Miracle-dz/UniFAR-FTUs huggingface dataset:
Singer Gender Classification using Feature-based and Spectrograms with Deep Convolutional Neural Network

The task of music information retrieval (MIR) is gaining much importance since the digital cloud is growing sparklingly. An important attribute of MIR is the singer-id, which helps effectively during the recommendation process. It is highly difficult to identify a singer in the case of music as the number of signers available in the digital cloud is high. The process of identifying the gender of a singer may simplify the task of singer identification and also helps with the recommendation. Hence, an effort has been made to detect the gender information of a singer. Two different datasets have been considered. Of which, one is collected from Indian cine industries having 20 different singer details of four regional languages. The other dataset is standard Artist20. Various spectral, temporal, and pitch related features have been used to obtain better accuracy. The features considered for this task are Mel-frequency cepstral coefficients (MFCCs), pitch, velocity, and acceleration of MFCCs. The experimentation has been done on various combinations of the mentioned features with the support of artificial neural networks (ANNs) and random forest (RF). Further, the genetic algorithm-based feature selection (GAFS) has been used to select the suitable features out of the best combination obtained. Moreover, we have also utilized the recent popular convolutional neural networks (CNNs) with the support of spectrograms to obtain better accuracy over the traditional feature vector. Average accuracy of 91.70% is obtained for both the Indian and Western clips, which is an improved accuracy of 3% over hand engineering features.
Structural Decision Theory Draft

Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. I found that it is structural models, rather than intervention analysis, serves as a valuable formal tool for a range of realistic decision problems that involve mixed causal mechanisms. The thesis concludes that structural models provide a more general framework for rational decision-makers.
Improving Joint Attention in Children with Autism: A VR-AR Enabled Game Approach

Autism Spectrum Disease (ASD) can be referred to as one type of neurodevelopmental disorder, causing communication or behavioral disabilities in multiple severity levels. These challenges cause difficulties in communication, interaction, problem-solving, etc. Though having no persistent cure for ASD, several treatments and intervention strategies prevail for their behavioral development. These treatments include social skill training, sensory integration, and occasional therapy, etc. Many of these researches highly emphasize joint attention, which is the common issue of most autism cases. Joint or shared attention is highly essential for language and communication skill development. In this work, we proposed a game-based approach to improving the joint attention of children with autism. This approach focuses on the efficiency of skilling up their shared attention by interacting with digital models through problem-solving. We integrate AR (Augmented Reality) and VR (Virtual Reality) technologies for solving this issue. Using these technologies, we have designed an interactive multi-staged game module, including several types of real-life alike problems. Problem-solving and interacting with these mascots can help children build up learning and communication skills, which sometimes real-life interaction may find challenging. Again, eye screening and health can be issues in the device-centric scheme. An analytical survey has been presented in this work, confirming that all these health issues have been carefully considered. Implementation and outperforming results of this work will give a clear stand about the effectiveness of this module. Besides, we present sufficient analytics confirming that this module can also be efficient for normal children.
Plasmon Coupled Fluorescence in Gold Nanorods for Enhanced Optical Detection of Oligonucleotides

The tips of gold nanorods (AuNRs) are hotspot regions with remarkable near field amplification of incident electromagnetic field, capable of enhancing the fluorescent emission of dyes in their vicinity. Molecular beacons are oligonucleotide sequences that emit a fluorescent signal when hybridization with target sequence occurs. The detection range of these probes is restricted by the intensity of the “on” fluorescent signal relatively to the “off” state. Therefore, the conjugation of AuNRs with selectively positioned molecular beacons is a promising strategy for the development of a nanohybrid sensor for enhanced optical detection of oligonucleotides. This work is integrated in the ongoing research for the development of said sensor, and gives a meaningful contribution through the characterization of fluorescence emission of the molecular beacons and DNA-AuNRs nanohybrids. First, fluorescence enhancement effects in AuNRs (25 x 71 nm) loaded with a ATTO647N-labelled dsDNA 10-bp spacer were characterized at single particle level through confocal fluorescence lifetime microscopy. Single particle study of DNA-AuNRs nanohybrids supported that indiscriminately loaded AuNRs required 10fold more dye to reach fluorescence emission Tip-selective AuNRs. Then, molecular beacon probes bearing the organic quenchers DDQII and QSY21 were studied in the absence of AuNRs, in regard to their quantum yield and fluorescence lifetime by conventional spectroscopy. The molecular beacons showed negligible quantum yield increase in the presence of target sequence, leading to their exclusion, under the conditions studied, as suitable oligonucleotide recognition moieties for the in-development DNA-AuNRs nanobiosensors.
Genome-wide Identi cation and Function Analysis of HMAD Gene Family in Cotton (Gossypium Spp.)

Qinqin Wang Xinjiang Agricultural University Xuke Lu CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Xiugui Chen CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Lanjie Zhao CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Mingge Han CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Shuai Wang CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Yuexin Zhang CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Yapeng Fan CAAS CRI: Chinese Academy of Agricultural Sciences Cotton Research Institute Wuwei Ye (  yew158@163.com ) Chinese Academy of Agricultural Sciences Cotton Research Institute
Triggering of intracellular aggregation and cytotoxicity by immature forms of human Islet Amyloid Polypeptide

[Habilitações Académicas] Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina Dissertação para obtenção do Grau de Mestre em [Engenharia Informática] Orientador: Doutora Regina Menezes Echaniz, PhD, Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa Co-Orientador: Doutora Margarida Castro-Caldas Braga, PhD, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa
UvA-DARE ( Digital Academic Repository ) Calculating the angle between jet axes

We study the angle between i) the standard jet axis, ii) the axis of a jet which has been groomed using soft drop, with reduced sensitivity to soft radiation, iii) the jet axis obtained with the winner-take-all recombination scheme, which is insensitive to soft radiation at leading power. We calculate the distributions for these angles at nextto-leading logarithmic accuracy, including non-global logarithms. The angle between the standard and groomed jet axis directly probes soft wide-angle radiation, leading to a novel factorization formula. This angle is also very sensitive to nonperturbative physics, which is directly connected to nonperturbative contribution to the rapidity anomalous dimension for transverse momentum distributions. Comparing our predictions to Pythia we find good agreement, and we foresee applications to jet substructure in proton-proton and heavy ion collisions.
Psychiatric morbidity in children and adolescents with dermatological disorders

Abstract Background: Understanding the existence of a cycle, where psychological disturbances cause skin diseases and in turn, skin diseases cause psychological disorders, provides the basis for good dermatological practice. Objective: The aim of this case-control study is to examine the psychiatric morbidity of dermatological disorders in children and adolescents with no history of psychiatric disorders. Method: In this study, 502 participants (251 patients and 251 healthy individuals) were evaluated according to DSM-IV criteria. All participants were interviewed and evaluated using the Turkish version of the Kiddie Schedule for Affective Disorders and Schizophrenia – Present and Lifetime Version (K-SADS-PL) and the State-Trait Anxiety Inventory for Children (STAI-1 and STAI-2), the Childhood Depression Inventory (CDI), adolescent and parent forms of the Strengths and Difficulties Questionnaire (SDQ-A and SDQ-P) and a questionnaire evaluating child’s level of school success. Results: Our results indicated that the rates of general psychiatric comorbidity, mood disorders, anxiety disorders, and adjustment disorders were significantly higher in the study group compared to the control group. The CDI, STAI-1, STAI-2, and SDQ (and subscales) scores were significantly higher in the study group. Moreover, psychiatric comorbidity was higher in inflammatory and allergic dermatoses compared to other dermatological subgroups. Having a dermatological disease restricts physical activity thus increasing the risk of psychiatric comorbidity. Conclusions: Investigating the education, attitudes, and awareness of dermatologists about psychocutaneous disorders might contribute to the development of new educational strategies and elicit appropriate biopsychosocial approaches.
SDN based Intrusion Detection and Prevention Systems using Manufacturer Usage Description: A Survey

Internet of things (IoT) is an emerging paradigm that integrates several technologies. IoT network constitutes of many interconnected devices that include various sensors, actuators, services and other communicable objects. The increasing demand for IoT and its services have created several security vulnerabilities. Conventional security approaches like intrusion detection systems are not up to the expectation to fulfil the security challenges of IoT networks, due to the conventional technologies used in them. This article presents a survey of intrusion detection and prevention system (IDPS), using state of art technologies, in the context of IoT security. IDPS constitutes of two parts: intrusion detection system and intrusion prevention system. An intrusion detection system (IDS) is used to detect and analyze both inbound and outbound network traffic for malicious activities. An intrusion prevention system (IPS) can be aligned with IDS by proactively inspecting a system’s incoming traffic to mitigate harmful requests. The alignment of IDS and IPS is known as intrusion detection and prevention systems (IDPS). The amalgamation of new technologies, like software-defined network (SDN), machine learning (ML), and manufacturer usage description (MUD), in IDPS is putting the security on the next level. In this study IDPS and its performance benefits are analyzed in the context of IoT security. This survey describes all these prominent technologies in detail and their integrated applications to complement IDPS in the IoT network. Future research directions and challenges of IoT security have been elaborated in the end. Keywords—Intrusion Detection and Prevention Systems (IDPS); Internet of Things (IoT); Software Defined Network (SDN); Machine Learning (ML); Deep learning (DL); Manufacturer Usage Description (MUD)
Surface Polymerization of Aniline on Silica Gel

The surface polymerization of aniline producing polyaniline nanofilms on surfaces in contact with the reaction mixture has been investigated. The hypothesis that aniline polymerization is heterogeneously catalyzed by the surfaces has been tested. This concept is applicable to planar surfaces and also to surfaces provided by microparticles. The introduction of silica gel, a substrate with a large specific surface area, led to the acceleration of polyaniline formation. The effect of silica gel concentration, particle size, and porosity is discussed, and the courses of polymerization in the presence of porous and compact spheres are compared. It can be concluded that the initiation of aniline polymerization is promoted by adsorption of oligomeric intermediates at the surface. The surface polymerization, giving rise to polyaniline coatings, is thus preferred to precipitation polymerization in the aqueous phase that produces a polyaniline precipitate. The coating of the silica gel surface with polyaniline was .
Dough and Baking Properties of High-Amylose and Waxy Wheat Flours

ABSTRACT The dough properties and baking qualities of a novel high-amylose wheat flour (HAWF) and a waxy wheat flour (WWF) (both Triticum aestivum L.) were investigated by comparing them with common wheat flours. HAWF and WWF had more dietary fiber than Chinese Spring flour (CSF), a nonwaxy wheat flour. Also, HAWF contained larger amounts of lipids and proteins than WWF and CSF. There were significant differences in the amylose and amylopectin contents among all samples tested. Farinograph data showed water absorptions of HAWF and WWF were significantly higher than that of CSF, and both flours showed poorer flour qualities than CSF. The dough of WWF was weaker and less stable than that of CSF, whereas HAWF produced a harder and more viscous dough than CSF. Differential scanning calorimetry data showed that starch in HAWF dough gelatinized at a lower temperature in the baking process than the starches in doughs of WWF and CSF. The starch in a WWF suspension had a larger enthalpy of gelatinization than thos.
Role of combining anticoagulant and antiplatelet agents in COVID-19 treatment: a rapid review

Although primarily affecting the respiratory system, COVID-19 causes multiple organ damage. One of its grave consequences is a prothrombotic state that manifests as thrombotic, microthrombotic and thromboembolic events. Therefore, understanding the effect of antiplatelet and anticoagulation therapy in the context of COVID-19 treatment is important. The aim of this rapid review was to highlight the role of thrombosis in COVID-19 and to provide new insights on the use of antithrombotic therapy in its management. A rapid systematic review was performed using preferred reporting items for systematic reviews. Papers published in English on antithrombotic agent use and COVID-19 complications were eligible. Results showed that the use of anticoagulants increased survival and reduced thromboembolic events in patients. However, despite the use of anticoagulants, patients still suffered thrombotic events likely due to heparin resistance. Data on antiplatelet use in combination with anticoagulants in the setting of COVID-19 are quite scarce. Current side effects of anticoagulation therapy emphasise the need to update treatment guidelines. In this rapid review, we address a possible modulatory role of antiplatelet and anticoagulant combination against COVID-19 pathogenesis. This combination may be an effective form of adjuvant therapy against COVID-19 infection. However, further studies are needed to elucidate potential risks and benefits associated with this combination.
Bubble dynamics in a strong first-order quark-hadron transition

We investigate the dynamics of a strong first-order quark-hadron transition driven by cubic interactions via homogeneous bubble nucleation in the Friedberg-Lee model. The one-loop effective thermodynamic potential of the model and the critical bubble profiles have been calculated at different temperatures and chemical potentials. By taking the temperature and the chemical potential as variables, the evolutions of the surface tension, the typical radius of the critical bubble, and the shift in the coarse-grained free energy in the presence of a nucleation bubble are obtained, and the limit on the reliability of the thin-wall approximation is also addressed accordingly. Our results are compared to those obtained for a weak first-order quark-hadron phase transition; in particular, the spinodal decomposition is relevant.
Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors

Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and depth priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and depth information provides a strong prior for snowy image restoration. We incorporate the semantic and depth maps as input and learn the semantic-aware and geometry-aware representation to remove snow. In particular, we first create a coarse network to remove snow from the input images. Then, the coarsely desnowed images are fed into another network to obtain the semantic and depth labels. Finally, we design a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images. Experiments evaluated on public synthetic and real-world snowy images verify the superiority of the proposed method, offering better results both quantitatively and qualitatively. https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network.
Insight-centric Visualization Recommendation

Visualization recommendation systems simplify exploratory data analysis (EDA) and make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all visualizations into a single list or set of groups based on particular attributes or encodings. This global ranking makes it difficult and time-consuming for users to find the most interesting or relevant insights. To address these limitations, we introduce a novel class of visualization recommendation systems that automatically rank and recommend both groups of related insights as well as the most important insights within each group. Our proposed approach combines results from many different learning-based methods to discover insights automatically. A key advantage is that this approach generalizes to a wide variety of attribute types such as categorical, numerical, and temporal, as well as complex non-trivial combinations of these different attribute types. To evaluate the effectiveness of our approach, we implemented a new insight-centric visualization recommendation system, SpotLight, which generates and ranks annotated visualizations to explain each insight. We conducted a user study with 12 participants and two datasets which showed that users are able to quickly understand and find relevant insights in unfamiliar data.
Intersection Regularization for Extracting Semantic Attributes

We consider the problem of supervised classiﬁ-cation, such that the features that the network extracts match an unseen set of semantic attributes, without any additional supervision. For example, when learning to classify images of birds into species, we would like to observe the emergence of features that zoologists use to classify birds. We propose training a neural network with discrete top-level activations, which is followed by a multi-layered perceptron (MLP) and a parallel decision tree. We present a theoretical analysis as well as a practical method for learning in the intersection of two hypothesis classes. Since real-world features are often sparse, a randomized sparsity regularization is also applied. Our results on multiple benchmarks show an improved ability to extract a set of features that are highly correlated with the set of unseen attributes.
Extracting the Unknown from Long Math Problems

In problem solving, understanding the problem that one seeks to solve is an essential initial step. In this paper, we propose computational methods for facilitating problem understanding through the task of recognizing the unknown in specifications of long Math problems. We focus on the topic of Probability. Our experimental results show that learning models yield strong results on the task, a promising first step towards human interpretable, modular approaches to understanding long Math problems.
A New Report on Mycosynthesis of Silver Nanoparticles by Fusarium culmorum

Plant pathogenic fungus Fusarium culmorum (MTCC-2090) assists in the mycosynthesis of silver nanoparticles. Formation of spherical silver nanoparticles was confirmed from TEM analysis and found in the range of 5-25 nm with an average diameter of 11 nm. Different temperature and pH affects the synthesis of silver nanoparticles indicating that synthesis depends significantly on temperature and pH. Formation of silver nanoparticles at room temperature and pH-7 was found to be optimum for synthesis process. The combined effects of mycosynthesized silver nanoparticles with different antibiotics like kanamycin, erythromycin, oxacillin, tetracycline, vancomy- cin and gentamycin against Klebsiella pneumoniae (MTCC-7407) and Enterobacter aerogenes (MTCC-6804) were carried out. Oxacillin showed the maximum increase in fold area as compared to other antibiotics tested against both the test organisms. Fungal proteins are re- sponsible for the synthesis of silver nanoparticles. This process is easy, eco-friendly and scalable for the large scale synthesis of silver nanoparticles. The synthesis of silver nanoparticles by F. culmorum has not been reported in the past, and thus, it is being reported for the first time.
SP Async: Single Source Shortest Path in Asynchronous Mode on MPI

Finding single source shortest path is a very ubiquitous problem. But with the increasing size of large datasets in important application like social network data-mining, network topology determination-efficient parallelization of these techniques is needed to match the need of really large graphs. We present a new Inter node-bellman cum Intra node Dijkstra technique implemented in MPI to solve SSSP problem. We have used a triangle based edge pruning for idle processes, and two different techniques for termination detection. Within each node the algorithm works as Dijkstra and for outer communication it behaves as inter node bellman ford. First termination detection technique is based on the token ring and counter. Second is a heuristic based technique, in which the timeout is calculated from the number of inter-edges and number of partitions. In this project asynchronous mode of message passing is used.