S. Jabeen

Text to face generation is a sub-domain of text to image synthesis.It has a huge impact on new research areas along with the wide range of applications in the public safety domain.Due to the lack of dataset, the research work focused on the text to face generation is very limited.Most of the work for text to face generation until now is based on the partially trained generative adversarial networks, in which the pre-trained text encoder has been used to extract the semantic features of the input sentence.Later, these semantic features have been utilized to train the image decoder.In this research work, we propose a fully trained generative adversarial network to generate realistic and natural images.The proposed work trained the text encoder as well as the image decoder at the same time to generate more accurate and efficient results.In addition to the proposed methodology, another contribution is to generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset.The dataset has also been labeled according to our defined classes.Through performing different kinds of experiments, it has been proved that our proposed fully trained GAN outperformed by generating good quality images by the input sentence.Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.

Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore, it is plausible that transcription factors can regulate splicing outcomes.We provide evidence for the hypothesis that transcription factors are involved in the regulation of intron retention by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events.This work opens up opportunities for further studies that elucidate the mechanisms by which transcription factors affect intron retention and other forms of splicing.Source code available at https://github.com/fahadahaf/chromir.

Abstract: Significant progress has been made in sentiment analysis in recent decades, with a focus on textual data. Nonetheless, the scientific community has not done a great deal to investigate sentiment analysis in the context of audio. By applying a novel method of sentiment analysis to voice transcripts and concentrating on the subtle interpretation of emotions sent by distinct speakers during conversations, this study seeks to close this gap in knowledge. The main goal of this suggested research paper is to create an advanced sentiment analysis system that can communicate with several users in a seamless manner while identifying and assessing the emotional content that each user is conveying through their audio inputs. Advanced approaches like Recurrent Neural Networks (RNN), Long Short Term Memory(LSTM), Teacher Forcing , Encoder- Decoder Model, Tokenization, Gated Recurrent Units (GRU),(Bidirectional Encoder Representations from Transformers),Gradient Boosting Machine.

The rapid development of multimedia technologies gave birth to large video data that demands effective and fast video summarization methods along with ability to assure the authenticity of digital data. Video summarization provides consolidated view of original video in short and compact form of a video. The summarized videos should be transmitted to remote IoT devices through secure channels. To prevent data manipulation and to verify the transmitted data, we exploited the blockchain technology. This paper focuses on different steps of keyframe extraction from a video followed by secure and trusted transmission to the consumers. The proposed system enables the users to summarize a video based on human and objects as parameters. Cryptographic hashes are used with blockchain, hashes are generated from summarized video blocks, signed and transmitted through blockchain. Cumulus blockchain technique is utilized to ensure the video integrity. System allows remote users to get tamper-proofed summarized video of their business sites or any sensitive premises on their smartphones.

This review efficiently covers the research progress in the area of polymer bio composites in perspective of the modern-day renewable materials.In the last decade, attraction towards the bio-composite based systems has been the topic of interest due to their potential as a substitute of conventional materials produced in important manufacturing industries.Recently, preparation of biocompatible and biodegradable polymer composites is an important achievement as an alternative of petrochemical based renewable products.Successful production of eco-friendly bio-composite materials have been achieved with natural fibers viz jute, bamboo, hair, flex, wool, silk and many others instead of synthesized fibers like carbon, glass dispersed in synthesized resins viz poly vinyl alcohol, epoxy and etc. Biomaterials based on natural fibers dispersed in natural matrix like natural rubber or polyester have also been obtained with endless applications for the mankind.The utilization of such materials for the good well of mankind is attributed to their ease of disposal and being renewable.The last but not the least, the extraordinary mechanical properties of bio-composites make them superior to many other conventional materials.This review paper addresses the recent trends, mechanical and chemical properties, synthesis, and application of bio-composites in the recent years.

Traditional multimedia forensics techniques inspect images to identify, localize forged regions and estimate forgery methods that have been applied. Provenance filtering is the research area that has been evolved recently to retrieve all the images that are involved in constructing a morphed image in order to analyze an image, completely forensically. This task can be performed in two stages: one is to detect and localize forgery in the query image, and the second integral part is to search potentially similar images from a large pool of images. We propose a multimodal system which covers both steps, forgery detection through deep neural networks(CNN) followed by part based image retrieval. Classification and localization of manipulated region are performed using a deep neural network. InceptionV3 is employed to extract key features of the entire image as well as for the manipulated region. Potential donors and nearly duplicates are retrieved by using the Nearest Neighbour Algorithm. We take the CASIA-v2, CoMoFoD and NIST 2018 datasets to evaluate the proposed system. Experimental results show that deep features outperform low-level features previously used to perform provenance filtering with achieved Recall@50 of 92.8%.

This paper proposes the summarization technique for the multimedia data present in the form of video, over the internet to provide a quick overview of the content present in it. This is very challenging task because finding the significant and useful portion of the video, needs to understand the content present in it. Moreover, the categories of the videos over the wide web are very diverse, like home videos, documentaries and sports videos etc. So, it makes video summarization more tough because of the unavailability of the prior knowledge. Currently, traditional hand crafted features have been utilized for video summarization, which fails to capture the information and content from all the scenes. To tackle this problem, we first find the scene boundaries using motion features. Then we pass the data to our proposed CNN architecture that gives us the frame level importance against each frame present in specific scene. The redundancy of the frames has been removed using the bidirectional LSTM. Experiments have been performed using the publically available dataset TVSUM50. Obtained results show that our proposed methodology outperforms the traditional feature based approaches in terms of relative F measure score.

This review summarizes the recent results for top quark and Higgs boson measurements from experiments at Tevatron, a proton–antiproton collider at a center-of-mass energy of [Formula: see text], and the Large Hadron Collider, a proton–proton collider at a center-of-mass energy of [Formula: see text]. These results include the discovery of a Higgs-like boson and measurement of its various properties, and measurements in the top quark sector, e.g. top quark mass, spin, charge asymmetry and production of single top quark.

In recent times, there has been continuous interest in the area of content based information retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data has triggered a cause for managing, storing and retrieving multimedia contents in convenient and efficient ways. Visual features from static images and dynamic videos are extracted to perform retrieval task. Once visual features are extracted, there is a need to search and retrieve relevant videos in efficient amount of time. This paper makes use of seven visual features; human detection, emotion, age, gender, activity, scene and object detection followed by sentence generation. Furthermore, generated sentence is used in multi-class recurrent neural network (RNN) to find genre of a video for retrieval task. Accuracy, precision and recall are used for evaluation of this framework on self generated dataset. Experiments show that our system is able to achieve high accuracy of 88.13%.

Extreme weather conditions such as heavy rain, fog and scorching heat of sun increase the risk factor of road accidents and traffic congestion. This impels valuable lives and property into tremendous dangers and causes dilatory. Therefore, automatic weather detection on roads for the assistance of drivers becomes very crucial. Weather detection and forecasting is usually figured out by using temperature, humidity and wind sensors. In this paper, we introduce a methodology which exploits visual data to detect the weather condition. We propose a weather detection system based on state-of-the-art deep learning techniques by transferring the learned weights of pre-trained inception v3 model to our problem. Proposed system can be utilized to generate alerts for upcoming vehicle's drivers to change their driving behavior according to the weather condition. The system can also be helpful for Intelligent Transportation System (ITS) authorities so that they can provide road safety to the citizens with efficient way and improve the current ITS. The system is trained using self-generated dataset. Proposed system achieved an accuracy of 98% on unseen dataset, which is very high as compare to prior models.

Background: The pandemic Covid-19 has very impacted the physical, psychological and emotional well-being of the nurses which were directly linked with their performance. The objective of the study was to assess the relationship between the leadership styles of nurse’ managers and their adherence with the organizational commitments and performance during the Covid-19 in Punjab, Pakistan. Methods: The nature of study was cross sectional and quantitative. The study was conducted in five public hospital located in District Lahore, Punjab i.e. Jinnah Hospital, General Hospital, Sheikh Zaid Hospital, Services Hospital and Lady Willingdon Hospital. The participants of the study were nurses who have performed their duty in the Covid-19 ward. Out of 755 nurses, 200 nurses were selected for the interview through convenient sampling technique. The researchers used well-structured research instruments to interview the participants. The collected data was entered and analyzed by using SPSS version 26. The descriptive analysis was employed to measure the demographic characteristics of the participants. T-test and one-way ANOVA were also applied to observe the differences among the variables. At the end, Pearson’s Correlation (bi-variate) were used to measure the extent of relationship between the leadership styles and nurse’ organizational commitments.

Scene recognition is used in many computer vision and related applications, including information retrieval, robotics, real-time monitoring, and event-classification. Due to the complex nature of the task of scene recognition, it has been greatly improved by deep learning architectures that can be trained by utilizing large and comprehensive datasets. This paper presents a scene classification method in which local and global features are used and are concatenated with the DCT-Convolutional features of AlexNet. The features are fed into AlexNet's fully connected layers for classification. The local and global features are made efficient by selecting the correct size of Bag of Visual Words (BOVW) and feature selection techniques, which are evaluated in the experimentation section. We used AlexNet with the modification of adding additional dense fully connected layers and compared its result with the model previously trained on the Places365 dataset. Our model is also compared with other scene recognition methods, and it clearly outperforms in terms of accuracy.

Abstract We have examined the effects caused on the motion and sedimentation of a free falling solid particle by the hydrodynamic forces acting on the particle’s surface arising when particle is close to wall. Drag and lift coefficients for a settling particle inside a narrow domain are calculated. An Eulerian mesh is adopted for computing the motion of free moving solid particles through the domain. The combined particle and fluid mixture is treated with a fictitious boundary method approach. To avoid particle-wall collisions, an approach proposed by Singh, Glowinsk and coauthors is used to handle such interactions. The particulate flow is computed using multigrid finite element solver FEATFLOW (Finite element analysis tool for flow problems). Numerical experiments are performed by decreasing domain widths for a single falling particle. The size and density of the particle is varied to inspect the particle paths. The behavior of the particle and its interaction with wall while it is moving inside constricted domains is analyzed. Results for the drag and lift forces on the surface of particle are presented and compared with the reference values.

Image data available on internet and in personal computers is colossal. There is a need of a search engine that can effectively meet the retrieval demands of user. Most of the systems available consider low level features for retrieval without taking input from user. To handle this problem, we propose a search engine that can retrieve images from database based on specific request from user. We present a system that has multiple computer vision algorithms based retrieval options available. Scene, human, face, age, emotion, face recognition, gender and object detection based systems are integrated to create a diverse image search engine. The retrieval performance of system is shown in pictorial form. Precision and recall metrics are used to evaluate system’s performance.

The DØ experiment at the Fermilab pp¯ Tevatron collider (Batavia, IL, USA) has undergone significant upgrades in anticipation of high luminosity running conditions. As part of the upgrade, the capabilities of the Central Track Trigger (CTT) to make trigger decisions based on hit patterns in the Central Fiber Tracker (CFT) have been much improved. We report on the implementation, commissioning and operation of the upgraded CTT system.

Precise labeling of an image based on its semantic description is quite challenging task and has its significant applications in surveillance area. Majority of scene classification techniques during past few decades have targeted low level feature by handcraft engineering or unsupervised feature extraction techniques. In this paper, we aim to categorize scene classes for surveillance systems by exploiting deep convolutional features to manifold projection along with supervised classification algorithms. A topology is constructed to depict high dimensions of convolution heat-maps to 128D salient features. Parameters of pre-trained network are tuned to precisely fit with the output of our problem. Experimental results depict that our methodology is more robust and competitive as compared to state of the art methods.

Lameness is one of the biggest insults to the well-being and productivity of dairy cows, which results in colossal economic losses for dairy producers.Nevertheless, it is overlooked and least studied dairy problem in Pakistan.The objective of this study was to determine the prevalence and associated risk factors of lameness at commercial dairy herds in Punjab, Pakistan.The sample size was 2,555 cows from 15 dairy herds assessed using a 5-point locomotion rating scale.A cow with a locomotion score of 3 or higher was considered to be lame.Lame cows were investigated for hoof and claw disorders based on clinical assessment.The prevalence of lameness at the herd-level ranged from 3.08% to 33.08% (overall = 14.20%).The prevalence based on severity showed that on all farms, the majority of cows were mildly lame (7.71%) followed in order by moderately lame (4.61%) and severely lame (1.88%).Prevalence of lameness was significantly higher (P<0.05) in cows with a low body condition score (≤2.75) than in cows with a higher body condition score.In addition, cows fed commercial concentrate were 1.6 times more likely to be lame than cows fed TMR.Cows on farms with an annual hoof trimming frequency had 1.7 times higher odds to be lame than cows with twice-a-year hoof trimming.Similarly, cows in environmentally controlled sheds had a 2.6 times higher probability of lameness than cows kept in open sheds.Moreover, prevalence of lameness was significantly associated (P<0.05) with the seasons of the year; highest in wet summer while lowest in spring.Among the hoof and claw lesions, sole ulcer was significantly (P <0.05) more prevalent than white line disease, sole hemorrhage or inter-digital dermatitis.It was concluded that lameness and hoof lesions are important health problems in studied commercial dairy herds.The occurrence of lameness could be reduced in dairy herds if producers become aware of the associated risk factors, and improve management practices related to cows, environment and facility design.

Due to the availability of high performance computational devices and enormous video data, deep learning algorithms are assisting for human understandable description of videos. Automatic commentary generation of cricket videos take advantage of aforementioned intelligent techniques. VGG-16 network facilitates extraction of visual pattern from frames followed by encoder-decoder LSTM model. Proposed model can handle variable length input data to output variable number of sequential output. Moreover, the model has ability to encompass temporal information to predict the line and length bowled by bowler, the shot selection of batsman and outcome of the ball. Due to unavailability of cricket commentary dataset, a novel cricket commentary dataset containing video-commentary pairs is presented. Evaluation is also performed on benchmark video captioning datasets which are Microsoft Video Description Dataset (MSVD) and MSR - Video to Text dataset (MSRVTT). Captions generated by our model are evaluated on video captioning metrics which are METEOR, BLEU, ROGUE L and CIDEr and outperforms the baseline model.

Usage of mobile phone is growing rapidly in all over the world and nowadays, mobile phones have advanced features. The concept of ringtone selection in the coming generation is increasing day by day. This study is conducted about the musical elements (tone color, rhythm, melody and tempo) of ringtone. The objective is to examine the elements that are mostly preferred during ringtone selection. For this purpose, a questionnaire was designed and sample size of 380 respondents (students) was selected from the University of Sargodha, Punjab, Pakistan by using simple random sampling technique. Bivariate analysis was used and the results conclude that the most frequently used elements during the ringtone selection are rhythm and tempo. Moreover; chi-square test for association reveals that age and tone color have a significant relationship.

The search for the Higgs boson and the study of the heaviest known fundamental particle, the top quark, have been at the center of the Tevatron research program. The Higgs boson is yet to be discovered and the top quark was discovered in 1995. Both of these particles have a very special place in the "periodic table" of fundamental particles. With Tevatron having delivered more than 11 fb−1 of data at 1.96 TeV and the Large Hadron Collider also rapidly collecting data at 7 TeV, we are entering a very exciting era where many interesting questions about these intriguing particles will be answered. Here I summarize the current status of Higgs boson and top quark studies at the Tevatron.

One of the main goals of the Tevatron RunII is to look for any hints for new physics. At D0, the range of searches for new physics signals is large. A few of these analyzes are discussed in this paper.

We present first evidence for the production of single top quarks at the Fermilab Tevatron p{bar p} collider. Using a 0.9 fb{sup -1} dataset, we apply a multivariate analysis to separate signal from background and measure cross section for single top quark production. We use the cross section measurement to directly determine the CKM matrix element that describes the Wtb coupling. We also present results of W0 and charged Higgs searches with the same final states as standard model single top quark production.

One of the main goals of the Tevatron RunII is to look for any hints for new physics. At D0, the range of searches for new physics signals is large and one of the places we look for hints for new physics is by measuring the top quark properties. A few of these measurements are discussed in this paper.

Purpose: To compare the efficacy of nepafenac 0.3% with nepafenac 0.1% to control postoperative pain and conjunctival redness after cataract surgery.&#x0D; Study Design: Randomized control trial&#x0D; Place and Duration: Ophthalmology Department, DHQ Teaching Hospital, Gujranwala from November 2020 to January 2021.&#x0D; Materials and Methods: A prospective review of 70 patients operated for age-related cataract was done. Patients were divided into two equal groups. Group A patients were given Ilevro eye drops (nepafenac 0.3%) once a day and group B patients were instilled Nevanac eye drops (nepafenac 0.1%) thrice a day. All patients were scored for ocular pain and conjunctival redness on basis of pre-defined scales on one day before surgery and on 1st, 7th and 14th postoperative day. Results from both groups were analyzed and compared using SPSS v 25.0.&#x0D; Results: Out of 70 patients, 35 were put in group A and 35 into group B. Overall 37 (52.8%) patients were male and 33 (47.2%) were female. Patients above 40 years of age were 33 (94.3%) in group A and 35 (100%) in group B. Patients having pain score ≥5 were 30 (85.7%) in group A and 25 (71.4%) in group B on 1st postoperative day, with ≥3 were 1 (2.8%) in group A and 33 (94.2%) in group B at 7th postoperative day and zero on 14th postoperative day. Patients with conjunctival redness ≥2 were 31 (88.6%) in group A and 29 (82.9%) in group B on 1st postoperative day, with ≥1 were 18 (51.3%) in group A and 28 (79.9%) in group B on 7th postoperative day while four (11.4%) in group A and one (2.8%) in group B on 14th postoperative day. Conclusion: Effect of once daily nepafenac 0.3% on postoperative pain and conjunctival redness was found to be sub-rated against thrice daily nepafenac 0.1% on 1st postoperative day. However, this effect became equal and then slightly superior to that of nepafenac 0.1% on 7th and 14th postoperative days.&#x0D; Key Words: Cataract Surgery, Nepafenac, Postoperative Pain, Conjunctival Redness

One of the main goals of the Tevatron RunII is to look for any hints for new physics. At D0, the range of searches for new physics signals is large and one of the places we look for hints for new physics is by measuring the top quark properties. A few of these measurements are discussed in this paper.

The Run II of the Tevatron has started in 2001 and the D0 and CDF experiments have collected more than 2 fb -1 data since then.We present the results of a search for electroweak production of single top quarks in pp collisions at √ s = 1.96TeV at the Fermilab Tevatron collider, using a dataset with integrated luminosity of nearly 1 fb -1 .

The authors present a search for single top quarks produced in the s-channel electroweak production mode. The search is performed in the electron+jets decay channels, with one or more secondary-vertex tagged jets to indicate the presence of a b-jet and hence improving the signal:background ratio. Separation between signal and background is further enhanced by the use of Feed Forward Neural networks. 360 pb-1 of Run II data used for this analysis was delivered by the Tevatron, and collected by D0 between August 2002 and August 2004. The resulting 95% confidence level upper limit is 4 pb.

The DOslash experiment was upgraded in spring 2006 to harvest the full physics potential of the Tevatron accelerator at Fermi National Accelerator Laboratory, Batavia, Illinois, USA. It is expected that the peak luminosity delivered by the accelerator will increase to over 300 times 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . One of the upgraded systems is the central track trigger (CTT). The CTT uses the central fiber tracker (CFT) and preshower detectors to identify central tracks with p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> > 1.5 GeV at the first trigger level. Track candidates are formed by comparing fiber hits to predefined track equations. In order to minimize latency, this operation is performed in parallel using combinatorial logic implemented in FPGAs. Limited hardware resources prevented the use of the full granularity of the CFT. This leads to a high fake track rate as the occupancy increases. In order to mitigate the problem, new track-finding hardware was designed and commissioned. We report on the upgrade and the improved performance of the CTT system.