Computer Science

In recent years a great deal of effort has been expended to develop methods that determine speech
quality through the use of comparative algorithms. These methods are designed to calculate an index
value of quality that correlates to a mean opinion score given by human subjects in evaluation sessions. In
this paper, we validate Perceptual Evaluation of Speech Quality (PESQ) - ITU-T Recommendation P.862
and the Netherlands’s Organization for Applied Scientific Research (TNO) proposal- Perceptual Speech
Quality Measurement 2010 (PSQM2010) on the Perceptual Objective Listening Quality Assessment (POLQA)
benchmark, which is the new ITU-T benchmarking for objective measurement of speech quality, on the
tonal language Igbo –a language spoken in south eastern- Nigeria. Experiments are done in super wideband
mode and PESQ was applied to down sampled versions of the signals. The result on PESQ P.862.2 shows a
good correlation between the subjective and objective measurements on the tonal language Igbo with a
correlation coefficient of r = 0.88 on the overall data set. For Dutch, the model shows less correlation
between the subjective and the objective measurements (r = 0.84) compared to the tonal language Igbo
(r = 0.88). .The PSQM2010 is optimized for western languages and the correlation for the Dutch super
wideband database is 0.91. For Igbo our super wideband result shows a poor correlation of 0.70 between
the subjective and objective measurements. For the tonal and non tonal sentences no big differences are
found. The correlation between the subjective results for tone and non-tone sentences is 0.96. We
conclude by making recommendations for research, policies and practices.

Purpose: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos need to be trained on data in which the spatial positions of the tools are manually annotated. Generating such training data is difficult and time-consuming. Instead, we propose to use solely binary presence annotations to train a tool tracker for laparoscopic videos. Methods: The proposed approach is composed of a CNN + Convolutional LSTM (ConvLSTM) neural network trained end-to-end, but weakly supervised on tool binary presence labels only. We use the ConvLSTM to model the temporal dependencies in the motion of the surgical tools and leverage its spatio-temporal ability to smooth the class peak activations in the localization heat maps (Lhmaps). Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5.0%, 13.9%, and 12.6%, respectively. Conclusions: In this paper, we demonstrate that binary presence labels are sufficient for training a deep learning tracking model using our proposed method. We also show that the ConvLSTM can leverage the spatio-temporal coherence of consecutive image frames across a surgical video to improve tool presence detection, spatial localization, and motion tracking. Keywords Surgical workflow analysis • tool tracking • weak supervision • spatio-temporal coherence • ConvLSTM • endoscopic videos

Mobile phone no doubt has become everyone’s personal companion. It finds close companionship among the youths. Mobile phone SMS slang has adversely affected students’ ability to write proper English grammar, as they find it a very convenient and economic way of communication. This work presents a needs assessment survey in which over 90% of respondents expressed desire to have an application that converts SMS slang. It also shows the design and implementation of SMS slang to English converter for Android devices. Results show a compression rate of over forty percent, therefore reducing the cost of the SMS, time and bandwidth.

Rilpivirine (RPV, TMC278, Edurant † ) is a next-generation non-nucleoside reverse transcriptase inhibitor (NNRTI), which demonstrated high virologic response rates and non-inferiority versus efavirenz in two Phase III trials in HIV-infected patients through 96 weeks . RPV has been shown to inhibit P-glycoprotein (P-gp) in vitro with an apparent IC 50 of 9.2 mM (3.4 mg/ mL). This study evaluated the in-vivo effect of steady-state RPV 25 mg once daily (qd) on the single-dose pharmacokinetics of the probe P-gp substrate digoxin. This was a Phase I, open-label, randomised, crossover trial in 22 HIV-negative volunteers. Participants received in one session a single 0.5 mg dose of digoxin, and in another session RPV 25 mg qd for 16 days with a single 0.5 mg dose of digoxin in the morning of Day 11. All study drugs were taken with a breakfast. Pharmacokinetic profiles of digoxin in plasma and urine were determined over 144 hours after dosing in each session. Steady-state RPV 24-hour pharmacokinetic profiles in plasma were determined on Day 11. Plasma and urine samples were analysed using validated LC-MS/MS methods. Pharmacokinetic parameters were calculated with non-compartmental methods. The least square (LS) means and associated 90% confidence intervals (CI) of treatment ratios were calculated based on log-transformed pharmacokinetic parameters. Safety and tolerability were assessed throughout the trial. Digoxin pharmacokinetic parameters and statistical results are summarised in .

In the management of Hepatitis C Virus (HCV) infection, there is a great interest in early monitoring of viral load
since early emergence of resistant strains can jeopardize the treatment outcome. In this paper we present an
ongoing research to address the question whether early longitudinal viral loads can be used as a surrogate in
discriminating responders / non-responders at the end of the trial. The data analyzed in this paper are from a
completed, randomized, phase II, clinical study, exploring the effect of a certain protease inhibitor (PI) therapy in
combination with the standard of care on efficacy, safety, tolerability, PK and PK/PD of the therapy. We propose a
semi-parametric mixed effect model which is used to model the longitudinal sequence up to week 12 of the
subjects. Based on subject specific parameters obtained from the semi-parametric model we estimated the
probability to be a responder at the end of the trial. The semi-parametric effect models were implemented with the
procedure PROC MIXED in SAS version 9.1.

Key Words: Hepatitis C Virus; Longitudinal viral RNA; Surrogate endpoint; Mixed Effects Models; PROC MIXED in SAS.

Background

This descriptive, non-comparative, phase III study evaluated the safety and tolerability of cell culture-derived (TIVc) and egg-derived (TIV) seasonal influenza vaccines in children at-risk of influenza-related complications.

Methods

430 subjects were randomized 2:1 to TIVc or TIV. Subjects aged 3 to <9 years received one dose (if previously vaccinated, n = 89) or two doses (if not previously vaccinated, n = 124) of the study vaccines; the 9- to <18-year olds (n = 213) received one dose. Reactogenicity was assessed for 7 days after vaccination; safety was monitored for 6 months.

Results

After any vaccination, the most frequently reported solicited local adverse event (AE) was tenderness/pain (TIVc [44%, 66%, 53%] and TIV [56%, 51%, 65%] in 3 to <6, 6 to <9, and 9- to <18-year olds, respectively) and the systemic AE was irritability (22% TIVc, 24% TIV) in 3- to <6-year olds and headache in 6- to <9-year olds (20% TIVc, 13% TIV) and 9- to <18-year olds (21% TIVc, 26% TIV). There were no cases of severe fever (≥40 °C). No vaccine-related serious AEs were noted. New onset of chronic diseases was reported in ≤1% of subjects.

Conclusion

TIVc and TIV had acceptable tolerability and similar safety profiles in at-risk children (NCT01998477).

Adjuvanted recombinant zoster vaccine 23-Valent pneumococcal polysaccharide vaccine Immune response Safety a b s t r a c t Background: This study evaluated immunogenicity and safety of the adjuvanted recombinant zoster vaccine (RZV) when the first dose was co-administered with the 23-valent pneumococcal polysaccharide vaccine (PPSV23) in adults aged 50 years. Methods: In this open label, multi-center study (NCT02045836), participants were randomized 1:1 to receive either the first dose of RZV and PPSV23, co-administered at Day 0 and the second dose of RZV at Month 2 (Co-Ad group), or PPSV23 at Day 0, the first dose of RZV at Month 2 and second dose of RZV at Month 4 (Control group). Co-primary objectives were the RZV vaccine response rate (VRR) in the Co-Ad group and the non-inferiority of the antibody responses to RZV and PPSV23 in the Co-Ad group compared to the Control group. Reactogenicity and safety were also assessed. Results: 865 participants were vaccinated (Co-Ad: 432, Control: 433). VRRs to RZV were >98% in both groups. Humoral immune responses to co-administration of RZV and PPSV23 were non-inferior to sequential administration. All three co-primary immunogenicity objectives were met. Solicited local symptoms after the first RZV dose were reported by similar percentages of participants in both groups. Solicited general symptoms were more frequently reported when the first dose of RZV and PPSV23 were co-administered. No differences were apparent between groups after the second RZV dose. Conclusions: No immunologic interference was observed between RZV and PPSV23 when co-administered in adults 50 years. No safety concerns were raised.

Background
This study evaluated immunogenicity and safety of the adjuvanted recombinant zoster vaccine (RZV) when the first dose was co-administered with the 23-valent pneumococcal polysaccharide vaccine (PPSV23) in adults aged ≥50 years.

Methods
In this open label, multi-center study (NCT02045836), participants were randomized 1:1 to receive either the first dose of RZV and PPSV23, co-administered at Day 0 and the second dose of RZV at Month 2 (Co-Ad group), or PPSV23 at Day 0, the first dose of RZV at Month 2 and second dose of RZV at Month 4 (Control group). Co-primary objectives were the RZV vaccine response rate (VRR) in the Co-Ad group and the non-inferiority of the antibody responses to RZV and PPSV23 in the Co-Ad group compared to the Control group. Reactogenicity and safety were also assessed.

Results
865 participants were vaccinated (Co-Ad: 432, Control: 433). VRRs to RZV were >98% in both groups. Humoral immune responses to co-administration of RZV and PPSV23 were non-inferior to sequential administration. All three co-primary immunogenicity objectives were met. Solicited local symptoms after the first RZV dose were reported by similar percentages of participants in both groups. Solicited general symptoms were more frequently reported when the first dose of RZV and PPSV23 were co-administered. No differences were apparent between groups after the second RZV dose.

Conclusions
No immunologic interference was observed between RZV and PPSV23 when co-administered in adults ≥50 years. No safety concerns were raised.

Abbreviations
VZV, varicella-zoster virus; HZ, herpes zoster; PHN, postherpetic neuralgia; CDC, United States Centers for Disease Control and Prevention; YOA years of age; ZVL zoster vaccine live; RZV adjuvanted recombinant zoster vaccine; PPSV2323-valent pneumococcal polysaccharide vaccine; PCV13 13-valent pneumococcal conjugate vaccine; IDP invasive pneumococcal disease; gE glycoprotein E; M month GMC geometric mean concentration; GMT geometric mean titer; ELISA enzyme-linked immunosorbent assay; MOPA multiplex opsonophagocytic assay; VRR vaccine response rate; (S)AE (serious) adverse event; pIMD potential immune-mediated disease; CI confidence interval; LL/UL lower/upper limit; TVC total vaccinate cohort; ATP according-to-protocol
Keywords
Herpes zoster; Adjuvanted recombinant zoster; vaccine23-Valent pneumococcal polysaccharide vaccine; Immune response; Safety

Quadrivalent influenza vaccines (QIVs), which include both B lineage strains, are expected to provide broader protection than trivalent influenza vaccines (TIVs). The non-inferiority, immunogenicity, and safety of a cell culture-based investigational QIVc and 2 TIVs (TIV1c, TIV2c), in adults (18 y), were evaluated in this Phase III, double-blind, multicenter study. A total of 2680 age-stratified subjects were randomized (2:1:1) to receive 1 dose of QIVc (n D 1335), TIV1c (n D 676), or TIV2c (n D 669). TIV1c (B/Yamagata) and TIV2c (B/Victoria) differed only in B strain lineage. The primary objective was to demonstrate noninferiority of the hemagglutinin-inhibition antibody responses of QIVc against TIVc, 22 d post-vaccination. Secondary objectives included the evaluation of immunogenicity of QIVc and TIVc in younger (18-<65 y) and older (65 y) adults. Hemagglutinin inhibition assays were performed at days 1 and 22. Solicited local and systemic adverse events (AEs) were monitored for 7 d post-vaccination, and unsolicited AEs and serious AEs until day 181. QIVc met the non-inferiority criteria for all 4 vaccine strains and demonstrated superiority for both influenza B strains over the unmatched B strain included in the TIV1c and TIV2c, when geometric mean titers and seroconversion rates with TIVc were compared at day 22. Between 48%-52% of subjects experienced 1 solicited AE, the most common being injection-site pain and headache. Serious AEs were reported by 1% of subjects, none were vaccine-related. The results indicate that QIVc is immunogenic and well tolerated in both younger and older adults. The immunogenicity and safety profiles of QIVc and TIVc were comparable at all ages evaluated. KEYWORDS adults; B-strain; cell culture; influenza; non-inferior; phase III trial; quadrivalent influenza vaccine Quadrivalent influenza vaccines (QIV), which incorporate strains from both influenza B lineages, are developed to overcome the risk of selecting the incorrect B lineage for the vaccine composition and to improve the immunity of individuals against both lineages. In 2012, the first seasonal QIV was licensed for use in the US. 9 Cell-culture technology is now available for the production of influenza vaccines. The first mammalian cell culture-derived trivalent inactivated influenza vaccine (TIVc) approved for use in adults (18 y) was Optaflu Ò (Novartis Vaccines and Diagnostics, GmBH, Marburg, Germany). This vaccine, produced using Madin-Darby Canine Kidney (MDCK) suspension cell lines, has been licensed in Europe since 2007 and in the US since 2012 (under the trade name Flucelvax Ò). 9,10 The safety and immunogenicity of this TIVc have been evaluated in clinical trials in individuals aged 6 mo. 11-13 Using the same MDCK manufacturing platform, Novartis Vaccines and Diagnostics has developed an investigational, cell culture-based, inactivated, quadrivalent influenza vaccine (QIVc). This study assessed the non-inferiority, immunogenicity, and safety of this QIVc compared with TIVc in adults.

The RINSE simulator is being developed to support large-scale network security preparedness and training exercises, involving hundreds of players and a modeled network composed of hundreds of LANs. The simulator must be able to present a realistic rendering of network behavior as attacks are launched and players diagnose events and try counter measures to keep network services operating. We describe the architecture and function of RINSE and outline how techniques like multiresolution traffic modeling and new routing simulation methods are used to address the scalability challenges of this application. We also describe in more detail new work on CPU/memory models necessary for the exercise scenarios and a latency absorption technique that will help when extending the range of client tools usable by the players. % % % % % % % %

A noisy dataset can contain contradictory data. Contradictory data is synonymous to incorrect data and it is important that such data be investigated and evaluated when analysing a noisy dataset. Different approaches to dealing with contradictory data have been proposed by different researchers. For example [1, 2] proposed methods for identifying and removing contradictory data in noisy datasets. However, the removal of contradictory data from a noisy dataset will increase the incompleteness in the dataset thereby reducing the soundness of any information from such set of data. It is therefore important to identify and evaluate contradictory instances when analysing a large and noisy dataset. This will improve the soundness of the analysis from such a dataset. Evidently, the analysis of big data is identified as the next frontier for innovation and advancement of technology [3, 4]. There is therefore the need to identify appropriate approaches to dealing with contradictions in a large and noisy dataset. There are different forms of contradictions. For example, there are contradictions from the use of modal words, structural, subtle lexical contrasts, as well as world knowledge

Antenatal care is an essential period in which medical experts examines pregnant women to prepare them for proper child delivery. Choosing or knowing the likely mode of child delivery is essential both for the mother and the medical team. It helps in proper preparation for labour and any possible complication that could arise. There are also chances of reducing maternal or child mortality. However, the decision on which of the options is appropriate is sometimes difficult due to several parameters and variable. Analyzing Obstetric and mode of delivery for pregnant woman is tedious, therefore, this work used data from three medical facilities in Katsina State and apply three machine leaning algorithms to predict the most appropriate mode of child delivery. The work was implemented using python programming language software. The result of the work has shown that random forest algorithm performs better with accuracy result as precision was 0.918, recall of 0.715 and 0.896 for Spontaneous Vagina delivery and 0.716 for precision, 0.929 for recall and 0.896 for Caesarian section mode.

Fuzzy Cognitive Maps (FCM) is a soft computing technique whose vertices and edges are fuzzy values with an inference mechanism for solving modelling problems; it has been used in modelling complex systems like industrial and process control. The concept was first introduced in 1986, with an initial learning algorithm in 1996; several works have been published on FCM methodology, learnings and applications. Fuzzy cognitive maps continue to evolve both in theory, learning algorithms and application. Many theories like intuitionistic theory, hesitancy theory, grey system theory, wavelet theory, etc., are integrated with the conventional FCM. These extensions have improved Fuzzy cognitive Maps to handle problems of uncertainty, incomplete information, hesitancy, dynamic systems and probabilistic fuzzy events. They also strengthen fuzzy cognitive Maps' modelling power for application in almost any domain. However, the compilation of the development in methodology and adaptation of FCM are either old or omitted some of the recent advances or focused on specific applications of FCM in some areas. This paper reports extension, learning and applications of FCM from the initial conventional FCM to recent extensions and some of the important features of those extensions and learning.

Agriculture plays a crucial role in Nigeria&#39;s economy, serving as a vital source of sustenance and livelihood for numerous Nigerians. With the escalating impact of climate change on crop yields, it becomes imperative to develop models that can effectively study and predict rice output under varying climatic conditions. This study collected rice yield data from Katsina state, spanning the years 1970 to 2017, sourced from the Nigeria Bureau of Statistics. Additionally, climatic data for the same period were obtained from the World Bank Climate Knowledge portal. Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Random Trees (RT), and Naïve Bayes (NB) were employed to develop rice yield prediction models utilizing this dataset. The findings reveal that random forest and random trees exhibited superior classification performance for yield prediction. The developed models offer a promising tool for predicting future rice yields, facilitating proactive meas...

Data centres form a key part of the infrastructure upon which a variety of information technology services are built. They provide the capabilities of centralized repository for storage, management, dissemination and networking of data and information organized around a particular body of knowledge or pertaining to a particular business. The increasing demand for data storage and compute resources has driven the growth of large data centres: the massive server farms that run many of our present day internet and e-commerce applications with its attendant significant investment capital outlay and ongoing operating costs. This makes a critical examination of costs associated with building, operating and upgrading data centres imperative. For this cost model to meet the requirements of stakeholders it must capture the multidimensional complexities in power delivery, cooling, and required levels of redundancies for a specific service level agreement. However, non factoring of major total cost of ownership (TCO) parameters like amortization and maintenance costs for power, cooling and information technology resources in the cost model for data centres have questioned the reliability of this cost model, hence necessitate a research of this nature. To attack the problems identified above, I adopted a more accurate data centre construction cost model developed at HP Labs and adopted by the Uptime Institute Inc., to serve as a business intelligence tool for measurement and prediction of TCO and return-on-investment analysis for data centres.

Electricity consumers in Nigeria are faced with the challenge of inaccurate and delay in their monthly bills due to drawbacks in their billing systems. This paper presents the design and development of an optimized web-based energy billing system. The proposed system uses a smart meter to measure accurately the electric power consumed by customers in a user friendly manner. It is a web-based application developed with Object Oriented Analysis and Design (OOAD) methodology and implemented using C# programming language with MySQL for back-end database. The meter measures and transmits consumed unit of a customer at the end of the month through a dedicated network. The system allows postpaid customers to clear their bills using recharge cards produced by the system administrator, while prepaid customers recharge their accounts before they have access to power and their consumption charges is deducted in real time. Customers can have access to their consumption history.

The efficiency of buffers in network routers plays an important role in effectively accommodating packets that arrive in bursts at the routers interfaces. The choice of a suitable router buffer size is still a significant problem. Since, the use of small buffer guarantees low packet delay but higher chances of packet lost. Similarly, larger buffer leads to buffer bloat which causes higher delay in a network resulting to poor Quality of Service (QoS). Buffer bloat is a significant problem due to the high changing link characteristics of modern heterogeneous network traffics. The access links can have connections with speedy links (Gbps) with small amount of packet losses and have connection with a susceptible high packet loss with low-bandwidth links like wireless and last mile connections. Hence, what may be thought to be a rational buffer size might be flawed when link rates and delay fall below the minimum value. Thus, this paper analyses buffer size performance and optimization in networks with heterogeneous traffics for Random Early Detection (RED) Queues using NS2 simulations to obtain a range of better suited buffer sizes that improve the QoS without extra signaling and computation by routers. The results obtained established that having a buffer size between five to ten kilobytes yielded best average throughput with low average delay for RED Queue at the congested router Interface. In addition, the optimized buffer size scheme improved the QoS accordingly.

Ethnic conflict, communal clashes, terrorism and insurgency are major security challenges in the twenty-first century. These are responsible for the deaths of millions of people around the world. At the moment intra-state conflict in the form of Farmer/herder conflict has claimed several thousands of lives in Nigeria; however, controlling this conflict system has been a great challenge for many years.  Combining Fuzzy Cognitive Map (FCM) and Non-liner Hebbian Learning (NHL), this work modelled Farmer/Herder conflict in Nigeria as a nonlinear system and demonstrates the ability of the machine learning algorithms to provide control mechanism for the conflict system.  Factors understood to be influential in Farmer/Herder conflict were used to form FCM model that represents the conflict situation, while NHL was used as a control mechanism to find levels of weights between these causative factors that will minimize the conflict and maximize peace.   The result of the work showed that kee...