Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Words from Lip-Movement of Speakers Using Deep Learning

The decryption of the text from the speaker lip movement is called as lipreading. In this paper, the system is enabled to open the webcam which detects the face and lip region, the prediction of the words from the lip movement of the speaker and display it with the help of deep learning algorithms like CNN and LSTM. MIRACLE VC1, an image dataset is used. CNN is a type of ANN that is specialized to manage large data of multi-dimensional. Rather than common matrix multiplication CNN makes use of convolution processing. Fundamental components of CNN are: max pooling, fully connected layers, loss layers, activation function, regularization, convolution layer and optimization. This CNN incorporates a filter set which is learnable. This CNN layer is the form of convolutional network which has the capability to discover along as fully connected layer. The important parameter of this layer includes number of filters, strides and spatial length. The next important layer is max pooling that decreases the model’s values of the network. Activation functions like sigmoid, ELU and RELU notably overwhelm CNN’s performance. After max pooling and convolution layers there comes the fully connected layers where the neurons have a complete contact with the preceding layer. Series of features are handled by LSTM. The extracted features from last CNN layer are then utilized by LSTM layer to draw out the temporal features from the series. The last hidden vector of the final LSTM layer has been utilized by the SoftMax layer in order to give the labels. VGG-16 is a CNN architecture. Although VGG-M model has a fine classification presentation, it is found that VGG 16 is quick to test and train the deeper models. By making use of VGG 16 network that contains three connected and 16 convolutional layers, the characteristics were obtained from the original mouth region.
Evidence for plant-mediated regulation of nitrogenase expression in theAnthoceros-Nostoc symbiotic association

Summary: A pleiotropic dinitrogen and nitrate assimilation mutant was obtained by mutagenesis of Nostoc sp. strain ATCC 29133 with N-methyl-N-nitro-N-nitrosoguanidine followed by penicillin counterselection in the presence of NO- 3 and N2. Mutant strain UCD 223 was capable of reducing acetylene in the free-living growth state only under anaerobic conditions, or under atmospheric conditions when in symbiotic association with Anthoceros punctatus. Heterocysts of strain UCD 223 were noticeably lacking the cohesive outer polysaccharide layer of wild-type heterocysts. Oxygen microelectrode profiles of symbiotic Anthoceros-Nostoc tissue revealed an anaerobic environment in the symbiotic cavities containing Nostoc. The acetylene-reducing activities of strain UCD 223, and of its spontaneously-arising Fix+ revertant strain UCD 236, were not repressed by the presence of 10 mm-NO- 3 when in the free-living growth state, in contrast to wild-type Nostoc ATCC 29133. However, in situ activities of acetylene reduction by symbiotically associated Nostoc ATCC 29133 and strains UCD 223 and UCD 236 were repressed by the presence of 10 mm-NO- 3. It appears that the symbiotic cavities of Anthoceros punctatus can physiologically replace the function of the heterocyst outer wall and that the repression of nitrogenase activity in symbiotic Nostoc in response to the presence of NO- 3, and probably NH+ 4, is mediated by Anthoceros.
Corporate Financing and Investment Decisions When Equity Issuance Reveals Managers’ Information to Investors

I study how a firm raises capital in a repeated game under dynamic adverse selection. The model considers a firm that must issue debt or equity to finance two sequentially arrived investment projects. An equity issuance is assumed to be more expensive than debt but can reveal managers’ information to potential investors and thus reduces the future’s information asymmetry between the firm and the capital market. In equilibrium, a firm will issue equity as a preferred choice if the incremental benefit from the future project outweighs today’s adverse selection cost. This finding suggests that empirically observed violations of the pecking order theory can nevertheless be consistent with information asymmetry being a main capital structure determinant. In support of model predictions, I find that seasoned equity o↵erings are less and less underpriced when firms conducting more and more SEOs. In addition, this underpricing reduction e↵ect is stronger for firms going public in the cold market.
Combining Conditional Random Fields with Deep Neural Networks for Semantic Segmentation

Semantic segmentation is the task of assigning each individual pixel in an image a predeﬁned class, such as “road”, “sidewalk”, “person”. Semantic segmentation has applications in domains where detailed understanding of an image is required. Traditionally, semantic segmentation has been approached by using structured probabilistic (graphical) models known as Conditional Random Fields (CRFs). Lately, deep neural networks (DNNs) have had great success on semantic segmentation. CRFs and DNNs both have their shortcomings and beneﬁts. There are some areas where CRFs are better than DNNs, and some areas where DNNs are better than CRFs. Combining these two methods allowed for improved performance in semantic segmentation. In this paper, we will be presenting both techniques individually, and then describing how they work together for better results in semantic segmentation.
Monitoring populations of Phthorimaea operculella in potato fields and in storage in Senegal

This study provided information on periods of peak trapping for the potato tubermoth (Phthorimaea operculella) in potato fields and in storage facilities in the Niayes area of Senegal. Delta sticky traps baited with P. operculella synthetic pheromone, IT053 ISCA lure, were used to monitor adult male moth populations in 2011 and 2012. In each trapping year, four peaks were observed with the maximum number of moths recorded in February, 2012. A Weibull regression model was developed to predict the incidence of P. operculella in potato fields using the number of male adults caught in pheromone-baited traps. The Weibull model provided a good fit to percentage cumulative mean trap catches for both 2011 and 2012 (R2 = 0.99; P < 0.0001). Based on the Weibull models for the sampling periods in 2011 and 2012, 50% of cumulative mean number of moths were trapped by day 85 (26 March) in both years, 63.2% (= α) were trapped by day 103 and 104 (13 April and 14 April), and 95% were trapped by day 179 and 190 (28 June and 9 July), respectively. Male moths continued to be trapped after potatoes were harvested, but the numbers decreased compared to captures before harvest. This information will be useful for predicting the most appropriate times to institute intervention measures to mitigate damage to potatoes by the moth. Laboratory investigation revealed that abandoned tubers in the field were the source of sustained incidence of the moth on farms following the harvesting of potatoes. The number of male moths trapped in stores tapered with the number of weeks the bagged tubers stayed in storage.
Object Discernment using Scale Invariant Feature Transform with Python

: Optical object detection and pose estimation are very challenging tasks in automobiles since they have to deal with problems such as different views of an object, various light conditions, surface reflections, and noise caused by image sensors. Presently available algorithms such as SIFT (Scale Invariant Feature Transform) can to some extent solve these problems as they compute so-called point features, which are invariant towards scaling and rotation. However, these algorithms are computationally complex and require powerful software in order to operate in real time. In self-propelled applications and generally in the field of mobile devices, restricted processing power and the stipulated for low battery power consumption play an important role. Hence, taking on those cultivated point feature algorithms to mobile hardware is enthusiastic, but also mandatory computer engineering task. The SIFT (Scale Invariant Feature Transform) algorithm is a catch-up for extracting distinctive invariant features from different images. It has been strongly applied to various computer vision problems based on feature match-ups including object detection, pose estimation and many others. However, in actual applications there is a need for improvement of the algorithm’s toughness with respect to the matching of SIFT features. In this work, the purpose to use original SIFT algorithm to provide more reliable feature matching for the purpose of object recognition. The algorithm will be implemented on ARM processor for portable device applications.
Building a Flexible Knowledge Graph to Capture Real-World Events

Events and situations unfold quickly in our modern world, generating streams of Internet articles, photos, and videos. The ability to automatically sort through this wealth of information would allow us to identify which pieces of information are most important and credible, and how trends unfold over time. In this paper, we present the first piece of a system to sort through large amounts of political data from the web. Our system takes in raw multimodal input (e.g., text, images, and videos), and generates a knowledge graph connecting entities, events, and relations in meaningful ways. This work is part of the DARPA-funded Active Interpretation of Disparate Alternatives (AIDA) project, which aims to automatically build a knowledge base that can be queried to strategically generate hypotheses about different aspects of an event. We are participating in this project as a TA1 team, building the first step of the overall system. Our approach is outlined in Figure 1 and will be discussed in detail in the following sections. The first step of the pipeline is pre-processing, shown in
The Knowledge, Attitude and Acceptance of Journalism Ethics among Journalist

The research will focus on fifty journalists from different newspaper and broadcasting companies in Malaysia. The researcher chooses journalist that comes from different newspaper and broadcasting companies in order to know how each journalist practice the ethics and the outcomes will be a variety. This study will help us understand the actual level of knowledge on journalism ethics that the Malaysian journalist posses, their attitude towards it and also their acceptance of journalism ethics in their working environment. Moreover, this research will also be helpful in seeing whether the Codes of Ethics which was stated by the Communication and Multimedia Content Forum is practised by the journalists in their writing process or not. The result showed that the journalists are mostly neutral regarding journalism ethics. Gender does not have any significant relationship between knowledge, attitude and acceptance. This shows that male and female journalist are considered as having the same opinion when it comes to determining the level of ethics they possessed. It showed that gender does not plays a role in finding out the journalists’ attitude and acceptance towards journalism ethics. It could be by chance that they posses the same level of knowledge, attitude and acceptance of journalism ethics.
Unsupervised Outlier Detection Using Reverse Neighbors Counts

— Due to curse of dimensionality, there are various challenges to detect outliers in high-dimensional data. The distance concentration in high-dimensional data hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we prove that such a view is quite simple; by showing that distance-based methods produce more conflicting outlier scores in high-dimensions. Moreover, we show that high dimensionality can have a different impact, by reexamining the reverse neighbors in the context of unsupervised outlier-detection. We provide awareness of how some points known as anti-hubs appear occasionally in k-NN lists of other points. We then describe the relation between anti-hubs and outliers. By figuring out the classic k-NN method, the angle-based methods for high-dimensional data and the density-based local outlier factor on numerous synthetic and real-world data sets, we present novel insight into the efficiency of reverse neighbor counts in unsupervised outlier detection.
DIFFERENT APPROACHES TO THE SILHOUETTE COEFFICIENT CALCULATION IN CLUSTER EVALUATION

Cluster analysis is a useful statistical tool for data exploration. It can help to identify groups of similar objects (e.g. countries) according to selected variables (e.g. economic indicators). The created groups (clusters) can be characterized based either on the variables used in clustering or on some other variables. The problem with using the methods of cluster analysis is that the analyst can obtain different results (assignments of objects into clusters) by different methods, and moreover, he needs to determine the number of clusters. Many coefficients for solving this problem have been proposed until now. However, the suggestion obtained by a certain coefficient can differ from another suggestion obtained by another coefficient. In addition, values of a certain coefficient can differ depending on the implementation in the software product. In this contribution, different approaches to the silhouette coefficient calculation are discussed. They concern the implementations of calculations in the R language and IBM SPSS Statistics system. It is analyzed how the different results influence decisions of an analyst in terms of both the choice of the suitable assignment of objects into clusters obtained by different algorithms, and the determination of the suitable number of clusters. The studied problems are illustrated using selected methods of cluster analysis applied to the EU countries characterized by the gender indicators.
A two-stage damage identification method for space truss structure

To space truss structure, a two-stage damage identification method was proposed. First, using the modal force vector difference with small and low order modes to identify the damage bar of the space truss structure, then identifing the extent of damage through the analysis method of eigenvalue sensitivity by use of the modal frequency with easy-get and high accuracy characteristics. Numerical analysis show that this method can effectively identify different types, different quantity as well as varying degrees of injury bar. the algorithm of the method is simple, small computational workload , and is apply to actually truss structure damage identification.
Prediction of Heart Disease Symptoms using Machine Learning Algorithm

: Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. Machine learning isan application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Now a days, Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The primary aim is to allow the computers learn automatically. Many doctors suggest healthy diet and regular exercise to keep the heart healthy. Following are the parameters which are considered for the study in designing the system. Age , Sex , Blood Pressure , Heart Rate , Diabetes , Hyper cholesterol, Body Mass Index (obesity). We propose to develop an application which can predict the vulnerability of a heart disease given basic symptoms like age, sex, pulse rate etc. The machine learning algorithm neural networks has proven to be the most accurate and reliable algorithm and hence used inthe proposed system. This System predicts the arising possibilities of Heart Disease. The outcomes of this system provide the chances of occurring heart disease in terms of percentage. The datasets used are classified in terms of medical parameters. This project proposes a prediction model to predict whether a people have a heart disease or not and to provide an awareness or diagnosis on that.
Changes of endogenous jasmonic acid and methyl jasmonate in apples and sweet cherries during fruit development.

Trans-jasmonic acid (JA), cis-JA, and trans-methyl jasmonate (MeJA) were quantified in pulp and seeds of 'Tsugaru' apple (Malus sylvestris (L.) Mill. var. domestica (Borkh.) Mansf. ) and 'Satohnishiki' sweet cherry (Prunus avium L.). Trans-JA and cis-JA showed similar changes during development in both types of fruit. JA concentration was high in the early growth stages of apple pulp development, decreased with days after full bloom (DAFB), and then increased again during maturation. There was an initial decrease in concentration of MeJA in apple pulp, followed by a general increase towards harvest. Concentrations of JA and MeJA in the pulp of sweet cherry were high during early growth stages, then decreased towards harvest. PDJ treatment at 104 DAFB (preclimacteric stage) increased endogenous abscisic acid concentration and anthocyanin concentration at 122 and 131 DAFB (maturation stages) in apple. JA concentration in apple seeds was also high in the early growth stages, then decreased, and finally peaked at harvest. MeJA concentration in apple seeds increased towards harvest. In the seeds of sweet cherry, JA and MeJA concentrations generally increased until harvest. In both types of fruit, concentrations of JA and MeJA in the seeds were higher than those of pulp. On a dry weight basis, changes in concentration in the seeds preceded those in the pulp. These results demonstrate that relatively high amounts of JA and MeJA are associated with young developing fruit. These substances may have a role in regulation of fruit growth at early growth stages, though this has not been demonstrated. Chemical name used: n-propyl dihydrojasmonate (PDJ). The effects of plant hormones on fruit maturation differ for climacteric and nonclimacteric fruit. Abscisic acid (ABA), rather than ethylene, plays a role in the onset of fruit maturation in nonclimacteric fruit (Kondo and Inoue, 1997). The influence of jasmonates on fruit maturation may also differ between climacteric and nonclimacteric fruit although this has not been investigated. The JA analog, PDJ, influences growth of radish (Raphanus sativus L.) and rice in the field, and alters sugar and tartaric acid concentration in grape berries compared to controls (Fujisawa et al., 1996, 1997). ABA is synthesized from carotenoids through lipoxygenase action (Parry and Horgan, 1991a) and it has been reported that lipoxygenase activity increased in MeJA-treated apple fruit (Olias et al., 1992). PDJ application may influence ABA metabolism, and therefore fruit maturation. However, the effects of jasmonate on ABA in the fruit have not been examined. In the present study, changes in endogenous concentrations of JA and its methyl ester, MeJA, in pulp and seeds of apple (climacteric) and sweet cherry (Prunus avium) (nonclimacteric) fruit were examined during fruit development. Additionally, endogenous ABA concentrations in fruit treated with PDJ were compared to that of control (nontreated) fruit.
A COMPLEXITY REDUCTION TECHNIQUE FOR MULTI HOP RELAY NETWORK WITH OUT SIGNAL INFORMATION

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Improved Differential distributed space-time coding (I-DDSTC) has been consider to improve both diversity and data rate in the absence of channel (signal) information [CSI]. This paper proposes a new differential encoding and decoding process for D-DSTC systems with two relays. The proposed method is robust against synchronization errors and does not require any channel information at the destination. Simulation results shows less complexity for various synchronization errors without CSI at receiver .
A System to Estimate the Amount and Calories of Food that Elderly People in the Hospital Consume

Malnutrition in the elderly is an important issue, especially for the elderly in hospitals. In general, nutritionists calculate energy from foods that the elderly need each day. If the elderly eat less food and do not receive enough energy, supplementary food must be provided. Currently, the problems in hospitals are the lack of nutritionists and the calculation of the amount and energy of food takes a long time. In this research, we present a system to estimate the amount and calories of the food that the elderly in the hospital consume by taking pictures in a divided food tray before and after consumption. Our system has three main parts: food detection and classification, food weight estimation, and calories estimation application. In the first part, we use the Faster R-CNN technique and select ResNet-50 as a pre-trained model. Our prediction model was trained on a Suandok Hospital Food Images Dataset (SH-FID) of 16, 067 food images from 39 different classes. The result with 4017 food images showed that mAP = 73.354. In the second part, we use the CNN technique, which uses a pre-trained model as InceptionResNetV2. From the experiment with the 4017 food images found that MAPE = 16.9729, which is considered a good prediction. Finally, we created a web application to display the number of calories consumed by the elderly by converting the calculated food weight into calories using the hospital reference table.
TOPOGRAPHY OF DNA SYNTHESIS IN THE MAMMARY GLAND OF THE C3H MOUSE AND ITS CONTROL BY OVARIAN HORMONES: AN AUTORADIOGRAPHIC STUDY

Tritium‐labelled thymidine was injected 45 min before sacrifice into virgin female C3H/HeJ mice 7–23 weeks of age, as well as into 10‐week‐old mice which had been ovariectomized and treated daily with 1 μg of oestradiol‐17β and/or 1 mg of progesterone. Autoradiographs were made of squash preparations of the mammary glands, stained by Feulgen's method. The following results were obtained: (1) During normal development of the gland, cells synthesizing DNA are abundant in terminal buds and virtually absent in duct epithelium. Hence ductal growth takes place by the addition of cells produced in the terminal end structures. (2) At 5–6 months, when mammary growth has ceased, a considerable number of cells synthesizing DNA can still be found in alveoli, though not in duct epithelium. Hence the alveolar cells constitute a renewal population in the adult virgin. Because they maintain the potentiality to divide, duct cells are a G0 population. (3) Ovariectomy results in arrest of DNA synthesis within 3–5 days. Both oestradiol and progesterone restore DNA synthesis in alveoli but only progesterone is able to induce DNA synthesis in duct epithelium, and the differentiation of terminal buds into alveoli. This finding provides an explanation for the resumed proliferation of duct cells in pregnancy. (4) The number of cells engaged in DNA synthesis varies considerably among identical structures within the same gland. This may be due either to synchrony of cell replication and/or to fluctuations of proliferative activity in the gland.