Miracle-dz/UniFAR-FTUs huggingface dataset:
Maturation of the Pupil Light Reflex Occurs Until Adulthood in Mice

With respect to photoreceptor function, it is well known that electroretinogram (ERG) amplitudes decrease with age, but to our knowledge, studies describing age-related changes in the pupil light response (PLR) of mice are lacking. This study recorded the PLR and ERG in C57BL/6 and Sv129S6 wild-type mice at three different ages during early adulthood. Dark- and light-adapted PLR and ERG measurements were performed at 1, 2, and 4 months of age. For PLR measurements, we used either a red (622 nm) or blue (463 nm) light stimulus (500 ms) to stimulate one eye. We selected various light intensities ranging across almost 4 log units and subsequently classified them as low, medium, or high intensity. From the recorded PLR, we selected parameters to quantify the early and late phases of the response such as the baseline pupil size, the maximal constriction amplitude, the maximal velocity, the early partial dilation (area under the curve of the positive peak of the first derivative of PLR tracing), and the sustained constriction amplitude. For ERG measurements, both scotopic and photopic responses were recorded following stimulation with green light (520 nm) at preselected intensities. The amplitudes and latencies of the a-wave and the b-wave were also analyzed. In both strains, 1-month-old animals presented with a smaller baseline pupil diameter compared to that in 2- and 4-month-old mice. They also exhibited greater maximal constriction amplitude in response to red stimuli of medium intensity. Further, 1-month-old Sv129S6 mice responded with greater constriction amplitude to all other red and blue stimuli. One-month-old C57BL/6 mice also demonstrated faster early partial dilation and smaller sustained response to low blue stimuli. The ERG of 1-month-old C57BL/6 mice showed a greater scotopic a-wave amplitude compared to that of 2-month-old mice, whereas no significant differences were found in Sv129S6 mice. These results suggest that the functional maturation of the neuronal pathway that mediates the PLR continues after 1 month of age. In studies that measure PLR to determine retinal integrity in adult mice, it is thus important to determine normative values in animals of 2 months of age.
Humanized Mice Reveal New Insights Into the Thymic Selection of Human Autoreactive CD8+ T Cells

Thymic selection constitutes the first checkpoint in T-cell development to purge autoreactive T cells. Most of our understanding of this process comes from animal models because of the challenges of studying thymopoiesis and how T cell receptor (TCR) specificity impacts thymocyte phenotype in humans. We developed a humanized mouse model involving the introduction of autoreactive TCRs and cognate autoantigens that enables the analysis of selection of human T cells in human thymic tissue in vivo. Here, we describe the thymic development of MART1-specific autoreactive CD8+ T cells that normally escape deletion and how their phenotype and survival are affected by introduction of the missing epitope in the hematopoietic lineage. Expression of the epitope in a fraction of hematopoietic cells, including all major types of antigen-presenting cells (APCs), led to profound yet incomplete deletion of these T cells. Upregulation of PD-1 upon antigen encounter occurred through the different stages of thymocyte development. PD-1 and CCR7 expression were mutually exclusive in both transgenic and non-transgenic thymocytes, challenging the view that CCR7 is necessary for negative selection in humans. In the presence of antigen, MART1-reactive T cells down-regulated TCR, CD3, CD8, and CD4 in the thymus and periphery. Moreover, expression of secondary TCRs influences MHC class I-restricted T cells to develop as CD4+, particularly regulatory T cells. This new model constitutes a valuable tool to better understand the development of autoreactive T cells identified in different human autoimmune diseases and the role of different APC subsets in their selection.
BER Performance of Self-Coherent PDM-QPSK Optical Transmission System in Presence of GVD and PMD Using Exact Probability Density Function

A technique is described to estimate the bit error rate (BER) of a polarization division multiplexed (PDM) quadrature phase-shift keying (QPSK) transmission scheme considering the resultant impact of polarization mode dispersion (PMD) as well as group velocity dispersion (GVD) where the link is of single mode fiber (SMF). The analytical approach is undertaken considering polarization diversity self-coherent receiver. The exact probability density function (pdf) of the phase variations due to differential group delay (DGD) and GVD is analysed from its moments by Monte-Carlo method. BER is calculated from received conditional BER by averaging the random phase fluctuation pdf. BER is evaluated for different parameters of the system. PDM-QPSK self-coherent approach doubles the spectral efficiency than QPSK but introduces higher power penalty. It is found that for BER of $10^{-9}$ and data rate of 100 Gbps at CD of 1700 ps/nm and DGD of 0.5T, the PDM-QPSK requires 2.09 dB additional power comparing with QPSK doubling the data rate.
Tibetan Word Segmentation Method Based on BiLSTM_ CRF Model

Tibetan word segmentation is one of the key technologies to realize Tibetan speech synthesis and Tibetan speech recognition. Traditional Tibetan word segmentations mainly relie on the combination of rules and statistics. The model automatic learning features become possible in the era of deep learning. This paper proposes a Tibetan word segmentation method based on bidirectional long-term memory neural network with conditional random field model (BiLSTM_ CRF). The Tibetan sentence is firstly divided into clauses, words and abbreviated words manually. Low-frequency words are removed to form a Tibetan dictionary. The text features are then extracted with the dictionary by embedding words into the corpus using Word2vec to get word vectors. The word vector features are transmited to the BiLSTM model. The learned result from BiLSTM model is finally transmitted as features to the CRF model for four-word labeling to obtain the Tibetan word segmentation results. The experimental results show that the proposed Tibetan word segmentation method can achieve better word segmentation effect. The accuracy of word segmentation can reach 94.33 %, the recall rate is 93.89 %, and the F value is 94.11 %.
Topical-Relevance Detection Using Attention-Based Neural Network

Text topical relevance detection focus on whether the content of a text is consistent with the theme of writing requirement. Previous studies use traditional NLP text features to represent the text. Since different digressive texts have different content or topics, the distribution of features corresponding to these texts is also different. Therefore, the conventional methods cannot capture the accurate feature distribution of the text well just by using the space vector to represent the text. In this paper, we propose using an attention-based neural network model to represent the content of the text so as to improve the performance of detection. We do several experiments on ASAP dataset and the results show that our model achieves better performance compared with the baseline methods.
Building the Indonesian NE Dataset Using Wikipedia and DBpedia with Entities Expansion Method on DBpedia

In Indonesian, the NER (Named Entity Recognition)system still needs a lot of improvement. Though NER is the main component in IE (Information Extraction)which is used by other advanced components. To create a reliable Indonesian NER system using a machine learning approach, large dataset is needed. If the dataset is constructed by tagging it manually, the size of the dataset generated is very small. Therefore, a system was created to build Indonesian NE (Named Entities)dataset which were tagged automatically using Wikipedia data as a source of corpus and DBpedia as NE labeling reference with the Entities Expansion method to expand DBpedia NE labeling reference. Currently, the existing system cannot detect name that contain words beginning with lowercase letter on automatic tagging, the existing system have not tried adding person entity gazetteers, and the DBpedia Entities Expansion method rules can still be modified to produce better NE labeling reference quality. In this study a system was built to overcome these shortcomings. Evaluation showed that the best Indonesian NE dataset was built in this study produced Fl-score of 54.93 %, 3.32 % higher than the result of previous studies 51.61 %. This best dataset was built by adding a detection method on automatic tagging, that using the DBpedia Entities Expansion modification rules in this study, but without adding person entity gazetteers.
The Impact of the Wheat Rht-B1b Semi-Dwarfing Allele on Photosynthesis and Seed Development Under Field Conditions

The Reduced Height (Rht) genes formed the basis for the green revolution in wheat by decreasing plant height and increasing productive tillers. There are two current widely used Rht mutant alleles, Rht-B1b and Rht-D1b. Both reduce plant height by 20% and increase seed yield by 5–10%. They are also associated with decreased seed size and protein content. Here, we tested the degree to which Rht-B1b impacts flag leaf photosynthetic rates and carbon and nitrogen partitioning to the flag leaf and grain during grain fill under field conditions using near isogenic lines (NILs) that were either standard height (Rht-B1a) or semi-dwarf (Rht-B1b). The results demonstrate that at anthesis, Rht-B1b reduces flag leaf photosynthetic rate per unit area by 18% and chlorophyll A content by 23%. Rht-B1b significantly reduced grain protein beginning at 14 days post anthesis (DPA) with the greatest difference seen at 21 DPA (12%). Rht-B1b also significantly decreased individual seed weight beginning at 21 DPA and by 15.2% at 28 DPA. Global expression analysis using RNA extracted from developing leaves and stems demonstrated that genes associated with carbon and nitrogen metabolism are not substantially altered by Rht-B1b. From this study, we conclude that Rht-B1b reduces flag leaf photosynthetic rate at flowering while changes in grain composition begin shortly after anthesis.
Variable Immunogenic Potential of Wheat: Prospective for Selection of Innocuous Varieties for Celiac Disease Patients via in vitro Approach

Celiac Disease (CD) is a multifactorial, autoimmune enteropathy activated by cereal proteins in genetically predisposed individuals carrying HLA DQ2/8 genes. A heterogenous gene combination of the cereal prolamins is documented in different wheat genotypes, which is suggestive of their variable immunogenic potential. In the current study, four wheat varieties (C591, C273, 9D, and K78) identified via in silico analysis were analyzed for immunogenicity by measuring T-cell proliferation rate and levels of inflammatory cytokines (Interferon-γ and Tumor Necrosis Factor-α). Peripheral Blood Mononuclear Cells and biopsy derived T-cell lines isolated from four CD patients in complete remission and two controls were stimulated and cultured in the presence of tissue transglutaminase activated pepsin-trypsin (PT) digest of total gliadin extract from test varieties. The immunogenicity was compared with PBW 621, one of the widely cultivated wheat varieties. Phytohaemagglutinin-p was taken as positive control, along with unstimulated cells as negative control. Rate of cell proliferation (0.318, 0.482; 0.369, 0.337), concentration of IFN- γ (107.4, 99.2; 117.9, 99.7 pg/ml), and TNF- α (453.8, 514.2; 463.8, 514.2 pg/ml) was minimum in cultures supplemented with wheat antigen from C273, when compared with other test varieties and unstimulated cells. Significant difference in toxicity levels among different wheat genotypes to stimulate celiac mucosal T-cells and PBMC's was observed; where C273 manifested least immunogenic response amongst the test varieties analyzed.
Perception Net: A Multimodal Deep Neural Network for Machine Perception

Perception in humans lays a strong foundation for our interaction with the surrounding multimodal environment. This can be credited to the adaptability which unified nature of human perception empowers us with. Machines generally lack this ability to adapt and hence are at a disadvantage when it comes to processing multimodal information. Inspired by the multimodal nature of human perception, this paper proposes deep neural network models to illustrate the idea of multimodal perception in machines. The proposed models are designed around the focal theories of human perception–Multisensory enhancement and Principle of inverse effectiveness. These models exhaustively explore the affect recognition problem and their performance is tested on a benchmark RECOLA dataset. The results obtained are better and computationally less expensive than most of the existing techniques. Hence, they illustrate the importance of multimodal perception approach for machines.
NEAT Algorithm for Testsuite generation in Automated Software Testing

Software testing is one of the most essential and an indispensable part of Software production life cycle. Software testing helps in validating if the product meets with the requirements or not, and also testing helps to validate the performance of the product. Unfortunately, this process takes up about 50% of the production time and budget, due to its laboriosity. Hence, in order to reduce the time it takes, Automated Software Testing becomes essential. Here we propose a novel idea of using Machine Learning for automatically generating the test suites. In this paper we present an approach that uses NEAT (Neuroevolution of Augmenting Topologies) Algorithm to automatically generate new test suites or for improving the coverage of already produced test suite. Our approach automatically generates test suites for white box testing. White box testing refers to testing of the internal structure and the working of the Software Under Test.
Sustainable education development in Engineering Institutions

Sustainable development refers to the ultimate goal of man-environment relationship. The education system has various goals such as democracy, human rights and social justice etc. Conventionally education system was based on attainment of facts and nowadays, this process becomes obsolete. Thus, this results in the decision of the policymakers to transform the education system which has more insights into the most special features of the human world from business to technology to social environment. It is also assured that there must be a separate education for each and every era. A strong curriculum forms the sturdy foundation of strong education systems. Hence, the curriculum needs a revision which must be in a way that portrays the essential competencies a student has to possess and sustain. In addition, a comparison about how the curriculum of the present century differs has been brought in this work. Moreover, this also gives a detailed overview of the sustainable education system that has been implemented in our campus which would meet out the challenges of the present social, societal and environmental challenges. To this end, the work also shows the outcome of the sustainable education system that has been followed in our campus.
A Comparison of the Effect of Single and Multiple Cavities on Base Flows

The paper represents a novel approach to understand the effect of single and multiple cavities on base pressure. We considered a control plate of 1 mm thick between a square nozzle of the cross-sectional area of 100 mm2 and square duct of the cross-sectional area of 625 mm2. Both single and multiple cavities results are compared for a different level of expansion. The nozzle pressure ratio taken are 1.27, 1.33, 1.53 and 1.7. The high-speed compressible subsonic nozzle is being used with internal flow apparatus to achieve flows ranging between Mach 0.6 to Mach 0.9. The comparison between single and multiple cavities are shown graphically with and without control. The multiple cavities were found to be more effective as compared to a single cavity for controlling the base pressure.
Teaching HW/SW codesign with a Zynq ARM/FPGA SoC

In this paper we describe a lab session-based hardware/software (HW/SW) codesign course for implementing embedded systems. The goals of the course are to teach the fundamental concepts of embedded system design, develop hands-on HW/SW codesign skills, and to show that there are many possible ways to explore the design space. The reason behind choosing HW/SW codesign approach is that it brings the best of the two worlds: the flexibility of SW and the power/energy/computation efficiency of HW. As an example project, students codesign the well-known RSA public-key cryptosystem in the Xilinx Zybo boards that contain a Xilinx 7-series FPGA coupled with an embedded ARM processing unit. Students are required to explore the design space, weigh the various alternatives and take design decisions. Besides, the project cultivates non-technical skills such as team building and management, sharing of work-load, decision making, presentation and technical report writing.
Self-Adaptive Sampling in Noisy Multi-objective Optimization

The paper proposes a novel sampling strategy to adapt sample size for periodic fitness evaluation of solutions of a multi-objective optimization (MOO) problem in the presence of noise in the objective surfaces. The existing works consider a fixed functional form of relationship between sample size of a solution and its local neighborhood fitness variance (LNFV). The non-decreasing monotonicity of the functional form being subjective to the noise characteristics, any fixed functional form is ineffective to allocate accurate sample size to a solution for all possible known/unknown noise distribution. This stalemate is overcome here by employing a novel learning induced sample size adaptation policy. The policy learns the success or failure of sample sizes assigned to solutions with specific LNFVs in the early exploration phase of an MOO and later utilizes the acquired knowledge to guide selection of sample size by solutions of future generations. Experiments undertaken reveal a statistically significant superiority of the proposed realizations to their existing counterparts with respect to inverted generational distance and hypervolume ratio metrics.
Making Short Movie using 3D Professional Camera

The use of 3D Professional Camera to create a 3D video is still rarely used due to the high price of the camera. Mostly, people produce 3D movies using two 2D cameras which are set precisely the same by arranging the camera one by one. Using the 3D camera makes the setting is much easier. The 3D Professional Camera provides convenience with the two lenses that have been connected so that the camera settings also become one. However, the technique of making movies using 3D Professional Camera needs to be studied furthermore. This paper presents the experiments to gain the optimum setting for 3D Professional Camera in making a short movie. The configuration will result the 3D effects on the movie.
Utilizing Indonesian Allophones and Intraword Short Pauses Handling to Improve Performance of Indonesian Text-To-Speech

An allophone is a phoneme variant based on the position within a word, for instance, the first phoneme $e$ in “pendekar” is pronounced differently from the second phoneme e. According to Badan Pengembangan dan Pembinaan Bahasa (Language Development and Fostering Agency), formerly Pusat Bahasa (Language Center), Bahasa Indonesia has 5 vowels and 22 consonants, with 6 of them have allophones. There are only allophones of a phoneme (e)that can change the meaning of a word, while allophones of other 5 phonemes are not changing words' meanings. Therefore, most researches/projects on developing an Indonesian text-to-speech (TTS)system focus only on allophones of the phoneme e. This paper proposes a method to utilize all allophones of Bahasa Indonesia in developing a model for an Indonesian TTS system with a deep neural network (DNN)method. Furthermore, intraword short pause is also implemented to improve intelligibility and naturalness aspects. A set of rules are introduced to automatically detect allophones and intraword short pauses in the text corpus used in recording audio data. Using subjective and objective evaluations, the resulted TTS model shows a better result compared to one that not using allophones and intraword short pauses.
Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals

We investigate the differences in brain signals of craving smokers, non-craving smokers, and non-smokers. To this end, we use data from resting-state EEG measurements to train predictive models to distinguish these three groups. We improve the neural network models applied earlier in two ways: firstly by adding channel-wise convolutional layers, secondly by adding residual connections to the network. We further extend the validation to make it similar to a real world scenario, in which a prediction is based on all data available for this measurement. Finally, we analyze the prediction quality for each measurement individually. Our results demonstrate significant improvements.
An Overview of Social Engineering in the Context of Information Security

Social engineering in the context of information security is the exploitation of human psychology to gain access into secure data. Human emotion can act as both a strength and a weakness. When it comes to the world booming with technology, human emotions which are completely unrelated to the matter is made to relate through social engineering. Social engineering employs ‘traps’ to pry on human emotion and its vulnerability, taking advantage of the flaws of human psychology. Information security breaches utilising social engineering techniques are vast, so that social engineering in this context is a topic which could not be neglected. This research paper presents an overview of social engineering attacks and suggested defence mechanisms. An introduction to social engineering attacks are given, with context to the current trends and related vulnerabilities. Main reasons for the spread of social engineering attacks in the current context are also presented. Attack frameworks are presented and defence approaches are proposed at the end.
A Taxonomy for Metamodeling Frameworks for Evolutionary Multiobjective Optimization

One of the main difficulties in applying an optimization algorithm to a practical problem is that evaluation of objectives and constraints often involve computationally expensive procedures. To handle such problems, a metamodel is first formed from a few exact (high-fidelity) solution evaluations and then optimized by an algorithm in a progressive manner. However, in solving multiobjective or many-objective optimization problems involving multiple constraints, a simple extension of the idea to form one metamodel for each objective and constraint function may not constitute the most efficient approach. The cumulative effect of errors from each metamodel may turn out to be detrimental for the accuracy of the overall optimization procedure. In this paper, we propose a taxonomy of different plausible metamodeling frameworks for multiobjective and many-objective optimization and provide a comparative study by discussing advantages and disadvantages of each framework. The results presented in this paper are obtained using the well-known Kriging metamodeling approach. Based on our extensive simulation studies on proposed frameworks, we report intriguing observations about the behavior of each framework, which may provide salient guidelines for further studies in this emerging area within evolutionary multiobjective optimization.