Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Diagnosis of Malaria by Travelers and Expatriates: Assessment of Malaria Rapid Diagnostic Tests Available on the Internet

Introduction In the past malaria rapid diagnostic tests (RDTs) for self-diagnosis by travelers were considered suboptimal due to poor performance. Nowadays RDTs for self-diagnosis are marketed and available through the internet. The present study assessed RDT products marketed for self-diagnosis for diagnostic accuracy and quality of labeling, content and instructions for use (IFU). Methods Diagnostic accuracy of eight RDT products was assessed with a panel of stored whole blood samples comprising the four Plasmodium species (n = 90) as well as Plasmodium negative samples (n = 10). IFUs were assessed for quality of description of procedure and interpretation and for lay-out and readability level. Errors in packaging and content were recorded. Results Two products gave false-positive test lines in 70% and 80% of Plasmodium negative samples, precluding their use. Of the remaining products, 4/6 had good to excellent sensitivity for the diagnosis of Plasmodium falciparum (98.2%–100.0%) and Plasmodium vivax (93.3%–100.0%). Sensitivity for Plasmodium ovale and Plasmodium malariae diagnosis was poor (6.7%–80.0%). All but one product yielded false-positive test lines after reading beyond the recommended reading time. Problems with labeling (not specifying target antigens (n = 3), and content (desiccant with no humidity indicator (n = 6)) were observed. IFUs had major shortcomings in description of test procedure and interpretation, poor readability and lay-out and user-unfriendly typography. Strategic issues (e.g. the need for repeat testing and reasons for false-negative tests) were not addressed in any of the IFUs. Conclusion Diagnostic accuracy of RDTs for self-diagnosis was variable, with only 4/8 RDT products being reliable for the diagnosis of P. falciparum and P. vivax, and none for P. ovale and P. malariae. RDTs for self-diagnosis need improvements in IFUs (content and user-friendliness), labeling and content before they can be considered for self-diagnosis by the traveler.
An Analytical Derivation of the Symbol Error Rate of Spatial Modulation in Correlated Rayleigh Fading Channels

The need for high-rate wireless data transmission with efficient spectrum usage has sparked the development of multiple-input multiple-output (MIMO) systems using antenna arrays at the transmitter and receiver side. Spatial modulation is a new spatial multiplexing scheme promising high data rates. Its performance is affected by certain impairments of the radio channel. The effects of transmitter-side spatial correlation on the modulation scheme are studied here, both analytically and with Monte Carlo simulation methods. A new scheme for the analytical calculation of the symbol error rate of spatial modulation is proposed.
Relationship between Phylogenetic Distribution and Genomic Features in Neurospora crassa

In the post-genome era, insufficient functional annotation of predicted genes greatly restricts the potential of mining genome data. We demonstrate that an evolutionary approach, which is independent of functional annotation, has great potential as a tool for genome analysis. We chose the genome of a model filamentous fungus Neurospora crassa as an example. Phylogenetic distribution of each predicted protein coding gene (PCG) in the N. crassa genome was used to classify genes into six mutually exclusive lineage specificity (LS) groups, i.e. Eukaryote/Prokaryote-core, Dikarya-core, Ascomycota-core, Pezizomycotina-specific, N. crassa-orphans and Others. Functional category analysis revealed that only ∼23% of PCGs in the two most highly lineage-specific grouping, Pezizomycotina-specific and N. crassa-orphans, have functional annotation. In contrast, ∼76% of PCGs in the remaining four LS groups have functional annotation. Analysis of chromosomal localization of N. crassa-orphan PCGs and genes encoding for secreted proteins showed enrichment in subtelomeric regions. The origin of N. crassa-orphans is not known. We found that 11% of N. crassa-orphans have paralogous N. crassa-orphan genes. Of the paralogous N. crassa-orphan gene pairs, 33% were tandemly located in the genome, implying a duplication origin of N. crassa-orphan PCGs in the past. LS grouping is thus a useful tool to explore and understand genome organization, evolution and gene function in fungi.
You keep using that word

Formal methods tools have been shown to be effective at finding defects in and verifying the correctness of safety-critical systems such as avionics systems. The recent release of DO-178C and the accompanying Formal Methods Supplement DO-333 will make it easier for developers of software for commercial aircraft to obtain certification credit for the use of formal methods. However, most developers of avionics systems are unfamiliar with formal methods, and most developers of formal methods tools are unfamiliar with certification requirements and processes. This article provides a brief overview of the certification process for commercial aircraft, as well as some of the issues related to the use of formal methods tools in this context.
A Comparison for Secured Transmission of Packets in Wireless LAN Using AES and ECC

Wireless local area network (WLAN) has increased rapidly, providing flexibility and mobility to the device users. This made the technology popular amongst a wide range of users in the universe. But one of the common problems we are overcoming is fragment loss. So far we were following a scheme to retransmit the entire packet if any fragment is lost. Here we introduce a new scheme called Aggregation with Fragment Retransmission (AFR). In this method, multiple packets are aggregated and transmitted in a single large frame reducing the access overheads and hence increasing the data rate. In addition, to investigate the performance added value when each large frame is fragmented so in the case of error in data frame, only the erroneous fragments are to be retransmitted reducing the error control overheads and this decreases the frame error rate without requiring more overhead. The advanced encryption standard is incorporated into existing aggregation method for 802.11 wireless LANs to achieve secure transmission of frames and compute the maximum throughput, optimal frame, and fragment sizes. The proposed system uses weighted fair scheduling with Advanced Encryption Standard (AES) for obtaining optimal fragment size and also used Elliptical curve cryptography (ECC) instead of AES to show that it will reduce the packet size which will be better than AES. We show that our proposed show increase in average rate of successful message delivery over a communication channel and increase in throughput. Keywords - 802.11 wireless LAN, AES, weighted fair scheduling, Fragmentation, security overhead.
Acute myeloid leukemia with expanded erythropoiesis

Depending on previous clinical history, number of blasts in the bone marrow, and results of cytogenetic analysis, hematologic malignancies with expanded erythropoiesis (more than 50% of bone marrow erythropoietic cells) may fall under various categories of the 2008 edition of the World Health
Mindful infrastructure as an enabler of innovation resilience behaviour in innovation teams

Purpose. This paper aims to investigate the principles of high reliability organisations (HROs), present in safety and crisis teams, as applied to innovation teams. Safety and crisis teams cannot fail, as failure leads to disaster and casualties. Innovation teams cannot fail either, as this harms the organisations’ competitiveness and effectiveness. Do HRO principles, rooted in mindful infrastructure, enable innovation resilience behaviour (IRB)? Design/methodology/approach. A study of 18 innovation projects performed by project teams was carried out. A survey by team members/leaders of these teams was completed; team members/leaders of other projects were added to achieve a larger sample. Mindful infrastructure consists of team psychological safety, team learning, complexity leadership and team voice. The analyses assessed the teams’ mindful infrastructures as a causal condition enabling IRB. Findings. Applying qualitative comparative analysis (QCA), the findings indicate that mindful infrastructure enables team IRB, which is a set of team behaviours indicating their resilience when encountering critical incidents. Teams apply different “paths” to IRB. Research limitations/implications. The exploratory study’s generalizability is limited. The findings nonetheless indicate the usefulness of non-linear techniques for understanding different roads to successful innovation processes. Practical implications. HRO principles are applicable by non-HROs. These require investments in organisational learning. Originality/value. HRO studies fail to account for the antecedents of HRO principles. This study groups these antecedents of team behaviour into a mindful infrastructure. QCA has not been applied within the domain of HROs before and only scarcely within the domain of innovation teams.
Genetic Algorithm based Mosaic Image Steganography for Enhanced Security

The concept of mosaic steganography was proposed by Lai and Tsai [4] for information hiding and retrieval using techniques such as histogram value, greedy search algorithm, and random permutation techniques. In the present paper, a novel method is attempted in mosaic image steganography using techniques such as Genetic algorithm, Key based random permutation . The creation of a predefined database of target images has been avoided. Instead, the randomly selected image is used as the target image reduces the enforced memory load results reduction in the space complexity . GA is used to generate a mapping sequence for tile image hiding. This has resulted in better clarity in the retrieved secret image as well as reduction in computational complexity. The quality of original cover image remains preserved in spite of the embedded data image, thereby better security and robustness is assured. The mosaic image is yielded by dividing the secret image into fragments and embed these tile fragments into the target image based on the mapping sequence by GA and permuted the sequence again by KBRP with a key . The recovery of the secret image is by using the same key and the mapping sequence. This is found to be a lossless data hiding method.
Phoneme confusability reduction by using visual information in noisy environment

Robust speech recognition has been a prominent research area in the recent past. The important aspect of speech recognition system is phoneme identification. It is a well established fact that the performance of speech recognition system varies under different background conditions. Using visual information in speech recognition makes the system robust to the problems associated with acoustic noise. In this paper, an automated Audio Visual Phoneme Recognition (AVPR) system has been proposed and implemented for Hindi language. A set of fifty sentences is used to extract the samples of utterances of phoneme and corresponding viseme shape. Mel Frequency Cepstral coefficient (MFCC) based technique is used to form the feature set for audio signal. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used to extract the visual information. Early integration technique is used to integrate the audio and visual feature set. Discrimination analysis based classifier is applied for the recognition of phonemes. To show the effect of interclass confusion associate in the viseme classes, the experiments are performed for 4 viseme classes and 8 viseme classes separately in clean and noisy background conditions. Visual information is utilized to decrease the effect of interclass confusion on phonemes. The overall maximum accuracy is 49.44% and 38.81% for 4 and 8 viseme classes respectively by using linear discrimination. It has been also established that an improvement of 2.91% and 6.07% is obtained by integrating visual information along with audio signal at -10 dB Signal to Noise Ratio (SNR).
Implementation of Four-Switch Brushless DC Motor Drive based on TMS320LF2407 DSP

Brushless DC (BLDC) motor is attracting much interest due to good performance and ease of control for many applications. Moreover, reducing of the drive components is more attractive for low cost applications. This paper presents the design and implementation of a reduced parts BLDC motor drive using the TMS320LF2407A digital signal processor (DSP) produced by Texas instruments. Part reducing is achieved by reducing the number of power switches. After analyzing the performance of the proposed BLDC motor drive, the hardware and software details of the system are explored. Finally, an experimental system included by a BLDC motor, DSP control board, inverters, and rectifier is set up to validate the theoretical results.
Cross-Language Information Retrieval

This paper is an overview of the field of cross-language information retrieval (CLIR) which is concerned with retrieving information in a language different from that used to specify the query. It focuses on technical issues from a linguistic perspective, briefly detailing several important issues and possible solutions. Another point of major interest is usability of CLIR systems, potential users and their requirements.
Case-Based Behavior Recognition to Facilitate Planning in Unmanned Air Vehicles

Abstract : An unmanned air vehicle (UAV) can operate as a capable team member in mixed human-robot teams if the agent that controls it can intelligently plan. However, planning effectively in an air combat scenario requires understanding the behaviors of hostile agents in that scenario, which is challenging in partially observable environments such as the one we study. We present a Case-Based Behavior Recognition (CBBR) algorithm that annotates an agent's behaviors using a discrete feature set derived from a continuous spatio-temporal world state. These behaviors can then be given as input to an air combat simulation, along with the UAV's plan, to predict hostile actions and determine the effectiveness of the given plan. We describe an initial implementation of a CBBR prototype in the context of a goal reasoning agent designed for UAV control.
Complexity of XOR/XNOR Boolean Functions: A Model using Binary Decision Diagrams and Back Propagation Neural Networks

This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.
Specification of Fair Data Practice Principles Using Privacy Policy Languages

During the last decades, multiple initiatives have formulated various fair data practice principles. Today, privacy policies are used to define how personal data can be created, disclosed, stored, used, shared, and destroyed by entities other than the data subject. However, there was a lack of comprehensive framework to reason about the fundamentals of privacy policy-based private data protection schemes. This paper presents a comprehensive yet generic set of fair data practice requirements. Next, the requirements are used as a consistent framework to reason about the capabilities, limitations and challenges of privacy policy-based private data protection in general and privacy policy languages in particular. More specifically, the paper discusses which fair data practice principles can be expressed by P3P.
Speech Rating System through Space Mapping

Predicting human behavior has been the subject of many research areas especially in machine learning. Due to its potential benefits, financially or otherwise, researchers have focused on modeling human behavior from recommending items in an online store to predicting the behavior of an entire ecosystem. In this paper, we make an attempt to predict human preference towards natural speech. The proposed approach makes use of extracted user features from the dataset using Singular Value Decomposition (SVD), features extracted from the wave signal using Mel-frequency cepstral coefficients (MFCC) and Radial Basis Function to map the two feature-spaces. The proposed approach was able to reach a Pearson Correlation Coefficient of 0.92 and a 0.258 MAE when compared to the original average scores. The main contribution of the presented work is the fact that mapping the signal-features (MFCC) into an intermediate feature space (SVD) is far more effective than mapping the signal features directly into the desired output. The proposed algorithm outperformed Support Vector Machines (SVM) in all measures, precisely by 88.14% in terms of correlation and by 48.62% in terms of error.
SDRE-Based Near Optimal Control System Design for PM Synchronous Motor

This paper presents a nonlinear optimal speed controller based on a state-dependent Riccati equation (SDRE) for permanent magnet synchronous motor (PMSM). An SDRE-based near optimal load torque observer is also proposed to provide the load torque information for the controller. In both designs, the stability is analytically proven, and the Taylor series method is used to find an approximate solution because the SDRE cannot be directly solved. The SDRE-based optimal controller and the observer can ensure better control performance such as no overshoot and fast transient response in speed tracking than the linear conventional controllers such as linear quadratic regulator and proportional-integral controller even under the variations of the model parameters and load torque. The proposed SDRE-based control strategy is implemented on a PMSM testbed using TMS320F28335 DSP. The simulation and experimental results are given to prove the feasibility of the proposed control scheme.
Developmental cholinotoxicants: nicotine and chlorpyrifos.

The stimulation of cholinergic receptors in target cells during a critical developmental period provides signals that influence cell replication and differentiation. Accordingly, environmental agents that promote cholinergic activity evoke neurodevelopmental damage because of the inappropriate timing or intensity of stimulation. Nicotine evokes mitotic arrest in brain cells possessing high concentrations of nicotinic cholinergic receptors. In addition, the cholinergic overstimulation programs the expression of genes that evoke apoptosis and delayed cell loss. Effects of cholinesterase inhibitors exhibit many similarities to those of nicotine. Chlorpyrifos administered to developing rats in doses that do not evoke signs of overt toxicity decreased DNA synthesis and caused shortfalls in cell numbers in brain regions enriched in cholinergic innervation. In embryo cultures, chlorpyrifos also evoked apoptosis during neurulation. However, chlorpyrifos also evokes noncholinergic disruption of cell development by interfering with cell signaling via adenylyl cyclase, leading to widespread disruption that is not limited to cholinergic systems. We have tested this hypothesis in vitro with PC12 cells, which lack the enzymes necessary to produce chlorpyrifos oxon, the metabolite that inhibits cholinesterase. Chlorpyrifos inhibited DNA synthesis in undifferentiated PC12 cells, which have relatively few cholinergic receptors. Furthermore, chlorpyrifos was more effective than nicotine and its effects were not blocked by cholinergic antagonists. When cells were allowed to differentiate in the presence of chlorpyrifos, cell replication was inhibited even more profoundly and cell acquisition was arrested. At higher concentrations, chlorpyrifos also inhibited neuritic outgrowth. Thus, chlorpyrifos elicits damage by both noncholinergic and cholinergic mechanisms extending from early stages of neural cell replication through late stages of axonogenesis and terminal differentiation. Accordingly, the window of developmental vulnerability to chlorpyrifos is likely to extend from the embryonic period into postnatal life. ImagesFigure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 10
Security controls for Android

The use of Smartphones in business has grown in the recent years bringing new ways to create and share information. Among the operating systems used by smartphones, Android has the most growing usage rate. Most recent Android versions have some security controls; however, there is a lack in the use of strong security controls for Android 2.3 (Gingerbread), which is the most spread version of this operating system. This leaves vulnerable company and user information stored in such devices. This work presents how to improve Android's security by adding more security controls to devices using the version 2.3 and by this way meet the security levels needed for the business and secure users environments. The security polices where added to Android's source code and to the data space.
An approach to characterization of parallel applications for DSM systems

The paper concentrates on the problem of defining and measuring parameters that characterize behavior of parallel applications targeted to DSM (distributed shared memory) systems. Results are based on the SPLASH-2 application suite. The developed characterization tool Scopa, along with applied simulation environment Limes, are publicly available, and appropriate for performing measurements on other parallel applications, as well.
NIVA: a Robust cluster validity

Clustering aims at extracting hidden structures in datasets. Many validity indices have been proposed to evaluate clustering results; some of them work well when clusters have different densities and sizes and others with different shapes. They usually have a tendency to consider one or two characteristics simultaneously. In this paper, we present a cluster validity index that takes advantage of the density, size and shape of cluster characteristics. The proposed index is experimentally compared with PS, CS and S_Dbw indices using 12 synthetic datasets. Our proposed index improves others indices.
Label-and-Learn: Visualizing the Likelihood of Machine Learning Classifier's Success During Data Labeling

While machine learning is a powerful tool for the analysis and classification of complex real-world datasets, it is still challenging, particularly for developers with limited expertise, to incorporate this technology into their software systems. The first step in machine learning, data labeling, is traditionally thought of as a tedious, unavoidable task in building a machine learning classifier. However, in this paper, we argue that it can also serve as the first opportunity for developers to gain insight into their dataset. Through a Label-and-Learn interface, we explore visualization strategies that leverage the data labeling task to enhance developers' knowledge about their dataset, including the likely success of the classifier and the rationale behind the classifier's decisions. At the same time, we show that the visualizations also improve users' labeling experience by showing them the impact they have made on classifier performance. We assess the visualizations in Label-and-Learn and experimentally demonstrate their value to software developers who seek to assess the utility of machine learning during the data labeling process.