Miracle-dz/UniFAR-FTUs huggingface dataset:
The reciprocal relation between sleep and memory in infancy: Memory‐dependent adjustment of sleep spindles and spindle‐dependent improvement of memories

Abstract Sleep spindle activity in infants supports their formation of generalized memories during sleep, indicating that specific sleep processes affect the consolidation of memories early in life. Characteristics of sleep spindles depend on the infant's developmental state and are known to be associated with trait‐like factors such as intelligence. It is, however, largely unknown which state‐like factors affect sleep spindles in infancy. By varying infants’ wake experience in a within‐subject design, here we provide evidence for a learning‐ and memory‐dependent modulation of infant spindle activity. In a lexical‐semantic learning session before a nap, 14‐ to 16‐month‐old infants were exposed to unknown words as labels for exemplars of unknown object categories. In a memory test on the next day, generalization to novel category exemplars was tested. In a nonlearning control session preceding a nap on another day, the same infants heard known words as labels for exemplars of already known categories. Central–parietal fast sleep spindles increased after the encoding of unknown object–word pairings compared to known pairings, evidencing that an infant's spindle activity varies depending on its prior knowledge for newly encoded information. Correlations suggest that enhanced spindle activity was particularly triggered, when similar unknown pairings were not generalized immediately during encoding. The spindle increase triggered by previously not generalized object–word pairings, moreover, boosted the formation of generalized memories for these pairings. Overall, the results provide first evidence for a fine‐tuned regulation of infant sleep quality according to current consolidation requirements, which improves the infant long‐term memory for new experiences.
#BiggBoss - Twitter Driven Temporal Peak Recognition in Long Run Events

Social media has established itself as the most easily accessible way of expressing public views and opinion on any social event. Twitter being a social media platform has the ability to convey precise and crisp public posts. Tweets are posted instantly and through a time period they end up in a huge pile. Eventually it becomes harder to extract the essential peak events for a long running event. In this research work tweets are collected over a period of 10 days related to the hashtag #BiggBoss. These tweets are collected in sequential bins and the proposed peak recognition algorithm using exponential moving average is applied. The result efficiently presents the temporal peaks based on the tweet count deviations.
Contingency Analysis Considering the Transient Thermal Behavior of Overhead Transmission Lines

In conventional contingency analysis (CA), the overloading of transmission lines is identified by the violation of current or power flow rather than the temperature rise. Thus, the transient thermal behavior of transmission lines is usually neglected, which may cause underestimation of power transfer capability and lead to misoperations. In this paper, a new CA method that considers the transient thermal behavior of overhead transmission lines is proposed, where a transient heat balance based power flow (THB-PF) model is established to estimate the temperature dynamics of the lines after contingencies. The THB-PF model is represented by algebraic differential equations, which can be solved as a sequence of extended power flow problems by discretizing the differential equations into difference equations. Moreover, a simplified solution method for the THB-PF is also developed, in which the THB difference equations are linearized and solved separately from the power flow equations. The simplified method can yield an acceptable approximate solution within a considerably shorter time. Numerical simulations verify the effectiveness and efficiency of the proposed methods.
The Hand-gesture-based Control Interface with Wearable Glove System

The paper presents an approach to building a gesture-based control interface with a wearable glove system and a real-time gesture recognition algorithm. The glove-based system is a wireless wearable device with hardware components, including Arduino Nano controller, IMU and flex sensors, and software for gesture recognition. Our gesture recognition methodology requires two stages: 1) Building a library of dynamic gesture models with the reference human gesture graphs; 2) Gesture capturing and evaluating with fuzzy cmeans (FCM) clustering and constructing grammars of gestures by fuzzy membership functions. The system tests were provided with 6 different dynamic gestures to control position and orientation of a quadcopter in VRep simulator that has demonstrated encouraging results with a reasonable quality of real-time gesture-based
Investigating the Effect of Software Metrics Aggregation on Software Fault Prediction

In inter-releases software fault prediction, the data from the previous version of the software that is used for training the classifier might not always be of same granularity as that of the testing data. The same scenario may also happen in the cross project software fault prediction. So, one major issue in it can be the difference in granularity i.e. training and testing datasets may not have the metrics at the same level. Thus, there is a need to bring the metrics at the same level. In this paper, aggregation using Average Absolute Deviation (AAD) and Interquartile Range (IQR) are explored. We propose the method for aggregation of metrics from class to package level for software fault prediction and validated the approach by performing experimental analysis. We did the experimental study to analyze the performance of software fault prediction mechanism when no aggregation technique was used and when the two mentioned aggregation techniques were used. The experimental study revealed that the aggregation improved the performance and out of AAD and IQR aggregation techniques, IQR performs relatively better.
A Machine Learning Approach For Simulating Ground Penetrating Radar

The ability to produce, store and analyse large amounts of well-labeled data as well as recent advancements on supervised training, led machine learning to gain a renewed popularity. In the present paper, the applicability of machine learning to simulate ground penetrating radar (GPR) for high frequency applications is examined. A well-labelled and equally distributed training set is generated synthetically using the finite-difference time-domain (FDTD) method. Special care was taken in order to model the antennas and the soils with sufficient accuracy. Through a stochastic parameterisation, each model is expressed using only seven parameters (i.e. the fractal dimension of water fraction, the height of the antenna and so on). Based on these parameters and the synthetically generated training set, a machine learning framework is trained to predict the resulting A-Scan in real-time. Thus, overcoming the time-consuming calculations required for an equivalent FDTD simulation.
An Efficient Ciphertext Policy-Attribute Based Encryption for Big Data Access Control in Cloud Computing

Big data refer to the variety of the massive amount and the velocity of the data. The traditional databases are not suitable to store the big data because of huge volume. The cloud computing is a best and optimal choice to store and process the big data. However, there is an important issue to be addressed before storing the big data into the cloud that is maintaining the privacy and access control, in which the data must encrypt and restrict the unauthorized user access by providing effective access control. Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a promising technique to provide both encryption and effective access control. The existing CP-ABE schemes are not apt for big data in cloud since it creates heavy computation overhead. In this paper, we propose an efficient ciphertext policy attribute based encryption scheme with less computation overhead in the encryption process by providing the short ciphertext and reducing the decryption time by minimizing the pairing operations required. Through the security analysis we demonstrate that our design is secure under chosen-plaintext attack and user collusion attack. The performance analysis proves that our scheme is efficient when compared with existing schemes.
Pumilacidins from the Octocoral-Associated Bacillus sp. DT001 Display Anti-Proliferative Effects in Plasmodium falciparum

Chemical examination of the octocoral-associated Bacillus species (sp.) DT001 led to the isolation of pumilacidins A (1) and C (2). We investigated the effect of these compounds on the viability of Plasmodium falciparum and the mechanism of pumilacidin-induced death. The use of inhibitors of protein kinase C (PKC) and phosphoinositide 3-kinase (PI3K) was able to prevent the effects of pumilacidins A and C. The results indicated also that pumilacidins inhibit parasite growth via mitochondrial dysfunction and decreased cytosolic Ca2+.
Oncogenic transformation of normal breast epithelial cells co-cultured with cancer cells

ABSTRACT The heterogeneity in human breast cancer poses a challenge for effective treatment. Better understanding of tumor initiation and development will help to resolve this problem. Current models explaining intratumoral diversity include cancer stem cells, clonal evolution and cancer cell dedifferentiation and reprogramming. Herein, a new model, cancer transmission, is proposed to explain cancer heterogeneity. We found breast cancer cells (MCF10A.NeuT) were capable of transforming normal mammary epithelial cells (MCF10A). The transformed cells exhibited cancerous properties including enhanced proliferation and migration, loss of apical-basal polarity and depolarized acini structure associated with epithelial-mesenchymal transition (EMT). The transformed MCF10A cells displayed distinct EMT characteristics compared to parental cells. We further showed that cancer cell-secreted factors were sufficient to induce cancerous transformation of normal cells. Furthermore, transformed cells were resistant to radiation treatment, providing new insights into mechanisms underlying therapeutic resistance.
Investigating Order Information in API-Usage Patterns: A Benchmark and Empirical Study

Many approaches have been proposed for learning Application Programming Interface (API) usage patterns from code repositories. Depending on the underlying technique, the mined patterns may (1) be strictly sequential, (2) consider partial order between method calls, or (3) not consider order information. Understanding the trade-offs between these pattern types with respect to real code is important in many applications (e.g. code recommendation or misuse detection). In this work, we present a benchmark consisting of an episode mining algorithm that can be configured to learn all three types of patterns mentioned above. Running our benchmark on an existing dataset of 360 C# code repositories, we empirically study the resulting API usage patterns per pattern type. Our results show practical evidence that not only do partial-order patterns represent a generalized super set of sequential-order patterns, partial-order mining also finds additional patterns missed by sequence mining, which are used by a larger number of developers across code repositories. Additionally, our study empirically quantifies the importance of the order information encoded in sequential and partial-order patterns for representing correct co-occurrences of code elements in real code. Furthermore, our benchmark can be used by other researchers to explore additional properties of API patterns.
A family of silicon transporter structural genes in a pennate diatom Synedra ulna subsp. danica (Kütz.) Skabitsch

Silicon transporters (SIT) are the proteins, which capture silicic acid in the aquatic environment and direct it across the plasmalemma to the cytoplasm of diatoms. Diatoms utilize silicic acid to build species-specific ornamented exoskeletons and make a significant contribution to the global silica cycle, estimated at 240 ±40 Tmol a year. Recently SaSIT genes of the freshwater araphid pennate diatom Synedra acus subsp. radians are found to be present in the genome as a cluster of two structural genes (SaSIT-TD and SaSIT-TRI) encoding several concatenated copies of a SIT protein each. These structural genes could potentially be transformed into “mature” SIT proteins by means of posttranslational proteolytic cleavage. In the present study, we discovered three similar structural SuSIT genes in the genome of a closely related freshwater diatom Synedra ulna subsp. danica. Structural gene SuSIT1 is identical to structural gene SuSIT2, and the two are connected by a non-coding nucleotide DNA sequence. All the putative “mature” SITs contain conserved amino acid motifs, which are believed to be important in silicon transport. The data obtained suggest that the predicted “mature” SIT proteins may be the minimal units necessary for the transport of silicon is S. ulna subsp. danica. The comparative analysis of all available multi-SITs has allowed us to detect two conservative motifs YQXDXVYL and DXDID, located between the “mature” proteins. Aspartic acid-rich DXDID motif can, in our opinion, serve as a proteolysis site during the multi-SIT cleavage. The narrow distribution of the distances between CMLD and DXDID motifs can serve as additional evidence to the conservation of their function.
Multi-Exposure Image Fusion Based on Patch using Global and Local Characteristics

In this paper, we propose an algorithm that improves the weight map part consisting of signal strength, signal structure, and mean intensity. The patch-based conventional weight map causes the brightness of the image to be shifted to one side, resulting in loss of image information, unexpected artifacts, and an overall unbalance in image brightness. In this study, we propose a novel algorithm by improving the weight map. First, the order-statistic filter using maximum values. Second, the unsharp masking filter using Laplacian. Third, the linear combination using gamma transformation. The proposed algorithm prevents the loss of image information by reducing the over-saturation of the image, accurate representation of dark and bright areas by increasing contrast, and preserve the detail such as the edge. Through subjective and objective experimental results, it is confirmed that the proposed algorithm shows better performance than the conventional algorithms.
Wireless Distributed Computing: Processing Time Analysis and Optimization

In a wireless distributed computing (WDC) setup, N wireless devices (called servers) jointly complete a large computational task. For this purpose, the original computationally intensive task is broken down into smaller sub-tasks. Now, each of the sub-tasks is assigned to and completed by one of the servers. One important measure of WDC performance is the processing time meaning that how long it would take for the servers to accomplish the original heavy task. Here, we derive a closed-form expression for the average processing time of an N -server WDC system. Then, by defining a cost function based on the number of used servers and the total processing time of the system, we find the optimal number of servers that should be accessed in order to minimize this cost. Finally, we present simulation results to verify our mathematical derivations and insights.
Modeling and position control of a high performance twisted-coiled polymer actuator

This paper presents the property characterization, modeling and position control of a hybrid twisted-coiled polymer actuator made from Spandex and nylon fibers. The actuator is low-cost, easy to fabricate and light-weight. Moreover, it can generate large displacement and provide Joule heating capability. Based on the energy method and experimental observations, a nonlinear dynamic model is developed to estimate the displacement of the actuator. The model is then utilized in a finite time controller, which guarantees the robustness of the actuator over model variations and external disturbances. The controlled system is proved to be stable using Lyapunov stability theory and verified through experiments. Experimental results show good control performance with highest position error of 0.25mm and root-mean squared error of 0.11 mm at steady state, nearly 2 times smaller compared with those of a computed-torque method controller.
A K-Means Clustering Based Message Forwarding Model for Internet of Things (IoT)

Internet of Things (IoT) is establishing to be the next stage of Internet resulting in a relation between internet connection data generating smart objects. IoT is understood as a seamless interconnection of sensor objects with the computational world. Clustering these objects is very helpful for data analytics generating a combination of structure of similar types of object characteristics. Clustering, therefore, is very useful in IoT for improving the Quality of Service (QoS) and organizing the resources. In this work, a K-Means clustering based resource allocation model for IoT has been discussed. The performance has been evaluated primarily for the response time realized for transmission of a message from the source to the destination. Simulation study reveals the effectiveness of the model.
Multi-Zone Building Temperature Control and Energy Efficiency Using Autonomous Hierarchical Control Strategy

This paper presents an autonomous hierarchical control (AHC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. The control objective is to keep the temperature of each room and zone at a comfortable level while minimizing energy and demand costs. This control strategy is divided into two layers. The upper layer solves an open loop control problem to autonomously generate optimal references by optimizing the demand and energy costs of building HVAC system and the value of the predicted mean vote (PMV) under the time-of-use (TOU) rate structure, where the requirement of the thermal comfort is within a comfortable range. The lower layer is designed as a closed-loop MPC controller to track the trajectory references. Simulation results show that the proposed control strategy is capable of reducing demand and energy costs in comparison with the previous control strategy while maintaining the temperature of each zone at a comfortable level.
Deep Learning on 1-D Biosignals: a Taxonomy-based Survey

Summary Objectives: Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field. Methods: A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model. Results: Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography, photoplethysmography, electrooculography, continuous glucose monitoring, acoustic respiratory signal, blood pressure, and electrodermal activity signal, while ballistocardiography or seismocardiography have yet to be analyzed using deep learning techniques. In supervised and unsupervised applications, CNNs and restricted Boltzmann machines are the most and least frequently used, (n = 34) and (n = 15), respectively. Conclusion: Our key-code classification of relevant papers was used to cluster the approaches that have been published to date and demonstrated a large variability of research with respect to data, application, and network topology. Future research is expected to focus on the standardization of deep learning architectures and on the optimization of the network parameters to increase performance and robustness. Furthermore, application-driven approaches and updated training data from mobile recordings are needed.
Comparing marine distribution maps for seabirds during the breeding season derived from different survey and analysis methods

Understanding how seabirds use the marine environment is key for marine spatial planning, and maps of their marine distributions derived from transect-based surveys and from tracking of individual bird’s movements are increasingly available for the same geographic areas. Although the value of integrating these different datasets is well recognised, few studies have undertaken quantitative comparisons of the resulting distributions. Here we take advantage of four existing distribution maps and conduct a quantitative comparison for four seabird species (black-legged kittiwake Rissa tridactyla; European shag Phalacrocorax aristotelis; common guillemot Uria aalge; and razorbill Alca torda). We quantify the amount of overlap and agreement in the location of high use areas identified from either tracking or transect samples and use Bhattacharyya’s Affinity to quantify levels of similarity in the general distribution patterns. Despite multiple differences in the properties of the datasets, there was a far greater degree of overlap than would be expected by chance, except when adopting the most constrained definition of high use. Distance to the nearest conspecific colony appeared to be an important driver of the degree of similarity. Agreed areas of highest use tended to occur close to colonies and, with increasing distance from colonies, similarity between datasets declined and/or there was similarity in respect of their being relatively low usage. Interpreting reasons for agreement between data sources in some areas and not others was limited by an inability to control for the multiple potential sources of differences from both the sampling and modelling processes of the underlying datasets. Nevertheless, our quantitative comparative approach provides a valuable tool to quantify the degree to which an area’s importance is corroborated across multiple datasets, and therefore confidence that an important area has been correctly identified. This can help prioritise where the implementation of conservation measures should be targeted and identify where greatest scrutiny is required of the potential adverse environmental effects of any planned anthropogenic activities.
Laser Scan Matching in Robot Navigation

In this study, Laser scan matching is implemented for simultaneous localization and mapping in robot navigation. Two algorithms are presented to accomplish the laser scan matching using normal distribution transform. Firstly, an algorithm is proposed to collect and store the laser scan data obtained from the robot scan. The second algorithm is to perform scan matching and map building. Using the data of good scan matches, a neural network is trained and tested. The experimental work is carried out on the Turtlebot robot model in Gazebo simulator using MATLAB software.