Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation- and Pattern-based State Machine Mining from Embedded C Code

: Automated extraction of state machine models from source code can improve comprehension of software system behavior required for many maintenance tasks and reuse in general. Furthermore, it can be used for subsequent automated processing such as refactoring and model-based veriﬁcation. This paper presents an approach based on normalizing transformations of an input program and a pattern to ﬁnd state machine implementations in the program and to extract relevant information. The results are used to create state machine models containing states, transitions, events, guards and actions. Fine-grained traceability between the model and the source code enables navigation and refactoring of model elements. We evaluate the approach by applying a prototypical implementation to industrial automotive embedded code and show that 74 % of the expected state machine implementations can be completely identiﬁed and 8 % partially.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals

In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance. To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery (MI) data. Individual variability, the critical and longstanding challenge underlying EEG signals, has been successfully handled with the state-of-the-art performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects. Numerical results were promising that the implementation of the graph-structured topology was superior to decode raw EEG data. The innovative deep learning approach was expected to entail a universal method towards both neuroscience research and real-world EEG-based practical applications, e.g., seizure prediction.
Dynamic Social-Aware Peer Selection for Cooperative Relay Management With D2D Communications

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.
Ejaculate Collection Influences the Salivary Oxytocin Concentrations in Breeding Male Pigs

Simple Summary This study aimed to evaluate how the process of ejaculate collection affects oxytocin concentrations in saliva of boars used in artificial insemination. Saliva samples of 33 boars were collected the day before ejaculate collection, during the ejaculation time, and two hours after ejaculate collection. Free oxytocin and oxytocin linked to proteins were quantified in these saliva samples. Oxytocin concentrations during the ejaculation time were higher than the day before with oxytocin linked to proteins showing higher differences. In addition, younger boars, boars with higher libido intensity and boars of the Pietrain breed showed higher values of oxytocin in saliva during ejaculation than the day before. This study demonstrated that ejaculation influences the salivary oxytocin concentrations boars. Abstract The objective of the present study was to evaluate the possible changes of oxytocin concentrations in saliva during and after ejaculate collection in breeding boars usually used in artificial insemination programs. Saliva samples of 33 boars were collected the day before ejaculate collection (DB), during the ejaculation time (T0) and two hours after ejaculate collection (T2). Free oxytocin and oxytocin linked to proteins concentrations were measured by two methods previously developed and validated for saliva of pigs. Younger boars, boars with higher libido intensity and boars of the Pietrain breed showed higher values of oxytocin in saliva during ejaculation than the day before. In addition, boars with higher libido showed higher concentrations two hours after ejaculate collection than during the day before. These changes were of higher magnitude and significance when oxytocin linked to proteins was measured. In conclusion, this study demonstrated for the first time that ejaculation influences the salivary oxytocin concentrations in breeding boars, although this influence varies according to age, libido and breed.
On the Pareto Principle in Process Mining, Task Mining, and Robotic Process Automation

Process mining is able to reveal how people and organizations really function. Often reality is very different and less structured than expected. Process discovery exposes the variability of real-life processes. Conformance checking is able to pinpoint and diagnose compliance problems. Task mining exploits user-interaction data to enrich traditional event data. All these different forms of process mining can and should support Robotic Process Automation (RPA) initiatives. Process mining can be used to decide what to automate and to monitor the cooperation between software robots, people, and traditional information systems. In the process of deciding what to automate, the Pareto principle plays an important role. Often 80% of the behavior in the event data is described by 20% of the trace variants or activities. An organization can use such insights to “pick its automation battles”, e.g., analyzing the economic and practical feasibility of RPA opportunities before implementation. This paper discusses how to leverage the Pareto principle in RPA and other process automation initiatives.
Local Rotation Pattern: A Local Descriptor of Color Textures

: Describing color textures is an extremely challenging problem in pattern recognition and computer vision. In this paper, a new texture feature is proposed and investigated for color texture image classiﬁcation. Based on quaternion representation of color images and quaternion rotation, a Local Rotation Pattern descriptor (LRP) is proposed. Using quaternion to represent color images is done by encoding the three RGB channels into the three imaginary parts of a quaternion. The distance between two color can be expressed as the angle of rotation between two unit quaternions using the geodesic distance to obtain ﬁnally our LRP histograms. Performance in texture classiﬁcation is assessed for three challenging datasets: Vistex, Outex-TC13 and USPtex databases facing to the recent results from the state-of-the-art. Results show the high efﬁciency of the proposed approach.
Reconfigurable photonic integrated devices on silicon

Reconfigurable photonic integrated devices are playing in smart photonic networks, so that it is possible to utilize the resources of the bandwidth/channels optimally and flexibly. Since silicon photonics has become one of the most popular technologies for realizing photonic chips currently, in this paper we focus on our recent work for reconfigurable photonic integrated devices on silicon. It includes the following three parts. The first part is for thermally-tunable optical filters based on micro-ring resonators (MRRs) and waveguide gratings. The second part is for thermo-optic switches, which are designed to be ultra-broad band and polarization-insensitive. The third part is for all-optically reconfigurable photonic integrated devices on silicon.
Diversity of Mosquitoes (Diptera: Culicidae) in the Caatinga Biome, Brazil, from the Widespread to the Endemic

Mosquito fauna in the northeast semiarid region of Brazil, Caatinga biome, are poorly known. Studies on the diversity are scarce and the few surveys available focus on local fauna. In order to understand the ecological pattern of mosquito’s distribution, information available from studies from 2008 to 2015 were gathered. A partitioning framework of the beta diversity, the turnover (βJTU) and nestedness (βJNE) components were used to determine dissimilarity among communities. Eighty-two morphospecies were recorded and 47 of the species were not shared between the areas. The most representative genera were Aedes, Anopheles, Psorophora, Haemagogus, Coquillettidia, and Mansonia, which all include species of medical interest. The communities had high rates of variation, and the mechanism of turnover accounted for the observed diversity pattern. Despite differences in collection methods, the observed dissimilarity may be related to the broad environmental heterogeneity of the biome, the intrinsic relationships of the species with their habitats, and the environmental degradation caused by different types of anthropogenic interference. Considering the mosquito species richness and endemicity, the hypothesis that the Caatinga harbor poor biodiversity is rejected. The spatial variation observed is of particular importance and should be taken into account for the knowledge of Caatinga biodiversity.
Predicting the Environment of a Neighborhood: A Use Case for France

Notion of neighbourhoods is critical in many applications such as social studies, cultural heritage management, urban planning or environment impact on health. Two main challenges deal with the definition and representation of this spatial concept and with the gathering of descriptive data on a large area (country). In this paper, we present a use case in the context of real estate search for representing French neighbourhoods in a uniform manner, using a few environment variables (e.g., building type, social class). Since it is not possible to manually classify all neighbourhoods, our objective is to automatically predict this new information.
Treatment outcomes of bumetanide continuous infusion: A systematic review and meta‐analysis

The clinical use of continuous bumetanide infusion for acute heart failure and volume overload is common. However, there is not enough supporting evidence for the use of continuous bumetanide infusion. Thus, we conducted this systematic review and meta‐analysis aiming to describe the treatment outcomes of continuous bumetanide infusion. We searched Ovid MEDLINE, EMBASE and the Cochrane Library for eligible publications. Inclusion criteria were patients age ≥18 years with bumetanide infusion for heart failure, acute kidney injury (AKI) or volume overload. From 1564 citations, three studies (n = 94 patients) were included in the systematic review and meta‐analysis. The mean dose of bumetanide was 1.08 ± 0.43 mg/hour with a mean treatment duration of 45.09 ± 10.12 hours. Mean urine output in response to continuous bumetanide infusion was 1.88 mL/kg/hour (95% confidence interval [CI], 1.72‐2.05). The incidence of AKI with continuous bumetanide infusion was 24.7% (95% CI, 8.2‐54.6). By using Pearson's correlation coefficient, increasing doses of bumetanide were correlated with increased urine output (P = .026) and increased incidence of AKI (P < .01). There was no correlation between increasing urine output and the incidence of AKI (P = .739). In conclusion, with available evidence, continuous bumetanide infusion may be used in the treatment of acute heart failure or volume overload with close monitoring for new‐onset or worsening AKI.
A novel role for Eip74EF in male reproduction in promoting sperm elongation and male fecundity

Spermatozoa are the most morphologically variable cell type, yet little is known about genes controlling natural variation in sperm shape. Drosophila fruit flies have evolved the longest sperm known, which are evolving under postcopulatory sexual selection, driven by sperm competition and cryptic female choice. Long sperm outcompete short sperm but primarily when females have long seminal receptacles (SRs), the primary sperm storage organ. Thus, selection on sperm length is mediated by SR length, and the two traits are coevolving across the Drosophila lineage, driven by a genetic correlation and fitness advantage of long sperm and long SR genotypes in both males and females. Ecdysone induced protein 74EF (Eip74EF) is rapidly evolving under positive selection in Drosophila, and it is expressed during post-meiotic stages of spermatogenesis, when spermatid elongation occurs. Partial knockdown of Eip74EF leads to shorter sperm but does not affect SR length, suggesting that Eip74EF is involved in promoting spermatid elongation but is not a genetic driver of male-female coevolution. We also found that Eip74EF knockdown has opposing effects on fecundity in males and females, with an increase in fecundity for males but a decrease in females, consistent with its documented role in oocyte maturation. It is possible that knockdown males produce more sperm that are also shorter, which would explain the increase in fecundity, but this hypothesis remains to be tested. Our results document a novel function of Eip74EF in spermatogenesis and demonstrates that this gene influences both male and female reproductive success.
VIP Blowfish Privacy in Communication Graphs

Communication patterns analysis is becoming crucial for global health security especially with the spread of epidemics such as COVID-19 by the means of social contact. At the same time, personal privacy is considered an essential human right. Privacy-preserving frameworks enable communication graph analysis within formal privacy guarantees. In this paper, we present a summary of Blowfish privacy and explore the possibility of applying it in the context of undirected communication graphs. Communication graphs represent social contact or call detail records databases. We define the notions of neighborhood, discriminative secrets, and policies for these graphs. We study several examples of queries and compute their sensitivity. Even though not addressed in the original Blowfish privacy paper, we explore the idea of having a discriminative secret graph per individual. This allows us to treat some persons as VIP and put their privacy on top priority, where other persons can have lower privacy constraints. This may help to offer privacy as a service and increase the utility of the anonymized communication graph to an appropriate level.
A Precautionary Assessment of Systemic Projections and Promises From Sunlight Reflection and Carbon Removal Modeling

Climate change is a paradigmatic example of systemic risk. Recently, proposals for large‐scale interventions—carbon dioxide removal (CDR) and solar radiation management (SRM)—have started to redefine climate governance strategies. We describe how evolving modeling practices are trending toward optimized and “best‐case” projections—portraying deployment schemes that create both technically slanted and politically sanitized profiles of risk, as well as ideal objectives for CDR and SRM as mitigation‐enhancing, time‐buying mechanisms for carbon transitions or vulnerable populations. As promises, stylized and hopeful projections may selectively reinforce industry and political activities built around the inertia of the carbon economy. Some evidence suggests this is the emerging case for certain kinds of CDR, where the prospect of future carbon capture substitutes for present mitigation. Either of these implications are systemic: explorations of climatic futures may entrench certain carbon infrastructures. We point out efforts and recommendations to forestall this trend in the implementation of the Paris Agreement, by creating more stakeholder input and strengthening political realism in modeling and other assessments, as well as through policy guardrails.
Low Penetrance Alleles in Colorectal Cancer: the arachidonic acid pathway

textabstractIn summary, we can conclude that we have successfully identified low penetrance alleles in the PPAR. , PLA2G2A and ALOX15 genes, conferring differential colorectal adenoma risk, and two such alleles in the PTGS2 gene, one of which is also involved in colorectal cancer risk. These results, also in view of the interactions found between some SNPs and fish consumption and NSAID use, reinforce the importance of the role of the AA pathway in colorectal tumorigenesis, and indicate that some of these modifying alleles might contribute to a proportion of sporadic colorectal cancer cases, although the exact underlying mechanisms involved are not understood yet.
Detecting Model View Controller Architectural Layers using Clustering in Mobile Codebases

Mobile applications are one of the most common software projects written nowadays. The software architectures used for building those type of products heavily impacts their lifecycle as the architectural issues affect the internal quality of a software system hindering its maintainability and extensibility. We are presenting a novel approach, Clustering ARchitecture Layers (CARL), for detecting architectural layers using an automatic method that could represent the first step in the identification and elimination of various architectural smells. Unlike supervised Machine Learning approaches, the involved clustering method does not require any initial training data or modelling phase to set up the detecting system. As a further key of novelty, the method works by considering as codebase’s hybrid features the information inferred from both module dependency graph and the mobile SDKs. Our approach considers and fuses various types of structural as well as lexical dependencies extracted from the codebase, it analyses the types of the components, their methods signatures as well as their properties. Our method is a generic one and can be applied to any presentational applications that use SDKs for building their user interfaces. We assess the effectiveness of our proposed layer detection approach over three public and private codebases of various dimensions and complexities. External and internal clustering metrics were used to evaluate the detection quality, obtaining an Average Accuracy of 77,95%. Moreover, the Precision measure was computed for each layer of the investigated codebase architectures and the average of this metric (over all layers and codebases) is 79,32% while the average Recall on all layers obtained is
FREQUENCY OF METABOLIC SYNDROME IN TYPE 2 DIABETES MELLITUS, SUDANESE POPULATION – WAD MEDANI-GEZIRA STATE-SUDAN- FEBRUARY 2015

Background: Metabolic syndrome is a disorder of energy utilization and storage, diagnosed by a co-occurrence of three out of five of the following medical conditions: abdominal (central) obesity, elevated blood pressure, elevated fasting plasma glucose, high serum triglycerides, and low high-density cholesterol levels. This study was to review the prevalence of the metabolic syndrome in Sudanese type 2 diabetes mellitus. Materials and Methods: The study included 80 Sudanese patients; half of them with type 2 diabetes mellitus (cases) and the rest are nondiabetics (control group). Cases and controls blood pressure, abdominal circumference and body mass index were reported. Venous blood samples were taken from all after at least 10 hours fasting for determination of serum level of cholesterol, triglycerides, high density lipoprotein and low density lipoprotein. Glucose level and HbA1c were also measured. Cases and controls were compared in all measures above looking for the differences. The metabolic syndrome was diagnosed according to criteria of national cholesterol education program. Result: In cases fasting blood glucose, waist circumference, high density lipoprotein, low density lipoprotein, triglyceride, total cholesterol, blood pressure were found to be 87.5% (35 cases), 130% (52 cases), 64.5% (28 cases), 77.5% (31 cases), 50% (20 cases), 32.5% (17 cases), 62.5% (25 cases) respectively. In control group, fasting blood glucose, waist circumference, high density lipoprotein, low density lipoprotein, triglyceride, total cholesterol, blood pressure were found to be 100% (40 cases), 150% (59 cases), 100% (40 cases), 100% (40 cases), 100% (40 cases), 100% (40 cases), 95% ( cases ), 100% (40 cases) respectively. Conclusion: In this study, the frequency of the metabolic syndrome was found significantly high in type 2 diabetic patients based in cases compared to nondiabetic control group. The study showed that, the metabolic syndrome was high in type 2 diabetic patients particularly in females (60%) which represent in (22 cases) plus 7 cases with no metabolic syndrome compared to males. In males, the metabolic syndrome occurred in 4 patients and with no metabolic syndrome in 7 patients out of 40% compared to normal control group. The serum level of HDL in female patients was 23(57.5%) which was less than 50 mg/dl, which was poor. The HDL level was lower in females 23(57%) than males 12 (30%) that’s indicated increased level of lipid profile and this can lead to cardiovascular and coronary artery disease as result of thermogenic dyslipidemia.
Computer Aided System for Diagnosis of Skin Cancer Using Classification

Skin cancer is the increasing growth of abnormal skin cells. It occurs when unrepaired DNA damage to skin cells begins mutations, or genetic defects, that lead the skin cells to multiply rapidly and form malignant tumors. Malignant melanoma is considered as one of the most dangerous type of skin cancers as it increases the mortality rate. Computer-aided diagnosis systems can help to detect melanoma early. In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks and NB which are used perform a classification of the different kinds of moles.
Autonomous Sensor Node Powered over WiFi: A Use Case Study

: This paper presents a new approach to the sizing of a radio frequency energy harvesting system. Starting from a sensor node to deﬁne our system constraints such as its energy requirement or its supply voltage, an energy budget analysis protocol is developed to validate whether it is possible to power such a system with a Radio Frequency to Direct Current (RF/DC) converter. We study a converter capable of harvesting RF signals in the Industrial, Scientiﬁc and Medical (ISM) band at 2.45 GHz whose ambient power has been previously characterized. Finally, the duty cycle is determined, i.e., how long would it take the converter to recover the required energy in order to power the sensor node.