The Burden of Stabilisation on Provinces and its Implications for the Social Sectors

An agenda of economic reform encompassing a broad range of structural adjustment policies (SAP) is underway in Pakistan since 1987-88. These policies have an adverse impact on the pace of economic growth and created more poverty and inequality in the country [see Bengali and Ahmed (2002); Kemal (2003)]. These studies argues that during the last fifteen years each government is trying to stabilise the economy even at the cost of economic growth and delivery of social services. The negative impact of stabilisation policies on economic growth of the country is reflected in the decline of GDP growth from an average annual growth of 4.6 percent during 1990s as compared to 6.5 percent during 1980s. Similarly, negligence of social services delivery is reflected in the recent UNDP Report (2003), which, show that the ranking of Pakistan has slipped from 136 to 141 along with the decline in many other social sector statistics. The top government officials now also recognise these facts and the relapse of growth oriented policy can be heard more often. Trend in public finance statistics of the country clearly indicate that one of the important victim of stabilisation policies are the expenditures of provincial governments. In last several years the significant portion of onus of containment of fiscal deficit has been shifted towards the provincial governments. The onus of containment of fiscal deficit by all four provincial governments during the last decade has increased from 18 percent to 50 percent, which has devastating impact on the service provision and poverty reduction.

Compressive Sensing for Speech Signals in Mobile Systems

Compressive sensing is an emerging and revolutionary technology that strongly relies on the sparsity of the signal. In compressive sensing the signal is sparsely compressively sampled by taking a small number of random projections of the signal, which contain most of the salient information. Compressive sensing has been previously applied in areas like: image processing, radar systems and sonar systems. This research work will discuss the potential implementation of compressive sensing in mobile communication systems and how it will influence their data rates. In a typical mobile communication system, the signal of interest is sampled at least at the Nyquist rate. The Nyquist sampling theorem states that the frequency used to sample a signal should be at least twice the maximum frequency contained within the signal. However, this is not the most efficient way to compress the signal, as it places a lot of burden in sampling the entire signal while only a small percentage of the transform coefficients are needed to represent it. The recent results in compressive sampling (also known as compressive sensing) provide a new way to reconstruct the original signal with a minimal number of observations. In compressive sensing the significant information about the signal/image is directly acquired, rather than acquiring the significant information that will be eventually thrown away. The goal of this research is to propose a new mobile communication system which employs compressive sampling to compress the speech signal at the transmitter and decompress it at the receiver. The expected results from the proposed system will be an increment in the data rates of these systems. In order to simulate how compressive sensing could be applied, a small speech signal was recorded in MATLAB. The signal at the transmitter is then multiplied by the measurement matrix which in this case is composed of randomly generated numbers. The measurement matrix is chosen in such a way that the sparse signal can be exactly recovered at the receiver using one of the different optimization techniques available. Once the signal has gone through the process of compressive sampling, it is ready to be transmitted through the mobile system. The transmitted signal is then reconstructed by the receiver from a significantly small number of samples by using any of the multiple optimization techniques available. The algorithm is simulated in MATLAB. The results show that if a threshold window is applied to the transmitted speech signal and the length of the signal is kept constant, the compression rate of the speech signal is increased

Enhancing scene text recognition with visual context information

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g. a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct andidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin and not unkind. We address this problem by drawing on successful developments in natural language processing and machine learning, in particular, learning to re-rank and neural networks, to present post-process frameworks that improve state-of-the-art text spotting systems without the need for costly data-driven re-training or tuning procedures. Discovering the degree of semantic relatedness of candidate words and their image context is a task related to assessing the semantic similarity between words or text fragments. However, semantic relatedness is more general than similarity (e.g. car, road, and traffic light are related but not similar) and requires certain adaptations. To meet the requirements of these broader perspectives of semantic similarity, we develop two approaches to learn the semantic related-ness of the spotted word and its environmental context: word-to-word (object) or word-to-sentence (caption). In the word-to-word approach, word embed-ding based re-rankers are developed. The re-ranker takes the words from the text spotting baseline and re-ranks them based on the visual context from the object classifier. For the second, an end-to-end neural approach is designed to drive image description (caption) at the sentence-level as well as the word-level (objects) and re-rank them based not only on the visual context but also on the co-occurrence between them. As an additional contribution, to meet the requirements of data-driven ap-proaches such as neural networks, we propose a visual context dataset for this task, in which the publicly available COCO-text dataset [Veit et al. 2016] has been extended with information about the scene (including the objects and places appearing in the image) to enable researchers to include the semantic relations between texts and scene in their Text Spotting systems, and to offer a common evaluation baseline for such approaches.Aquesta tesi aborda el problema de millorar els sistemes de reconeixement de text, que permeten detectar i reconèixer text en imatges no restringides (per exemple, un cartell al carrer, un anunci, una destinació d’autobús, etc.). L’objectiu és millorar el rendiment dels sistemes de visió existents explotant la informació semàntica derivada de la pròpia imatge. La idea principal és que conèixer el contingut de la imatge o el context visual en el que un text apareix, pot ajudar a decidir quines són les paraules correctes. Per exemple, el fet que una imatge mostri una cafeteria fa que sigui més probable que una paraula en un rètol es llegeixi com a Dunkin que no pas com unkind. Abordem aquest problema recorrent a avenços en el processament del llenguatge natural i l’aprenentatge automàtic, en particular, aprenent re-rankers i xarxes neuronals, per presentar solucions de postprocés que milloren els sistemes de l’estat de l’art de reconeixement de text, sense necessitat de costosos procediments de reentrenament o afinació que requereixin grans quantitats de dades. Descobrir el grau de relació semàntica entre les paraules candidates i el seu context d’imatge és una tasca relacionada amb l’avaluació de la semblança semàntica entre paraules o fragments de text. Tanmateix, determinar l’existència d’una relació semàntica és una tasca més general que avaluar la semblança (per exemple, cotxe, carretera i semàfor estan relacionats però no són similars) i per tant els mètodes existents requereixen certes adaptacions. Per satisfer els requisits d’aquestes perspectives més àmplies de relació semàntica, desenvolupem dos enfocaments per aprendre la relació semàntica de la paraula reconeguda i el seu context: paraula-a-paraula (amb els objectes a la imatge) o paraula-a-frase (subtítol de la imatge). En l’enfocament de paraula-a-paraula s’usen re-rankers basats en word-embeddings. El re-ranker pren les paraules proposades pel sistema base i les torna a reordenar en funció del context visual proporcionat pel classificador d’objectes. Per al segon cas, s’ha dissenyat un enfocament neuronal d’extrem a extrem per explotar la descripció de la imatge (subtítol) tant a nivell de frase com a nivell de paraula i re-ordenar les paraules candidates basant-se tant en el context visual com en les co-ocurrències amb el subtítol. Com a contribució addicional, per satisfer els requisits dels enfocs basats en dades com ara les xarxes neuronals, presentem un conjunt de dades de contextos visuals per a aquesta tasca, en el què el conjunt de dades COCO-text disponible públicament [Veit et al. 2016] s’ha ampliat amb informació sobre l’escena (inclosos els objectes i els llocs que apareixen a la imatge) per permetre als investigadors incloure les relacions semàntiques entre textos i escena als seus sistemes de reconeixement de text, i oferir una base d’avaluació comuna per a aquests enfocaments

Visual Re-ranking with Natural Language Understanding for Text Spotting

Many scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and vision. We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text. For this, we initially rely on an off-the-shelf deep neural network, already trained with a large amount of data, which provides a series of text hypotheses per input image. These hypotheses are then re-ranked using word frequencies and semantic relatedness with objects or scenes in the image. As a result of this combination, the performance of the original network is boosted with almost no additional cost. We validate our approach on ICDAR'17 dataset.Comment: Accepted by ACCV 2018. arXiv admin note: substantial text overlap with arXiv:1810.0977

Visual re-ranking with natural language understanding for text spotting

The final publication is available at link.springer.comMany scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and vision. We propose a post processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text. For this, we initially rely on an off-the-shelf deep neural network, already trained with large amount of data, which provides a series of text hypotheses per input image. These hypotheses are then re-ranked using word frequencies and semantic relatedness with objects or scenes in the image. As a result of this combination, the performance of the original network is boosted with almost no additional cost. We validate our approach on ICDAR'17 dataset.Peer ReviewedPostprint (author's final draft

Visual Semantic Re-ranker for Text Spotting

Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic correlation between text and its surrounding visual context. In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene. We initially rely on an off-the-shelf deep neural network that provides a series of text hypotheses for each input image. These text hypotheses are then re-ranked using the semantic relatedness with the object in the image. As a result of this combination, the performance of the original network is boosted with a very low computational cost. The proposed framework can be used as a drop-in complement for any text-spotting algorithm that outputs a ranking of word hypotheses. We validate our approach on ICDAR'17 shared task dataset

Semantic relatedness based re-ranker for text spotting

Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta–airplane, or quarters–parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.Peer ReviewedPostprint (author's final draft

The Burden of Stabilisation on Provinces and Its Implications for the Social Sectors

An agenda of economic reform encompassing a broad range of structural adjustment policies (SAP) is underway in Pakistan since 1987-88. These policies have an adverse impact on the pace of economic growth and created more poverty and inequality in the country [see Bengali and Ahmed (2002); Kemal (2003)]. These studies argues that during the last fifteen years each government is trying to stabilise the economy even at the cost of economic growth and delivery of social services. The negative impact of stabilisation policies on economic growth of the country is reflected in the decline of GDP growth from an average annual growth of 4.6 percent during 1990s as compared to 6.5 percent during 1980s. Similarly, negligence of social services delivery is reflected in the recent UNDP Report (2003), which, show that the ranking of Pakistan has slipped from 136 to 141 along with the decline in many other social sector statistics. The top government officials now also recognise these facts and the relapse of growth oriented policy can be heard more often. Trend in public finance statistics of the country clearly indicate that one of the important victim of stabilisation policies are the expenditures of provincial governments. In last several years the significant portion of onus of containment of fiscal deficit has been shifted towards the provincial governments. The onus of containment of fiscal deficit by all four provincial governments during the last decade has increased from 18 percent to 50 percent, which has devastating impact on the service provision and poverty reduction

Supergravity inflation on a brane

We discuss supergravity inflation in braneworld cosmology for the class of potentials $V(\phi)=\alpha \phi^n\rm{exp}(-\beta^m \phi^m)$ with $m=1,~2$. These minimal SUGRA models evade the $\eta$ problem due to a broken shift symmetry and can easily accommodate the observational constraints. Models with smaller $n$ are preferred while models with larger $n$ are out of the $2\sigma$ region. Remarkably, the field excursions required for $60$ $e$-foldings stay sub-planckian $\Delta\phi <1$.Comment: 10 pages, 4 figure

Impact of contemporary rebuilding process on changing architectural genotype

The rapid economical, socio-cultural changes in Sulaymaniyah city, Iraq in the last three decades promoted radical changes on both urban and architectural level. Several traditional houses in the historical center of the city have been demolished and replaced with rebuilt modern houses leaving negative impacts on the old fabric at both formal and spatial level. This paper aims to investigate the role of the contemporary rebuilding process achieved by landowners within the traditional neighborhoods of the city on changing the underlying genotype constants of housing spatial configuration through examining the morphological characteristics of the architectural layouts of both original and rebuilt type. To achieve this aim five traditional houses’ plans built from (1900-1960) were selected to compare with five modern rebuilt houses (1990-2022) within the same neighborhoods, their spatial arrangements have been compared following analytical quantitative methodology using (A-graph software) as one of space syntax techniques also known as (Gamma analysis) to determine the characteristics of houses layouts in terms of (Symmetry/Assymmetry) and (Distributness/Non Distributness) of the whole system. Results suggest existence of different structuring modes based on genotype distinction despite similarities in some organizational principles