Miracle-dz/UniFAR-FTUs huggingface dataset:
Organization Type and Size Based Identification of Requirements Change Management Challenges in Global Software Development

Global Software Development (GSD) is adopted by organizations to develop quality software at relatively low cost. Requirement Change Management (RCM) plays a key role in overall success of a GSD project. The objective of this study is to identify the challenges of RCM process by adopting systematic literature review (SLR) and validate them by employing questionnaire survey approach in real world practices. A total of 25 challenges were identified through SLR and empirical study. We have further classified the identified challenges into client and vendor organizations with an aim to understand RCM challenges in the context of both types of GSD organizations. The identified challenges were also categorized into three core types of the organization size (small, medium, large), that highlights the significance of each challenge for specific organizational size. The results indicate that there is a moderate correlation between the ranking of these challenges in the literature and the survey results. The finding of this study has the potential to help the GSD organizations in addressing the problems related with RCM in GSD projects.
Emergency Patient Identification System

Emergency patient identification system (EPIS) will enable the more powerful quality system in the health industry. This research study was conducted to develop an EPI system, which is a complete patient-based medical information recording system. This system will help to identify the patients uniquely. Doctors can get patients' latest situation and can make quick decisions to do the treatments in emergencies. To achieve this approach, authors use the patient's fingerprint, face recognition technology, and eye recognition to identify the person. The patient can view his medical records; system reminds the notifications. The system helps the patients in storing and tracking the diet weight, medications, allergies, health history, fitness, lab results, x-rays, blood pressure, ongoing surgeries, drug reminders, doctor visits, doctor's appointments, images and more. The system helps the user in receiving and sending necessary health-related information to the doctors and hospitals. This system has many unique features; an emergency will help to find nearest hospitals. This can add patient's family member's history so it can help to remind his present medical situation. The main system is working on web interface; another part is offered as a mobile application.
Commonality Evaluation Method of Civil Aircraft Maintenance Technical Publications Based on Improved Design Structure Matrix

The commonality evaluation of civil aircraft operation support products has become increasingly important in the overall design and operation support phase of modern civil aircraft products. The commonality evaluation of technical publications is an important part of that, but the evaluation methods suitable for it are still lacking. Therefore, by taking maintenance technical publications as the research object, a commonality evaluation method based on the improved design structure matrix is proposed in this paper. By extracting key indexes, the commonality evaluation index system of civil aircraft maintenance technical publications is established. The traditional design structure matrix is improved, and the structural index is quantified based on the improved design structure matrix. Finally, the commonality of technical publications is characterized by the calculated modified Hamming distance between the improved design structure matrices of manuals, and an example is given to verify it.
A Review of Research Work on Network-Based SCADA Intrusion Detection Systems

Specific intrusion detection systems (IDSs) are needed to secure modern supervisory control and data acquisition (SCADA) systems due to their architecture, stringent real-time requirements, network traffic features and specific application layer protocols. This article aims to contribute to assess the state-of-the-art, identify the open issues and provide an insight for future study areas. To achieve these objectives, we start from the factors that impact the design of dedicated intrusion detection systems in SCADA networks and focus on network-based IDS solutions. We propose a structured evaluation methodology that encompasses detection techniques, protected protocols, implementation tools, test environments and IDS performance. Special attention is focused on assessing implementation maturity as well as the applicability of each surveyed solution in the Future Internet environment. Based on that, we provide a brief description and evaluation of 26 selected research papers, published in the period 2015–2019. Results of our analysis indicate considerable progress regarding the development of machine learning-based detection methods, implementation platforms, and to some extent, sophisticated testbeds. We also identify research gaps and conclude the analysis with a list of the most important directions for further research.
Classifying Packed Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network

In recent years, the variety and quantity of malware has increased rapidly, which makes classification based on fixed features very difficult. In order to better preserve the integrity of the malware, we extract the control flow graph of malware as the feature information. However, control flow graph of a packed malware consists of both unpack control flow graph and local control flow graph. In order to obtain a clean control flow graph, we analysis the packed malware dynamically and apply an algorithm to strip the unpacking routine from the graph. With the rapid development of Graph Neural Network (GNN), we use Deep graph Convolutional Network (DGCNN) to classify control flow graph data. Experimental results show that the proposed method can achieve 96.4% accuracy.
Analysis of Electroencephalogram for Cognitive Effects of Human Being Before and After Meditation

The meditation is significantly suggested by the medical practitioners and health care organization for the individuals to relieve the stress and pain. The meditation has severe impact of human beings brain functions. Thus, it is required to analyze the neuro-cognitive effects of meditation using the more effective tool called Electroencephalogram (EEG) to record the brain activities of meditators. In this paper, it is analyze the EEG for neuro-cognitive effects on human being for before and after a month of meditation intervention for 15 subjects. The EEG analysis is done using the energy spectral analysis, statistical and spatial features. From the analysis it is found that, the variance for before meditation (16.8133) is higher and variance for after meditation (7.4628) is lower. Mahalanobis distance for before meditation (0.0848) is lower and for after meditation (0.1537) is higher. The p-value is less than significance level (p < 0.05) for both features indicating that, good discrimination between control and meditators group. The Delta, alpha and theta rhythms’ energy increases in EEG after meditation. It is observed that after a month of regular meditation, increases alpha and theta energy in a frontal lobe. Hence relaxation improves after meditation and the brain cognitive function excel in meditators.
Head Concepts Selection for Verbose Medical Queries Expansion

Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users’ query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, and iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.
Design of Discrete-Time Backstepping Sliding- Mode Control for LCL-Type Grid-Connected Inverter

This study designs a discrete-time backstepping sliding-mode control (DTBSMC) method for an LCL-type grid-connected inverter. Firstly, the dynamic model of a discrete-time three-order system is derived, and a discrete-time backstepping control method cascading with the sliding-mode control theory is designed via Lyapunov stability verification. Moreover, the system state equation is transformed into a special form by using a time-varying mapping for overcoming the difficulty of a non-causal problem. Besides, through the recursively subsystem design for the high-order LCL-type inverter, the asymptotic system stability can be ensured by step-by-step virtual control designs. In addition, the proposed method can combine both the advantages of the backstepping control method and sliding-mode control theory. Therefore, the inverter system can have strong robustness under the condition of a power grid with varied grid impedances. Finally, the effectiveness of the proposed DTBSMC method is verified by numerical simulations and experimental results in comparison with a traditional proportional-resonant (PR) method and a backstepping control method.
Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

In recent years, exoskeletons have been widely accepted as rehabilitative and walking assistive devices for either healthy persons or patients with mobility impairment. Depending on mobility of the wearers, the control strategies for exoskeletons may differ significantly. This paper aims at developing an admittance control framework for exoskeletons to assist healthy persons in safer, faster, or more energy-efficient walking. A crucial step to accomplish assistive walking for healthy persons is to detect their intention. Direct sensing of the wearer’s biological signals, such as electromyography (EMG) and electroencephalography (EEG), requires additional sensors, which increases the cost and makes it inconvenient for the wearer to put on and take off the exoskeleton. Instead, we propose to detect the wearer’s intention by estimating the total torques applied from the wearer to the human-exoskeleton system based on the motor current and joint angles. Then the velocity commands to the joint motors are generated according to the estimated torque and a predefined mechanical admittance function of each joint. Consequently, the exoskeleton complies with the wearer’s intention. Rigorous theoretical analysis is performed on robust stability of the closed-loop system. Then experiments are carried out to verify the accuracy and robustness of the proposed torque estimation algorithm. In addition, experimental data show that the wearer’s gait can be shaped in a desired way by choosing an appropriate admittance function in the proposed admittance control loop.
Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification

In this study, we investigate the detection of cardiomegaly on frontal chest radiographs through two alternative deep-learning approaches - via anatomical segmentation and via image-level classification. We used the publicly available ChestX-ray14 dataset, and obtained heart and lung segmentation annotations for 778 chest radiographs for the development of the segmentation-based approach. The classification-based method was trained with 65k standard chest radiographs with image-level labels. For both approaches, the best models were found through hyperparameter searches where architectural, learning, and regularization related parameters were optimized systematically. The resulting models were tested on a set of 367 held-out images for which cardiomegaly annotations were hand-labeled by two independent expert radiologists. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated. The performance of the segmentation-based system with an AUC of 0.977 is significantly better for classifying cardiomegaly than the classification-based model which achieved an AUC of 0.941. Only the segmentation-based model achieved comparable performance to an independent expert reader (AUC of 0.978). We conclude that the segmentation-based model requires 100 times fewer annotated chest radiographs to achieve a substantially better performance, while also producing more interpretable results.
The parasitic theory of sexual reproduction: parasitism in unisexual and bisexual geckos

The parasitic theory of sexual reproduction was extended to account for the existence of unisexual higher vertebrates. Because unisexual lizards arose through hybridization of two sexual species, the hybrids’ genotypes should be maximally heterozygous resulting in hybrid vigor. We therefore predicted that at least some unisexuals should be resistant to parasitism. To test the above prediction, we examined parasitism rates in a unisexual (Lepidodactylus lugubris) and a sexual gecko species (Hemidactylus Frenatus) which live sympatrically on the island of Hawaii. Sixteen percent of the sexual geckos were found to have pseudotumors, 36% of the sexuals were infested with mites and the sexuals were also found to be susceptible to cestode, nematode and pentastomid infections. None of the unisexual geckos examined showed evidence of pseudotumor infection, mite infestation (even after they were housed with mite infested geckos) or cestode infections. The unisexuals were found to harbor pentastomids and nematodes although in the latter case the unisexuals were expelling adult nematodes in their faeces (an immune response to nematode infection). The above data lend support to our prediction that some unisexual species should be highly resistant to parasitism because they are maximally heterozygous.
Placement and Capacity Optimization of Unified Power Flow Controller using Imperialist Competitive Algorithm

This paper offers a consideration to take in implementing a FACTS device to improve the power quality and service reliability provided by power utility companies. The installation of the device must be planned meticulously to get the most benefit from it. A Unified Power Flow Controller (UPFC) is considered to be used in the 500 kV JAMALI power system in Indonesia. An optimization method based on the Imperialist Competitive Algorithm (ICA) is proposed to find the best location and size of the device. The installation of a common capacitor bank is used as a comparison. The results show that the addition of bank capacitors and UPFC can improve the voltage profile so that the bus voltage can be maintained within the specified value limits (0.95-1.05 p.u.). The use of ICA method for UPFC optimization enabled the reduction of active power losses by 13.92% and the reactive power losses by 14.16%. It is better than the losses reduction obtained on bank capacitors optimization, which was 9.96% of active power losses and 4.28% of reactive power losses.
Impact of Seasonal Conditions on Vector-Borne Epidemiological Dynamics

Vector-borne diseases such as malaria, dengue fever, West Nile virus, and so forth are some of the most prominent threats to human health. They are transmitted to the human population by infected insects or by direct transmission between humans. The epidemic process relies on suitable environmental and climatic conditions. Indeed, climatic factors affect the development of pathogens in vectors as well as the population dynamics of the vectors impacting significantly the incidence of disease in the human population. While the influence of the climatic conditions on Vector-borne diseases is well-documented, there is a strong need to design more realistic epidemiological models incorporating environmental features that show a close relationship with the epidemic process observed in the human population. Indeed, classical models concentrate either on the climatic influence on the vector population dynamics or on the connectivity patterns of the host population, loosing the full picture of the epidemic process dynamics. Inspired by real data of infectious diseases, a Seasonal Susceptible-Infected-Recovered (Seasonal SIR) epidemiological model is developed and analyzed. The proposed model incorporates the influence of the temperature variations together with the heterogeneous structure of the human interaction network on the spreading process of vector-borne diseases. Simulations are performed in order to get a better understanding of the climate variations and of the heterogeneous nature of the contact network on the transmission dynamics. Results show that failing to incorporate these features on the model can lead to a poor estimation of the maximum fraction of infected individuals in the host population. Furthermore there is a serious influence on the time needed to reach this maximum. The Seasonal SIR model proves to finely model the dynamics of outbreaks observed in real-world situations. It provides a basis for more effective predictions of disease outbreaks that can be used in order to implement appropriate control measures to contain epidemics.
A Multipath Transport Scheme for Real-Time Multimedia Services Based on Software-Defined Networking and Segment Routing

It is still a challenging task to provide satisfactory quality of experience (QoE) to users by traditional routing mechanism in current public Internet for real-time multimedia services with high bandwidth and strict end-to-end latency constraint. Multipath transmission is a promising way to improve the performance of data delivery, due to its advantage in bandwidth aggregation capacity over multiple paths to speed up high volume transfers between endpoints. However, existing multipath transmission has many disadvantages such as the requirements for end-host support (i.e. the necessary multi-homed network setting, and operating system or upper applications updates of user devices), and blindingly pursuing maximum of transmission throughput. Path allocation and end-to-end transmission control are two important factors affecting the quality of multipath transmission. As an emerging network architecture, software-defined networking (SDN) is flexible, manageable, and responsive to rapid changes in traffic requirements. In this paper, we propose a multipath transport scheme based on SDN with segment routing (SR) which can meet the bandwidth and low end-to-end transmission latency requirements of real-time interactive multimedia services. The centralized SDN controller is extended to allocate multiple disjoint paths meeting bandwidth requirement meanwhile balancing the network load. The proprietary multipath media server (MMS) dynamically controls the assignment between subflows and routing paths to reduce the end-to-end transmission latency and thus improve QoE of end-users. To show the feasibility of our approach, we construct an SDN platform using Mininet. Simulation results show that our approach performs better compared to the conventional approaches in terms of traffic balancing and end-to-end transmission delay.
The effect of Lasius niger (Hymenoptera, Formicidae) ant nest on selected soil chemical properties

Summary The set of chemical properties: total, available and watersoluble P, total C, available K, Na, Ca and pH in H2O and KCl were studied in 64 nests of Lasius niger and in surrounding soils (control). Ant nests were located in wide range of soil conditions. Additionally, spatial distribution of those parameters was studied in three nests. The pooled average values for all investigated soil parameters, except that of Ca and C were significantly higher in the nests than in surrounding soil. At the same time, chemical properties of the nest were effected by chemical properties of surrounding soil; pH value in the nests increased in comparison to control in acidic soils but the opposite was true in alkaline soil. Content of available P in the nest was higher than in surrounding soil. As the content of available P in surrounding soil increased the enrichment of the nest (increase of available P in nest in comparison with control) was higher. Adjacent soils with low available Ca and total C supported nests that were higher in these elements while surrounding soil with higher Ca ans C supported nests that were lower in these elements. The changes in chemical properties were more pronounced in the aboveground than in the belowground part of the nest. The results indicates that the main processes taking part in chemical changes of soil in the ant nests are soil mixing due to excavation of deeper soil layers, enrichment of substrate by ants (by rest of food, excreta etc.) and interactions of nest chemistry with other physical, chemical and microbial parameters in the nest.
Machine Learning Approaches for Emotion Classification of Music: A Systematic Literature Review

Music is ubiquitous in every corner of the world. Fundamental mechanisms for how a listener perceives emotions in music as well as the way in which the emotional goal of a composer is expressed through music have been widely explored. Not only humans but also machines can recognize emotions in music once they are trained. As a result, various machine learning approaches are tested by previous researchers for their capability in music emotion recognition. To identify the current status of research and the research gaps in the above domain, we conducted a systematic literature review, through which we investigated several key aspects including types of music, acoustic features, feature extraction mechanisms, classification algorithms, and their performance. Six electronic databases were searched for studies published until 2018. Initial set of studies comprised of 198 studies from which 14 were selected for detailed analysis. Findings revealed that a considerable number of studies have used western music whereas other cultural-specific music still remains to be explored. Acoustic features pertaining to pitch, intensity, timbre, tempo, rhythm, melody, and harmony have been commonly used. While Support Vector Machine, Naive Bayes, and k-Nearest Neighbor are among the frequently used standard classifiers, Fuzzy classifiers and ensemble learning also have been attempted. The discrepancies among the classifier performances reported in previous studies could be partially attributed to the differences in the key aspects we have considered. The study makes a significant contribution by providing a comprehensive and up-to-date review of the previous attempts made in the selected domain.