Miracle-dz/UniFAR-FTUs huggingface dataset:
Handwritten Bengali Numeral Recognition using HOG Based Feature Extraction Algorithm

Hand written character recognition is widely used in modern research. Recognize the characters from an image has always been active research area in the computer vision community. Apart from English, there are several regional languages available worldwide. In India there are 22 official languages, and Bengali is one of them. Bengali script has its own writing pattern. Like any other language number system, Bengali numeral system has ten different digits to indicate 0-9. It has its own alphabets and numerals. In this paper, Histogram of oriented gradient (HOG) and color histogram for selection of features algorithm is proposed. HOG are used as the feature set to represent each digit sample at the feature space and Support Vector Machine (SVM) used to produce the output from input image. Proposed algorithm is efficient and gives an accuracy of 98.05 on CMATERDB3.1. 1 dataset. This type of numeric image recognition can be use in the post offices to acknowledge the pin code written in Bengali.
Data scientist under the Da.Re perspective: analysis of training offers, skills and challenges

The digitalization advent is characterized by the increasing availability of huge amounts of data that allows to generate new knowledge to support decision-making, problem solving and process optimization. The successful implementation of this digital transformation strongly depends on the skills and competences that professionals can have in the different dimensions of the multidisciplinary vision associated to the data scientist profile. This paper describes a study, conducted under the scope of the Da.Re Erasmus + project, that surveys the existing trainings paths dedicated to the academic education of data scientists and the required hard and soft skills of the job offers for these professionals. The survey covers three countries of the project consortium, namely Portugal, Italy and UK.
Biological effects and inter-individual variability in peripheral blood lymphocytes of healthy donors exposed to 60 MeV proton radiotherapeutic beam

Abstract Purpose: The aim of our study was to investigate the amount of initial DNA damage and cellular repair capacity of human peripheral blood lymphocytes exposed to the therapeutic proton beam and compare it to X-rays. Materials and methods: Lymphocytes from 10 healthy donors were irradiated in the Spread Out Bragg Peak of the 60 MeV proton beam or, as a reference, exposed to 250 kV X-rays. DNA damage level was assessed using the alkaline version of the comet assay method. For both sources of radiation, dose–DNA damage response (0–4 Gy) and DNA repair kinetics (0–120 min) were estimated. The observed DNA damage was then used to calculate the relative biological effectiveness (RBE) of the proton beam in comparison to that of X-rays. Results: Dose–response relationships for the DNA damage level showed linear dependence for both proton beam and X-rays (R2 = 0.995 for protons and R2 = 0.993 for X-rays). Within the dose range of 1–4 Gy, protons were significantly more effective in inducing DNA damage than were X-rays (p < .05). The average RBE, calculated from the proton and X-ray doses required for the iso-effective, internally standardized tail DNA parameter (sT-DNA) was 1.28 ± 0.57. Similar half-life time of residual damage and repair efficiency of induced DNA damage for both radiation types were observed. In the X-irradiated group, significant inter-individual differences were observed. Conclusions: Proton therapy was more effective at high radiation doses. However, DNA damage repair mechanism after proton irradiation seems to differ from that following X-rays.
Bt Resistance Implications for Helicoverpa zea (Lepidoptera: Noctuidae) Insecticide Resistance Management in the United States

Abstract Both maize and cotton genetically engineered to express Bt toxins are widely planted and important pest management tools in the United States. Recently, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) has developed resistance to two toxin Bt maize and cotton (Cry1A and Cry2A). Hence, growers are transitioning to three toxin Bt cotton and maize that express both Cry toxins and the Vip3Aa toxin. H. zea susceptibility to Vip3Aa is threatened by 1) a lack of availability of non-Bt refuge crop hosts, including a 1–5% annual decline in the number of non-Bt maize hybrids being marketed; 2) the ineffectiveness of three toxin cultivars to function as pyramids in some regions, with resistance to two out of three toxins in the pyramid; and 3) the lack of a high dose Vip3Aa event in cotton and maize. We propose that data should be collected on current Cry-resistant H. zea in the field to inform future Bt resistance models and that the deployment of Bt toxins and non-Bt refuge crops should be adjusted to favor susceptibility of H. zea to Bt toxins such as Vip3Aa. Finally, maize growers should be incentivized to plant non-Bt structured refuge and have access to hybrids with high-yielding genetic potential at a reasonable price.
Widening and Linearization of DC Magnetic Field Magnetoelectric Sensor Characteristic Using a Compensation Scheme

A sensor of dc magnetic fields using the magnetoelectric effect in a planar ferromagnetic-piezoelectric composite structure is described. It is shown that introduction in the sensor circuit a negative feedback, which contains a current amplifier, a voltage divider, and a compensation coil, results in a widening of the sensor magnetic field region and linearization of its characteristic. The width of the sensor field region depends on the compensation coil parameters and feedback coefficient and can be increased by a factor of tens. A prototype of the sensor based on the metglas-piezofiber composite-metglas structure was fabricated. The prototype had a linear output voltage versus magnetic field dependence within the field region from 0.1 Oe to 72 Oe and a sensitivity of 29 mV/Oe.
Singing Voice Synthesis System for Carnatic Music

Singing Voice Synthesis systems take speech, lyric and note information as inputs, and produce songs as the output. For converting speech to song, the duration and pitch of the speech need to be modified to match the desired pitch and duration of the song. In this paper, we propose a baseline speech to singing voice synthesis system for Carnatic music. We synthesize two popular Carnatic songs from the flat pitched recordings of a vowel sound. Pitch of the input sound is modified according to the frequencies of notes present in the original songs. To avoid abrupt pitch changes, transitions between adjacent notes are smoothed using a sinusoid-based function. To add naturalness to the synthesized song, fluctuations present in input speech are retained. Harmonic plus Noise Model is used to synthesize the songs. Subjective evaluation is performed by ten listeners, and the Mean Opinion Scores for the songs are found to be 3.1 and 3.
Accurate Weed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery

Chemical control is necessary in order to control weed infestation and to ensure a rice yield. However, excessive use of herbicides has caused serious agronomic and environmental problems. Site specific weed management (SSWM) recommends an appropriate dose of herbicides according to the weed coverage, which may reduce the use of herbicides while enhancing their chemical effects. In the context of SSWM, the weed cover map and prescription map must be generated in order to carry out the accurate spraying. In this paper, high resolution unmanned aerial vehicle (UAV) imagery were captured over a rice field. Different workflows were evaluated to generate the weed cover map for the whole field. Fully convolutional networks (FCN) was applied for a pixel-level classification. Theoretical analysis and practical evaluation were carried out to seek for an architecture improvement and performance boost. A chessboard segmentation process was used to build the grid framework of the prescription map. The experimental results showed that the overall accuracy and mean intersection over union (mean IU) for weed mapping using FCN-4s were 0.9196 and 0.8473, and the total time (including the data collection and data processing) required to generate the weed cover map for the entire field (50 × 60 m) was less than half an hour. Different weed thresholds (0.00–0.25, with an interval of 0.05) were used for the prescription map generation. High accuracies (above 0.94) were observed for all of the threshold values, and the relevant herbicide saving ranged from 58.3% to 70.8%. All of the experimental results demonstrated that the method used in this work has the potential to produce an accurate weed cover map and prescription map in SSWM applications.
Revisit of large‐gap Si16 clusters encapsulating group‐IV metal atoms (Ti, Zr, Hf)

Doped clusters by Si16 cage encapsulating group‐IV metal atoms (M@Si16, M = Ti, Zr and Hf) are computationally investigated by both density functional theory (DFT) and high‐level CCSD(T) method. Their low‐energy structures are globally searched using a genetic algorithm based on DFT. The ground state structures of neutral and anionic M@Si16 are determined by calculating the vertical and adiabatic detachment energies and comparing them with the experimental data. For neutral Ti@Si16, the Frank‐Kasper (FK) deltahedron with T d symmetry and distorted FK isomer with C3v symmetry are nearly degenerate as the ground state and may coexist in laboratory, while the distorted FK isomer is the most probable structure for Ti@Si16− anion. For neutral and anionic Zr@Si16 and Hf@Si16 clusters, the ground states at finite temperatures up to 300 K are the fullerene‐like D 4d bitruncated square trapezohedron. These theoretical results establish a more complete picture for the most stable structures of M@Si16 clusters, which possess large gaps and may serve as building blocks for electronic and optoelectronic applications.
"Non-Naked" Gold with Glucose Oxidase-Like Activity: A Nanozyme for Tandem Catalysis.

It has been widely reported that "naked" gold nanoparticles (Au NPs) without protectors have glucose oxidase (GOx)-like activity, and the use of protectors can inhibit the GOx-like activity. Here, "non-naked" Au NPs with GOx-like activity are synthesized by using protein as protector. Although "naked" Au NPs have peroxidase-like activity and GOx-like activity, the optimal pH ranges of the both activities are obviously different. Fortunately, as-synthesized "non-naked" Au NPs show the dual enzyme-like activities at the same pH. So, "non-naked" Au NPs can be described as "tandem nanozyme. " As another bonus, the participation of protein protector can stabilize the GOx-like activity and make Au NPs modifiable. Even though Au NPs are connected with graphene oxide (GO), the GOx-like activity is still not changed. Further, Au NPs-GO nanocomposites are applied on the one-pot nonenzymatic glucose colorimetric detection. The "non-naked" gold not only broadens the species of tandem nanozymes, but also facilitates the functionalization of nanozymes, which is promising for immunoassay, biosensor, and medical treatment.
A Novel Approach for Insight Finding Mechanism on ClickStream Data Using Hadoop

In today's world, there is huge importance for analyzing large data sets in a short span of time. Hadoop is one of such framework that is used to store and process huge unstructured or semi structured data in a distributed manner. The main theme of this paper is to analyze clickstream data that has been gathered from online retail e-commerce website using Hadoop framework. In this process, we are going to use many tools like Pig, Hive, Sqoop which works based on map-reduce algorithm in order to process big data in efficient way. The Insight finding mechanism used to tell us day wise sales report, hourly sales report and top sold item reports based on the clickstream dataset. In the end, the output visualization plots will give the detailed insights based on the clickstream data that we have processed.
A Multifunctional Molecular Probe for Detecting Hg2+ and Ag+ Based on Ion-Mediated Base Mismatch

In this paper, a multifunctional biosensing platform for sensitively detecting Hg2+ and Ag+, based on ion-mediated base mismatch, fluorescent labeling, and strand displacement, is introduced. The sensor can also be used as an OR logic gate, the multifunctional design of sensors is realized. Firstly, orthogonal experiments with three factors and three levels were carried out on the designed sensor, and preliminary optimization of conditions was performed for subsequent experiments. Next, the designed sensor was tested the specificity and target selectivity under the optimized conditions, and the application to actual environmental samples further verified the feasibility. Generally, this is a convenient, fast, stable, and low-cost method that provides a variety of ideas and an experimental basis for subsequent research.
An Examination of Inefficiencies in Key Dependent Variations of the Rijndael S-Box

Substitution boxes, or s-boxes, provide the primary source of non-linearity in symmetric cryptographic primitives. There are a variety methodologies used to design s-boxes. One method, that is widely used, is an s-box that is created at execution time, based on the cipher key. These are usually termed key-dependent s-boxes. Such s-box designs have been demonstrated to be sufficiently secure. However, what has not been adequately studied, is whether the additional overhead required by key-dependent s-boxes actually improves the nonlinearity of the algorithm, or is offset by a decrease in efficiency. Efficiency in algorithms is a growing concern due to devices with limited processing power. Internet of Things (Io‘T), implanted medical devices, and sensors all require security which includes encryption. However, these devices have limited processing power. Additional processing overhead may be counterproductive. This current study examines two questions. The first being how much additional overhead is introduced by key-dependent s-boxes. The focus for this study is on key dependent variations of the Rijndael cipher. The current study shows that while key-dependent variations of Rijndael are sufficiently secure, they do not demonstrate improved non-linearity over the standard Rijndael s-box, and do introduce additional processing overhead. This indicates these key-dependent variations of Rijndael may not have any advantage, and are contra indicated for implementations with limited processing resources.
Comparing CNN and RNN for Prediction of Judgement in Video Interview Based on Facial Gestures

This paper presents a novel technique of judging the performance of a candidate in a video interview. The candidate is judged as confident and attentive or unconfident and inattentive by taking the direction of face and eye into consideration. This corresponds to how many times is the candidate interacting actively, by making a firm eye contact with the interviewer. Image Processing techniques like Haar Cascade, Image filtering, Gamma Correction have been used for the detection of face and eye. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been used for training and testing the images into right classes.
Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster

Log file based data analysis methods in the closed fault tolerant OS have shown several problems. First, it is not easy to add or change the data analysis direction while the service is running after the analysis process has been set and compiled. Second, in an independent closed system, due to the limited resource policy, it is difficult to perform real-time data analysis. Finally, it is not easy to utilize new technologies and open sources such as in-memory database and python. Due to these problems, existing methods have difficulty in detecting abnormal payment transactions in real time. In this paper, we propose an abnormal payment transaction detection scheme based on scalable network architecture and Redis cluster, which can collect transaction data quickly and perform their analysis in real-time. We show its performance by implementing a prototype system and performing several experiments on it. Furthermore, we show that our proposed scheme can be used for data analysis through the reproduction of data using in-memory storage, which can solve the aforementioned problem of unidirectional analysis by doing parallel processing on the distributed Redis repository.
Behavioural equivalences for timed systems

Timed transition systems are behavioural models that include an explicit treatment of time flow and are used to formalise the semantics of several foundational process calculi and automata. Despite their relevance, a general mathematical characterisation of timed transition systems and their behavioural theory is still missing. We introduce the first uniform framework for timed behavioural models that encompasses known behavioural equivalences such as timed bisimulations, timed language equivalences as well as their weak and time-abstract counterparts. All these notions of equivalences are naturally organised by their discriminating power in a spectrum. We prove that this result does not depend on the type of the systems under scrutiny: it holds for any generalisation of timed transition system. We instantiate our framework to timed transition systems and their quantitative extensions such as timed probabilistic systems.
Deep End-to-End Representation Learning for Food Type Recognition from Speech

The use of Convolutional Neural Networks (CNN) pre-trained for a particular task, as a feature extractor for an alternate task, is a standard practice in many image classification paradigms. However, to date there have been comparatively few works exploring this technique for speech classification tasks. Herein, we utilise a pre-trained end-to-end Automatic Speech Recognition CNN as a feature extractor for the task of food-type recognition from speech. Furthermore, we also explore the benefits of Compact Bilinear Pooling for combining multiple feature representations extracted from the CNN. Key results presented indicate the suitability of this approach. When combined with a Recurrent Neural Network classifier, our strongest system achieves, for a seven-class food-type classification task an unweighted average recall of 73.3% on the test set of the iHEARu-EAT database.
Single-cell copy number variant detection reveals the dynamics and diversity of adaptation

Copy number variants (CNVs) are a pervasive, but understudied source of genetic variation and evolutionary potential. Long-term evolution experiments in chemostats provide an ideal system for studying the molecular processes underlying CNV formation and the temporal dynamics of de novo CNVs. Here, we developed a fluorescent reporter to monitor gene amplifications and deletions at a specific locus with single-cell resolution. Using a CNV reporter in nitrogen-limited chemostats, we find that GAP1 CNVs are repeatedly generated and selected during the early stages of adaptive evolution resulting in predictable dynamics of CNV selection. However, subsequent diversification of populations defines a second phase of evolutionary dynamics that cannot be predicted. Using whole genome sequencing, we identified a variety of GAP1 CNVs that vary in size and copy number. Despite GAP1’s proximity to tandem repeats that facilitate intrachromosomal recombination, we find that non-allelic homologous recombination (NAHR) between flanking tandem repeats occurs infrequently. Rather, breakpoint characterization revealed that for at least 50% of GAP1 CNVs, origin-dependent inverted-repeat amplification (ODIRA), a DNA replication mediated process, is the likely mechanism. We also find evidence that ODIRA generates DUR3 CNVs, indicating that it may be a common mechanism of gene amplification. We combined the CNV reporter with barcode lineage tracking and found that 103-104 independent CNV-containing lineages initially compete within populations, which results in extreme clonal interference. Our study introduces a novel means of studying CNVs in heterogeneous cell populations and provides insight into the underlying dynamics of CNVs in evolution.
Novel Architecture for Fault Tolerant Parallel FFTs based on Error correction codes using Vedic Algorithm

The convolution in signal process and communication circuits rises per annum. This can be attained using CMOS technology scaled down to one device. Soft errors creates dependability hazard in recent circuit systems. Signal process and communication system circuits are not exclusion in the current advancement. In few utilizations, a possible event is that use of algorithmic based fault tolerance (ABFT) approaches that are strive and deed of recursive attributes in recognize as well as rectify faults. Communications and Signal processing utilities are more compatible to algorithmic based fault tolerance. A key building blocks in some devices are FFTs. Many secured techniques suggested for recognize as well as rectify faults in FFTs. Amidst of techniques, most likely utilization of Sum of squares or Parseval’s check is the most generally glorious. At a recent time, one method employs that truth put into effect of fault tolerance projected over similar filters. During irregularity, the system first implemented for lookout Fast Fourier Transforms. After that, more two advanced security approaches are mixing a utilization of projected and analized of Parseval’s checks and error correction codes.
Endoparasites of Arctic Wolves in Greenland

Fecal flotation was used to evaluate the presence of intestinal parasites in 423 wolf feces from Nansen Land, North Greenland (82 55 N, 41 30 W) and Hold with Hope, East Greenland (73 40 N, 21 00 W), collected from 1991 to 1993. The species diversity of the endoparasitic fauna of wolves at this high latitude was depauperate relative to that at lower latitudes. Eggs and larvae of intestinal parasites were recorded in 60 feces (14%): Nematoda (roundworms) in 11%; Cestoda (flatworms of the family Taeniidae) in 3%. Four genera were recorded: Toxascaris, Uncinaria, Capillaria , and Nematodirus . Eggs of taeniids were not identifiable to genus, but likely represented Echinococcus granulosus and Taenia hydatigena . The high prevalence of nematode larvae may be a consequence of free-living species' invading the feces. The occurrence of taeniids likely reflects the reliance of wolves on muskoxen for primary prey. This is the first quantitative study of the endoparasites of wolves in the High Arctic.
Semantic Classification of Road Markings from Geometric Primitives

The classification of semantically meaningful road markings in images is an important and safety critical task for autonomous and semi-autonomous vehicles. However, beyond simple lane markings, real-time detection and interpretation of road markings is challenging as images are subject to occlusions, partial observations, lighting changes and differing weather conditions. Additionally, there is high variation in the road markings between countries and regions, which makes interpretation difficult. In this work we present a three-fold approach to the semantic classification. Firstly, we employ a weakly supervised neural network to detect pixels belonging to road markings under different conditions. Subsequently, these pixels are classified into geometric primitives, from which we retrieve the semantic classes through a fast and parallel model-fitting algorithm that offers real-time performance. Unlike other methods in the literature that perform road marking classification independently, our proposed approach performs a joint classification leveraging the highly structured configurations that characterise urban traffic scenes. Consequently, we retrieve the underlying semantic classes under a variety of weather and lighting conditions as we demonstrate in our results.