Miracle-dz/UniFAR-FTUs huggingface dataset:
The Naïve Researcher: Doing Social Research in Africa

This paper discusses the practicalities of doing social research. It draws on the author’s field experiences in four African countries during postgraduate training. It discusses physical risk, visible or invisible, that can be encountered during fieldwork and highlights other conceptual issues that are necessary for postgraduate students as first‐timers or ‘naïve’ researchers to consider. The reason(s) for doing this is to demonstrate a reflexive stance, i.e. the relationship between the theory and practice of qualitative research. The discussion thus offers an insight into what new researchers are likely to face when undertaking fieldwork abroad and issues that can act as a deterrent to the researcher as ‘novice’ or ‘naïve’.
Chinese medicinal herbs for asymptomatic carriers of hepatitis B virus infection.

BACKGROUND About 350 million people are chronically infected carriers of hepatitis B virus and are at a higher risk of serious illness and death from cirrhosis of the liver and liver cancer. Chinese medicinal herbs have been used widely for more than 2000 years to treat chronic liver disease. OBJECTIVES To assess whether Chinese medicinal herbs are effective and safe for treating asymptomatic carriers of hepatitis B virus. SEARCH STRATEGY The trials registers of the Cochrane Hepato-Biliary Group, the Cochrane Library and the Cochrane Complementary Medicine Field were searched in combination with MEDLINE, EMBASE, and handsearches of Chinese journals and conference proceedings. SELECTION CRITERIA Randomised or quasi-randomised trials (minimum follow-up three months) in asymptomatic carriers of hepatitis B virus. Chinese medicinal herbs (single herb or compound of herbs) compared with placebo, no intervention, general non-specific treatment, or interferon treatment. Trials of Chinese medicinal herbs plus interferon versus interferon alone were also included. DATA COLLECTION AND ANALYSIS Data were extracted independently by two reviewers. Analysis was performed by intention-to-treat where possible. Pre-specified subgroup analyses were: ethnic origin, age at time of infection, and single herb or compound of herbs. MAIN RESULTS Three randomised clinical trials (307 patients) that followed patients for three months or more after the end of treatment were included. The methodological quality was poor. The herbal compound 'Jianpi Wenshen recipe' had significant effects on viral markers compared to interferon: relative risk 2.40 (95% CI 1.01 to 5.72) for clearance of serum HBsAg, 2.03 (95% CI 0.98 to 4.20) for clearance of HBeAg, and 2.54 (95% CI 1.13 to 5.70) for seroconversion of HBeAg to anti-HBe. Phyllanthus amarus and Astragalus membranaceus showed no significant antiviral effect compared with placebo. Analysis of pooling eight randomised clinical trials with less than three months follow-up did not show a significant benefit of Chinese medicinal herbs on viral markers. Data on long-term clinical outcomes and quality of life were lacking. REVIEWER'S CONCLUSIONS Based on one low quality trial, the medicinal herb 'Jianpi Wenshen recipe' may have an antiviral activity in asymptomatic carriers of hepatitis B virus. However, rigorous randomised, double-blind, placebo-controlled trials are needed before herbs should be used for this condition.
A News Analysis System

This paper describes a prototype news analysis system which classifies and indexes news stories in real time. The system extracts stories from a newswire, parses the sentences of the story, and then maps the syntactic structures into a concept base. This process results in an index containing both general categories and specific details. Central to this system is a Government-Binding parser which processes each sentence of a news item. The system is completely modular and can be interfaced with different news feeds or concept bases.
A Failure Prediction Strategy for Transistor Aging

This paper presents a novel failure prediction technique that is applicable for system-on-chips (SoCs). Highly reliable systems such as automobiles, aircrafts, or medical equipments would not allow any interruptive erroneous responses during system operations, which might result in catastrophes. Therefore, we propose a failure prediction technique that can be applied during an idle time when a system is not working, such as power-on/-off time. To achieve high reliability in the field, the proposed technique should take into consideration various types of aging mechanisms and the testing environment of voltage and temperature which is uncontrollable in the field. Therefore, we propose: 1) an accurate delay measurement technique considering the variation due to voltage and temperature and 2) an adaptive test scheduling that gives more test chances to more probable degrading parts. Experimental results show the required memory space and area cost for implementing the proposed technique.
Vision based simultaneous localization and Mapping using sigmapoint particle filter

One of the most challenging problems in mobile robotic is to localize the robot and simultaneously prepare a map of its environment. In this paper we have used a particle filter to solve SLAM problem in an indoor environment by using stereo vision. We have implemented this method on a mobile robot while navigating in a planar surface among landmarks which were detected by Scale Invariant Feature Transform (SIFT) technique. We have also proposed a novel method based on Sigma Point Particle Filter (SPPF) to solve SLAM problem. Simulation results are compared with the Particle Filter (PF) based SLAM with the same scenario. Finally the experimental results which verify the accuracy and efficiency of our proposed algorithm are presented.
An Efficient and Private Authentication Protocol for RFID Systems

Radio Frequency IDentification based systems are getting pervasively deployed in many real-life applications in various settings for identification and authentication of remote objects. However, the messages that are transmitted over a insecure channel, are vulnerable to security and privacy concerns such as data privacy, location privacy of tag owner and etc. Recently, Yeh et al.’s proposed a RFID authentication protocol based on quadratic residue which is claimed to provide location privacy and prevent possible attacks. In this paper, we formally analyzed the protocol and we proved that the protocol provides destructive privacy according to Vaudenay privacy model. Moreover, we proposed a unilateral authentication protocol and we prove that our protocol satisfies higher privacy level such as narrow strong privacy. Besides, we proposed an enhanced version of our proposed protocol, which has same privacy level as Yeh et al.’s protocol, but has reader authentication against stronger adversaries. Furthermore, the enhanced version of our protocol uses smaller number of cryptographic operations when compared to Yeh et al.’s protocol and it is also cost efficient at the server and tag side and requires O(1) complexity to identify a RFID tag.
Improvised eigenvector selection for spectral Clustering in image segmentation

General spectral Clustering(SC) algorithms employ top eigenvectors of normalized Laplacian for spectral rounding. However, recent research has pointed out that in case of noisy and sparse data, all top eigenvectors may not be informative or relevant for the purpose of clustering. Use of these eigenvectors for spectral rounding may lead to bad clustering results. Self-tuning SC method proposed by Zelnik and Perona [1] places a very stringent condition of best alignment possible with canonical coordinate system for selection of relevant eigenvectors. We analyse their algorithm and relax the best alignment criterion to an average alignment criterion. We demonstrate the effectiveness of our improvisation on synthetic as well as natural images by comparing the results using Berkeley segmentation and benchmarking dataset.
Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties*

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.
Scalable Stochastic Alternating Direction Method of Multipliers

Stochastic alternating direction method of multipliers (ADMM), which visits only one sample or a mini-batch of samples each time, has recently been proved to achieve better performance than batch ADMM. However, most stochastic methods can only achieve a convergence rate $O(1/\sqrt T)$ on general convex problems,where T is the number of iterations. Hence, these methods are not scalable with respect to convergence rate (computation cost). There exists only one stochastic method, called SA-ADMM, which can achieve convergence rate $O(1/T)$ on general convex problems. However, an extra memory is needed for SA-ADMM to store the historic gradients on all samples, and thus it is not scalable with respect to storage cost. In this paper, we propose a novel method, called scalable stochastic ADMM(SCAS-ADMM), for large-scale optimization and learning problems. Without the need to store the historic gradients, SCAS-ADMM can achieve the same convergence rate $O(1/T)$ as the best stochastic method SA-ADMM and batch ADMM on general convex problems. Experiments on graph-guided fused lasso show that SCAS-ADMM can achieve state-of-the-art performance in real applications
Multi-UAV Task Allocation using Team Theory

A multiple UAV search and attack mission in a battlefield involves allocating UAVs to different target tasks efficiently. This task allocation becomes difficult when there is no communication among the UAVs and the UAVs sensors have limited range to detect the targets and neighbouring UAVs, and assess target status. In this paper, we propose a team theoretic approach to efficiently allocate UAVs to the targets with the constraint that UAVs do not communicate among themselves and have limited sensor range. We study the performance of team theoretic approach for task allocation on a battle field scenario. The performance obtained through team theory is compared with two other methods, namely, limited sensor range but with communication among all the UAVs, and greedy strategy with limited sensor range and no communication. It is found that the team theoretic strategy performs the best even though it assumes limited sensor range and no communication.
Reasoning Through Search: A Novel Approach to Sentiment Classification

We introduce a novel approach to sentiment classication. Our Reasoning Through Search (RTS) technique uses existing labeled data, query formation strategies, and a case base to estimate the sentiment of a text review. Unlike previous systems, when classifying a document from a domain that the system does not have explicit, in-domain training data, our classication system leverages domain relatedness and a case base of labeled reviews to perform the classication. While the system does require labeled training data, it does not rely on the guaranteed presence of in-domain labeled training data.
Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility Maximization Under Imperfect Channel-State Information

We consider the problem of simultaneous user-scheduling, power-allocation, and rate-selection in an orthogonal frequency division multiple access (OFDMA) downlink, with the goal of maximizing expected sum-utility under a sum-power constraint. In doing so, we consider a family of generic goodput-based utilities that facilitate, e.g., throughput-based pricing, quality-of-service enforcement, and/or the treatment of practical modulation-and-coding schemes (MCS). Since perfect knowledge of channel state information (CSI) may be difficult to maintain at the base-station, especially when the number of users and/or subchannels is large, we consider scheduling and resource allocation under imperfect CSI, where the channel state is described by a generic probability distribution. First, we consider the “continuous” case where multiple users and/or code rates can time-share a single OFDMA subchannel and time slot. This yields a nonconvex optimization problem that we convert into a convex optimization problem and solve exactly using a dual optimization approach. Second, we consider the “discrete” case where only a single user and code rate is allowed per OFDMA subchannel per time slot. For the mixed-integer optimization problem that arises, we discuss the connections it has with the continuous case and show that it can solved exactly in some situations. For the other situations, we present a bound on the optimality gap. For both cases, we provide algorithmic implementations of the obtained solution. Finally, we study, numerically, the performance of the proposed algorithms under various degrees of CSI uncertainty, utilities, and OFDMA system configurations. In addition, we demonstrate advantages relative to existing state-of-the-art algorithms.
Studying the effect of cold plasma on the blood using digital image processing and images texture analysis

The present work contend with physical plasma and digital image processing and this process needs a high degree of safety. This paper sheds light on the plasma interactions with living cells studying the blood cells expose to cold Plasma using texture analysis, it is a new method to finding the changes which occur in the blood texture as a result from exposure the blood samples to the plasma for different times; this effect can be studied utilizing the statistical Feature. It is important to mention that the paper includes two types of people their leukocytes number exceeded (10000), while the other type their leukocytes number less than (4000).
Validity of stage assessment in the adoption and maintenance of physical activity and fruit and vegetable consumption.

OBJECTIVE Stage assessments are examined to develop and test refined measurements that can be used for classifying individuals. DESIGN Stages were assessed in 1,850 persons in terms of their physical activity and dietary behaviors. MAIN OUTCOME MEASURES Stages for both behaviors were compared to behavior and other test variables. Misclassification, sensitivity, specificity, receiver-operation-curves, and discontinuity patterns were computed. Discontinuity patterns were tested with trends across stages and planned contrasts between adjacent stages. RESULTS In comparison to previous studies, sensitivity (70% to 80%) and specificity (80% to 87%) were high. When using lower level criteria (such as less intensive activity), sensitivity was lower, whereas specificity was higher. When behavioral maintenance was assessed, results suggested that the temporal cut-off point between action and maintenance was equally optimal at different cut-off points. Applying contrast analyses, nonlinear trends across the stages and a match of 87% of predictions of stage differences resulted. CONCLUSION Stage assumptions are supported in general, and refined stage assessment in particular. Levels of psychological variables (e.g., easiness, habit) may discriminate stages as well as or even better than temporal stage definitions.
A noisy sparse convolution neural network based on stacked auto-encoders

Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image classification through greedy layer-wise training. In order to make the constructed CNN to have strong robustness to noise, we added a noisy-layer in the pre-training stage. Adding the sparsity constraint can make the training of the CNN more effective, and can also reduce data redundancy. For classification applications, our experiments show that the final classification results of the proposed model is superior to the combination of auto-encoders and the noisy auto-encoders.
[The short version of the Brief Symptom Inventory (BSI -18): preliminary psychometric properties of the German translation].

The short version of the Brief Symptom Inventory (BSI-18) is an easily applicable, reliable and valid self-report measure in wide international use. It assesses the syndroms of somatisation, depression and anxiety by means of 6 items each. For the psychometric evaluation of the German translation, undergraduate students, non-clinical subjects and psychosomatic outpatients were administered the BSI-18. Across these populations, all scales showed satisfactory to good internal consistencies (Cronbachs alpha ranged between 0.63 and 0.93). The item-total correlation as item discrimination index was sufficiently high, patricularly in the patient sample (rit ≥ 0.40). The correlations of the BSI-18 with measures of similar dimensions were moderate to high indicating a good convergent validity. Criterion-related validity was established in so far that patients scored significantly higher in all BSI-18 scales than did the non-clinical samples. With some exceptions, the German translation of the BSI-18 has similarly good psychometric qualities as the American original.
TEG BASED POWER SYSTEM for OPERATION of HEALTH MONITORING SERVER in INDUSTRIES

In Hazardous environment industries where the emission of toxic gases and effluents will have an impact on the health of the operators, the recording of health parameters of individual operators is very important. In such situation the health monitoring of operators are being monitored and processed centrally by a server and this can be powered by a system of batteries which can be operated from TEG to ensure reliability and to utilize the exhaust heat energy of industries for better performance. This paper describes the Thermo electric generator with battery that supports medical server. The thermoelectric generator converts heat energy into electrical energy. Mathematical model of thermoelectric generator was developed in MATLAB SIMULINK. This exhibits different voltage and current for various temperature differences. The proposed system uses maximum power point tracking algorithm to obtain the maximum power from the thermoelectric generator. This algorithm is developed in embedded MATLAB controller, which gives the firing angle to the DC-DC Boost converter for various temperature differences. This will increase the efficiency of the system.
Automated mosaicing for improving vehicle situational awareness in real time

Situational awareness is increasingly important across many applications. Having a more adept sense of situational awareness leads to better understanding and prediction in various scenarios. This is apparent with the increasing use of infrared cameras. The benefits of infrared imaging make it an attractive option for use in ground vehicles. However, they are limited in their field-of-views. We propose an automated mosaicing method, to improve situational awareness, using infrared images from a vehicle mounted camera. Within our method we also propose a novel key frame selection algorithm, for efficient real time mosaicing. We validate our algorithm using different driving speeds, showing that it is robust across different driving scenarios.
Trace-driven rapid pipeline architecture evaluation scheme for ASIP design

This paper proposes a rapid evaluation scheme of pipeline architectures using phase-accurate simulation with only delay model and trace. With latency information for every stage, we can decide if an instruction in one stage can proceed to the next stage or if an instruction can be issued for each cycle without evaluating the value for registers. Branch target becomes available with trace generated by fast instruction set simulation. Fast verification time becomes possible because instruction set simulation is performed only once.
Risk Factors for Culling, Sales and Deaths in New Zealand Dairy Goat Herds, 2000–2009

The aim of this study was to identify risk factors for culling, sales and deaths in intensively managed dairy goat herds in New Zealand. A data set provided by the New Zealand Dairy Goat Cooperative (n = 13,197 does) was analyzed using a Cox proportional hazard model. The outcome of interest was length of productive life (LPL), defined as the number of days from the date of second kidding to the date of removal from the herd or the date on which follow-up was terminated, whichever occurred first. Milk solids yield in the first lactation (MSL1) as a predictor of LPL was parameterized in the model as a penalized spline term. To account for MSL1 violating the proportional hazards assumption of the Cox model, LPL was divided into two intervals: T1 (less than or equal to 730 days from the date of second kidding) and T2 (greater than 730 days from the date of second kidding). MSL1 was then included in the model as a time-dependent covariate. A frailty term was included in the model to account for unmeasured, herd-level effects on LPL. During T1, the daily hazard of removal for does that produced 80 kg milk solids in the first lactation was 0.84 (95% CI 0.58–1.23) times the daily hazard of removal for does that produced 30 kg milk solids in the first lactation. During T2, the daily hazard of removal for does that produced 80 kg milk solids in the first lactation was 1.44 (95% CI 0.79–2.65) times the daily hazard of removal for does that produced 30 kg milk solids in the first lactation. We conclude that involuntary losses may be avoided if high MSL1 yielding does are preferentially managed from 2 years beyond the date of second kidding.