain parts are identified accurately according to the chromatic property of rain for the rain detection.For the rain removal,the color of streak is replaced by the proportion-blending color of the rain's color and the background's color based on the statistic characteristics.And for achieving a clearer restoration result,the original images are preprocessed before the detection and filtering is carried out for the no-rain parts after rain removal.Experimental results prove that the proposed algorithm is simple and effective.
Implementación del algoritmo VNS-DEEPSO para el despacho de energía en redes distribuidas inteligentes

Introducción: Las redes eléctricas tradicionales están migrando a nuevas configuraciones de redes inteligentes, que traen retos operacionales y de planeación. Con miras a avanzar en estos retos se propone resolver un problema de optimización usando programación en elementos de redes distribuidas inteligentes. Objetivo: El problema de optimización consiste en administrar el despacho energético de una red inteligente para optimizar los recursos disponibles, considerando la incertidumbre de energías renovables, viajes planeados de vehículos eléctricos, el pronóstico de carga y los precios del mercado. Metodología: Se propuso utilizar un ensamble entre dos métodos heurísticos. El algoritmo VNS (Variable Neighborhood Search) y el DEEPSO (Differential Evolutionary Particle Swarm). Resultados: El algoritmo VNS-DEEPSO fue evaluado en una competencia de “Smart Grids” con otros algoritmos con un valor de 18.21, siendo 7 % mejor que el segundo algoritmo clasificado en la competencia. Conclusiones: El algoritmo VNS-DEEPSO fue ganador entre 9 algoritmos metaheurísticos que solucionaron el problema, este problema tenía un mayor incremento de dificultad debida a la incertidumbre generada por factores ambientales, pronóstico de carga, viajes en vehículos eléctricos y el mercado de precios. Acorde a los resultados, el algoritmo VNS-DEEPSO demostró ser el más eficiente en minimizar los costos operacionales y maximizar los ingresos de la red inteligente.
Review on Proniosome-a Novel Approach to Vesicular System

INTRODUCTION Vesicular systems have been receiving a lot of interest as a carrier for advanced drug delivery. Encapsulation of the drug in vesicular structures is one such system, which can be expected to prolong the duration of the drug in systemic circulation, and to reduce the toxicity by selective up taking. In the ensuing years, great strides were made toward understanding the way in which vesicular systems interact with the biological milieu at the molecular and cellular level [1]. Proniosome technology offers novel solution for poorly soluble drugs. Proniosome is a dry free flowing, granular product that could be hydrated immediately before use and would avoid many of the problems associated with aqueous niosome dispersions and problem of physical stability [2].