Visual Speaker Identification Using Lip and Body Movements

Speaker identification has been studied in many fields such as image processing, audio processing, artificial intelligence and speech recognition. Two of these areas are integrated together in order to identify the speaker. This research will focus on two main approaches which are lip movements and body movements. We will work on the two approaches to achieve the speaker identification. The expected outcome of this study will be to identify the speaker in different scenarios, if there is a single speaker or if there is multiple speakers in the video or if the speaker’s lips are not in view

Frequency of Subdural and Epidural Hematoma in Brain Injury Via Computed Tomography in Trauma Center of DHQ Teaching Hospital Sargodha

At least 10 million TBIs serious enough to result in death or hospitalization occur annually. The mortality associated with acute subdural hematoma has been reported to range from 36-79%. Epidural hematoma occurs in approximately 2% of patients with head injuries and 5–15% of patients with fatal head injuries. Both can be caused by fall, motor vehicle crashes, assaults, blasts and sports activities. CT is best modality for diagnosis of brain injury. Objective:To measure the frequency of subdural and epidural hematoma in brain injury via computed tomography in trauma center of DHQ Teaching Hospital Sargodha.Methodology:In this descriptive study, among 137 patients of traumatic brain injury (TBIs) were selected with age and gender discrimination by convenient sampling, at Department of Radiology, DHQ Teaching Hospital Sargodha. Single slice Computed Tomography Toshiba asteion machine was used.Results:Out of 137 patients collected, 35 were females and 102 were males who visited emergency department due to brain Injury. It shows 25.5% were females and males were 74.5%.Out of 137 patients, 63.5% were injured with RTA and 35.8% came with the history of fall. 67.2% patients present with loss of consciousness, 67.9% patients with skull fractures and 73% with swelling. Out of 137 patients 85.4% develop SDH and 14.6% develop EDH. Conclusion:In this study we conclude that male develop larger number of brain injuries than females. Most patients with history of RTA had epidural hematoma. Females most likely develop subdural hematoma. Most patients with brain injury later develop subdural hematoma. Keywords: Subdural Hematoma, Epidural Hematoma, Traumatic Brain Injury(TBI), Road Traffic Accident(RTA) DOI: 10.7176/JHMN/71-01 Publication date: February 29th 202

MLGOPerf: An ML Guided Inliner to Optimize Performance

For the past 25 years, we have witnessed an extensive application of Machine Learning to the Compiler space; the selection and the phase-ordering problem. However, limited works have been upstreamed into the state-of-the-art compilers, i.e., LLVM, to seamlessly integrate the former into the optimization pipeline of a compiler to be readily deployed by the user. MLGO was among the first of such projects and it only strives to reduce the code size of a binary with an ML-based Inliner using Reinforcement Learning. This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner. It employs a secondary ML model to generate rewards used for training a retargeted Reinforcement learning agent, previously used as the primary model by MLGO. It does so by predicting the post-inlining speedup of a function under analysis and it enables a fast training framework for the primary model which otherwise wouldn't be practical. The experimental results show MLGOPerf is able to gain up to 1.8% and 2.2% with respect to LLVM's optimization at O3 when trained for performance on SPEC CPU2006 and Cbench benchmarks, respectively. Furthermore, the proposed approach provides up to 26% increased opportunities to autotune code regions for our benchmarks which can be translated into an additional 3.7% speedup value.Comment: Version 2: Added the missing Table 6. The short version of this work is accepted at ACM/IEEE CASES 202

Can bacterial endophytes be used as a promising bio-inoculant for the mitigation of salinity stress in crop plants? : a global meta-analysis of the last decade (2011-2020)

Soil salinity is a major problem affecting crop production worldwide. Lately, there have been great research efforts in increasing the salt tolerance of plants through the inoculation of plant growth-promoting endophytic bacteria. However, their ability to promote plant growth under no-stress and salinity-stress conditions remains largely uncertain. Here, we carried out a global meta-analysis to quantify the plant growth-promoting effects (improvement of morphological attributes, photosynthetic capacity, antioxidative ability, and ion homeostasis) of endophytic bacteria in plants under no-stress and salinity-stress conditions. In addition, we elucidated the underlying mechanisms of growth promotion in salt-sensitive (SS) and salt-tolerant (ST) plants derived from the interaction with endophytic bacteria under no-stress and salinity-stress conditions. Specifically, this work encompassed 42 peer-reviewed articles, a total of 77 experiments, and 24 different bacterial genera. On average, endophytic bacterial inoculation increased morphological parameters. Moreover, the effect of endophytic bacteria on the total dry biomass, number of leaves, root length, shoot length, and germination rate was generally greater under salinity-stress conditions than no-stress conditions. On a physiological level, the relative better performance of the bacterial inoculants under the salinity-stress condition was associated with the increase in total chlorophyll and chlorophyll-b, as well as with the decrease of 1-aminocylopropane-1-carboxylate concentration. Moreover, under the salinity-stress condition, bacterial inoculation conferred a significantly higher increase in root K+ concentration and decrease in leaf Na+ concentration than under the no-stress condition. In SS plants, bacterial inoculation induced a higher increase in chlorophyll-b and superoxide dismutase activity, as well as a higher decrease in abscisic acid content, than in ST plants. Under salinity-stress, endophytic bacterial inoculation increased root K+ concentration in both SS and ST plants but decreased root Na+ concentration only in ST plants. Overall, this meta-analysis suggests that endophytic bacterial inoculation is beneficial under both no salinity-stress and salinity-stress conditions, but the magnitude of benefit is definitely higher under salinity-stress conditions and varies with the salt tolerance level of plants

Angiogenesis: Managing the Culprits behind Tumorigenesis and Metastasis

Deregulated angiogenesis has been identified as a key contributor in a number of pathological conditions including cancer. It is a complex process, which involves highly regulated interaction of multiple signalling molecules. The pro-angiogenic signalling molecule, vascular endothelial growth factor (VEGF) and its cognate receptor 2 (VEGFR-2), which is often highly expressed in majority of human cancers, plays a central role in tumour angiogenesis. Owing to the importance of tumour vasculature in carcinogenesis, tumour blood vessels have emerged as an excellent therapeutic target. The anti-angiogenic therapies have been shown to arrest growth of solid tumours through multiple mechanisms, halting the expansion of tumour vasculature and transient normalization of tumour vasculature which help in the improvement of blood flow resulting in more uniform delivery of cytotoxic agents to the core of tumour mass. This also helps in reduction of hypoxia and interstitial pressure leading to reduced chemotherapy resistance and more uniform delivery of cytotoxic agents at the targeted site. Thus, complimentary combination of different agents that target multiple molecules in the angiogenic cascade may optimize inhibition of angiogenesis and improve clinical benefit in the cancer patients. This review provides an update on the current trend in exploitation of angiogenesis pathways as a strategy in the treatment of cancer.Ashwaq H. S. Yehya is funded by TWAS (The Academy of Sciences for the Developing World, Italy). Chern Ein Oon is supported by L’Oréal-UNESCO for Women in Science National Fellowship (304/CIPPM/650806/L117) and MAKNA Cancer Research Award (304/CIPPM/650859/M122)

Energy-aware caching and collaboration for green communication systems.

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid

Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources

The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production