Miracle-dz/UniFAR-FTUs huggingface dataset:
RESOURCE ALLOCATION FOR LOW-LATENCY VEHICULAR COMMUNICATIONS WITH DELAYED CSI FEEDBACK

- Vehicular communications have stringent latency requirements on safety-critical information transmission. However, lack of instantaneous channel state information due to high mobility poses a great challenge to meet these requirements and the situation gets more complicated when packet retransmission is considered. Based on only the obtainable large-scale fading channel information, this paper performs spectrum and power allocation to maximize the ergodic capacity of vehicular-to infrastructure (V2I) links while guaranteeing the latency requirements of vehicular-to-vehicular (V2V) links. First, for each possible spectrum reusing pair of a V2I link and a V2V link, we obtain the closed-form expression of the packets’ average sojourn time (the queueing time plus the service time) for the V2V link. Then, an optimal power allocation is derived for each possible spectrum reusing pair. Afterwards, we optimize the spectrum reusing pattern by addressing a polynomial time solvable bipartite matching problem. Numerical results show that the proposed queueing analysis is accurate in terms of the average packet sojourn time. Moreover, the developed resource allocation always guarantees the V2V links’ requirements on latency.
A Successful Live Birth with In Vitro Fertilization from a Woman with a Huge Uterus Due to Recurrent Multiple Uterine Fibroids after Abdominal Myomectomies

We report a successful live birth with in vitro fertilization (IVF) from a woman with an extremely huge uterus. A 30-year-old woman with an uterus of approximately 13×10×12 cm3 due to recurrent multiple fibroids after two myomectomies presented to our hospital with a desire to become pregnant. She has undergone one cycle of fresh embryo transfer and two cycles of frozen-thawed embryo transfer following Gonadotropin-releasing hormone (GnRH) agonists treatment. The IVF procedures for the woman resulted in a pregnancy that leaded to a live birth of a healthy male baby weighing 3400 g delivered at 39 weeks. Thus, we concluded that for women with huge uterus and multiple fibroids, or otherwise unexplained infertility, assisted reproduction might lead to pregnancy and live birth, even if previous myomectomy and GnRH agonists treatment didn’t exhibit beneficial effects on fibroids.
LONG-TERM SURVIVABILITY OF STRESS TOLERANT AZOSPIRILLUM STRAINS IN DIFFERENT INOCULANT CARRIERS AND ITS FIELD LEVEL EVALUATION AT GRADED N LEVELS

Article History: Received 17, May, 2014 Received in revised form 24, May, 2014 Accepted 19, June, 2014 Published online 28, June, 2014 Stress tolerantAzospirillum strains (ATT-3 and AST-1) on comparison with reference strain (MTCC-125) were found to superior in survival in different inoculant carriers. However the degree of survivability varied among the different carriers. Sterilized lignite sustained the highest number of viable Azospirillum cells, followed by unsterilized lignite, sterilized pressmud and unsterilized pressmud. A field trail was also conducted to study the efficiency of these strains at graded nitrogen levels. Azospirillum cells were also found to be superior in augmenting the growth and yield of sunflower.
SCENE DESCRIPTION FROM IMAGES TO SENTENCES

— People exchange their views using language, whether spoken, written, or typed. A notable amount of this language describes the environment around us, especially the visual scenario in our surroundings or depicted in images or video. Scene description aims to generate the sentences from given set of input images. It links the visual perception with the language space. All present approaches are purely found in supervised machine learning setup. However, owing to the dearth of training data, this seldom achieves desired accuracy. We present a model that uses “Distributed Intelligence” as the prevalent theme in the artificial intelligence literature. Rather than only relying on the training dataset (PASCAL VOC 2012 containing 11530 images, FLICKR8K, FLICKR30K & MSCOCO), we harness the power of internet in order to generate more precise sentences related to the images.
Super Resolution Image Reconstruction Using fast Inpainting Algorithm

--This paper introduces a novel framework for examplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A hierarchical super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with a loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method. Results are compared with existing method by the running time calculation of proposed work inpainting methods.
CURRENT MODE CONTROL METHOD USED FOR LLC RESONANT DC-DC CONVERTER SUITABLE FOR PORTABLE APPLICATIONS

In this paper, current mode control with Pulse-Frequency Modulation (PFM) is addressed to improve the dynamic performance of LLC resonant DC-DC converters irrespective of its operating conditions. In order to improve the dynamic performance and the noise immunity, the addressed control method employs an additional feedback from the current of the resonant tank with an integrator-type compensating amplifier. In this paper, the required design guide lines along with simulation studies are presented for current mode control using PFM. Design of the converter uses Fundamental Harmonic Approximation (FHA) with highest gain adjustment. Finally, a prototype is built with 12V and 5A DC. An efficiency of 94% is found for the converter.
Speech Separation from Noises by using Deep Neural Network

Speech separation from noise is a binary classification problem. To solve this problem the speech signal processed with variety of matching and un-matching conditions. To train the various training data set kernel support vector machine was used. The large variety of training data sets processed by using deep neural network is proposed here. The training data set processed and compared with various other data sets to estimate the pitch of the signal based on Ideal binary mask (IBM) construction. Various type of filter is used for process the signal. The signal processed at the various hidden layers. The signal processed at the different types of frequency ranges. A different type of classifier is used for process the signal based on the difference between the HIT –FA (influence of the hit-false alarm) rate is used for analyze the signal. In existing system Back propagation algorithm is used for propagate the signal at ‘n’ input layer. It perform mapping between the input and output. This algorithm provides better output with less error. In proposed method Semi-supervised learning algorithm is used to improve speech intelligibility in background noise by providing more gain for soft low frequency sounds than for loud low frequency sounds. Simple implementation and Computation time will be reduced. It provides support for the unknown noise also and it separates the noise about 80% or even more. The speech separation system mainly used in automatic speech recognition and high hearing aids. In kernel support vector machine, it support for only known noise. But in our proposed system, it support unknown noise also. Keyword: binary classification problem, IBM, HIT-FA rate, semi-supervised learning algorithm
Remote detection of music influence with physical sensor

Goals and objectives of the study: The goal of this paper was to study the possibility of remote detection of electronic music influence with a physical sensor. Study design: Bio-Well technology with a special environmental sensor was used in the study. This sensor was developed for the analysis of the electromagnetic fields and was shown to respond to the changed in the environmental parameters. Signal of a sensor was detected for an hour in the background mode after this music was turned on for 20 minutes, and then after-effect was detected for about one hour. In experiment 5 different files of the modern electronic music “Yippi Wave 3.0” (https://yippiweb.com/what-is-wave) in *wav format and several files of Handel music have been used. Results: The statistically significant changes of sensor parameters to “Yippi Wave 3.0” music were detected in several time-series, while no response to Handel music. Conclusions: This study should be considered a pilot study and the results are preliminary. We need to conduct experiments with different sources of both acoustic and electromagnetic signals and the possibility of their registration by the Bio-Well device
Traditional Dimensionality Reduction Techniques Using Deep Learning

From the analysis of big data, dimensionality reduction techniques play a significant role in various fields where the data is huge with multiple columns or classes. Data with high dimensions contains thousands of features where many of these features contain useful information. Along with this there contains a lot of redundant or irrelevant features which reduce the quality, performance of data and decrease the efficiency in computation. Procedures which are done mathematically for reducing dimensions are known as dimensionality reduction techniques. The main aim of the Dimensionality Reduction algorithms such as Principal Component Analysis (PCA), Random Projection (RP) and Non Negative Matrix Factorization (NMF) is used to decrease the inappropriate information from the data and moreover the features and attributes taken from these algorithms were not able to characterize data as different divisions. This paper gives a review about the traditional methods used in Machine algorithm for reducing the dimension and proposes a view, how deep learning can be used for dimensionality reduction.
Simplified Matrix Converter Fed Induction Motor Drive

Matrix converter (MC) consists of an array of bidirectional switches, which are used to directly connect the power supply to the load without using any dc-link or large energy storage elements. These highly attractive characteristics are the reason for the tremendous interest in this topology. One of the biggest difficulties in the operation of this converter was the commutation of the bidirectional switches. This problem has been solved by introducing intelligent and soft commutation techniques, giving new momentum to research in this area. The aim of this paper is to propose a scalar matrix converter modulation equivalent to the SVM ones in order to obtain the same electrical characteristics with a simple approach. Then, the proposed symmetrical carrier-based PWM, equivalent to the SVM, is applied to control the induction motor.
SENTIMENT ANALYSIS BASED ON WORD-EMOTICON CLUSTERS

Sentiment analysis is a process of identifying and extracting subjective information in source materials by performing text analysis and Natural Language Processing. It aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. Conventionally, a machine learning algorithm is applied to classify the polarity of a given text into positive, negative or neutral and this classification is done based on emotional states such as „angry‟, „sad‟, „happy‟ etc. A better classification can be achieved by considering emoticons along with emotional states. In many past studies, the emoticons played an important role in building sentiment lexicons and in training machine learning classifiers and also they are considered to be the reliable indicators of sentiment. But, real meaning of all emoticons is not known to many of the social media users. Clustering of words and emoticons in the context of social media will give a good insight about the meaning conveyed by the emoticons. Emoticons are labeled as positive, negative or neutral based on the respective cluster of words they come under. Emoticons are identified from the text and sentiment analysis is performed by using emotional states in the text and emoticons. Such an analysis will result in better classification. This paper focuses on clustering of words and emoticons to know the meaning conveyed by the emoticons and compare the results of sentiment analysis before and after the emoticons are removed from the text.
BLIND DUAL WATERMARKING WITH COLOR IMAGE

The clients all around the globe are utilizing on the web informal community. The dominant parts of interpersonal organizations have feeble client to client verification technique, which depends on some essential data in this system, data hiding techniques to hide some data in profile pictures keeping in mind the end goal to identify botnets and fake profiles lastly will propose a robotized model to distinguish fake profiles and botnets rather than current manual strategy which is exorbitant and work concentrated. This system displays a grouping and investigation of location instruments of clone assaults on online interpersonal organization, in view of trait likeness, companion arrange comparability, profile examination for a period interim and record of Internet Protocol successions. In this system, discrete wavelet transform algorithm is system for information stowing away. Along these lines this would keep the clone assaults and giving complete client information security protecting. When clients transfer the profile picture or photographs it would be watermarked and overhauled. For watermarking strategy Java static watermarking frameworks and calculations is been utilized. Any fake users overhauling a similar profile picture can be distinguished and their particular IP would be followed and blocked.
Hybrid Approach for Securing the IoT Devices

: Today the Internet has turned out to be omnipresent, has touched every edge of the globe, and is influencing human life in incredible ways. We are presently entering a period, where different kind’s appliances are associated with the web. We are entering a time of the IoT. Internet of Things enables the appliances to communicate and perform their activities based on network activity. Today, a PC is substantially less helpful without an association of internet; tomorrow, that will be the situation with apparatuses like a fridge. To put it plainly, these apparatuses should convey to one another. Sensors in the perception layer gather the information from the sources. This information will be transmitted through the system layers over the web to the cloud. Today IoT deals with huge amount of data. This information may be exceptionally touchy and their protection and security must not be endangered. Here comes the requirement for security algorithms to protect the information. In this paper, we provide a hybrid approach of security algorithms (AES along with RSA) to secure the data in network layer .
Assessment of the tissue-culture induced variation in durum wheat (Triticum turgidum) under normal and osmotic stress conditions

Durum wheat (Triticum turgidum L. var. durum) is a major crop with a high protein content that mainly used for the production of pasta and spaghetti. The induction of somaclonal variation is a way for widening the genetic variation. DNA-based markers are able to detect the somaclonal variation in plants. In the present study, six ISSR primers were used to investigate the genetic variation among different callus subcultures derived from embryo culture of durum wheat on MS medium supplemented with various concentration of mannitol. The results showed levels of genetic variation among samples and also, revealed that ISSR technique is a simple and suitable method for assessment of tissue culture induced variation in durum wheat.
Does Age Affect the Outcomes of Translocation Renal Cell Carcinoma?

Renal cell carcinomas (RCCs) are a heterogeneous group of tumors which account for around 90% of all adult renal malignancies [1]. The most common subtypes are clear cell (60-75%), followed by papillary (10-15%), chromophobe (5%) and collecting duct carcinoma [2]. Recent advances in the understanding of molecular alterations implicated in the pathogenesis of RCC have led to the development of a new subclassification of these tumors [3]. Translocation renal cell carcinoma (tRCC) is a newly recognized subtype of RCC with chromosomal translocations involving TFE3 (Xp11.2) or, less frequently, TFEB (6p21) [4]. Microphthalmia-associated transcription (MiT) family tRCC entails Xp11 tRCC and t (6;11) RCC. Xp11 tRCC and t (6;11) RCC are also known as TFE3and TFEB-rearranged RCC, respectively. TFE3 and TFEB belong to the MiT family that regulates melanocyte and osteoclast differentiation. TFE3and TFEB-rearranged RCC show distinguished clinicopathological and immunohistochemical features [5]. Xp11 tRCC comprises 20-40% of childhood RCC and about 4% of adult RCC with an average age of 50 years at diagnosis [6,7]. The natural history of Xp11 tRCC is variable from indolent behaviors [8,9] to aggressive disease that demonstrates lymph node and widespread metastasis. Xp11 tRCCs have the potential to metastasize as late as 20-30 years after diagnosis [7,10]. The associations between age, stage, grade of the disease, survival and recurrence of the disease have not been addressed in the literature. The aim of this study is to identify if these associations exist.
Laser-based Condition Assessment Assistant for Bridges

Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. It is essential to accurately assess the current conditions of these aging, deteriorating and damaged structures in order to evaluate their present status and decide on future steps for rehabilitation. Three-dimensional laser scanning technology has been increasingly used for capturing the current condition of structures. However, it is challenging to automatically extract meaningful information from the high-resolution laser scans even though they provide a detailed geometric representation, and the current state-of-the-art requires user interaction. This work focuses on generating a visualization and interactive assessment solution to assist bridge inspectors. This proposed solution utilizes previously developed laserand image-based damage detection strategies that integrates existing condition rating criteria for automatically locating, quantifying and documenting a wide range damage types that are collected under three main categories: small deformations already existing on the structure (cracks); damage types that induce larger deformations, but where the initial topology of the structure has not changed appreciably (e.g., bent members); and large deformations where localized changes in the topology of the structure have occurred (e.g., rupture and spalling). This approach enables the establishment of transformative strategies for automated damage detection that will provide new capabilities for ascertaining the current state of large-scale structures and infrastructure systems.
Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020

How do machine-learning researchers run their empirical validation? In the context of a push for improved reproducibility and benchmarking, this question is important to develop new tools for model comparison. This document summarizes a simple survey about experimental procedures, sent to authors of published papers at two leading conferences, NeurIPS 2019 and ICLR 2020. It gives a simple picture of how hyper-parameters are set, how many baselines and datasets are included, or how seeds are used.
Cloning and characterization of the gene encoding a blastieidin S acetyltnnfm from

A gene (b/s) encoding a blasticidin $-acetyltransferase from $~ptovenfcillum sp. JCM4673 was cloned in the Sweptomyces plasmid plJ702 and expressed in 8. lividans 1326. Four ofthe recombinant plasmids contained a common 1129-bp Bcll-Psti DNA fragment, which by subcloning experiments was shown to carry the b/. v gene. Sequencing of the 1129-bp Bcll.Pstl fragment indicated that it contains two open reading frames of 408 and 366 nucleotides (nt), both starting with ATG and terndnating at TGA triplets. Deletion analysis showed that the correct b/s sequence is that of 408 nt, whose deduced amino acid sequence conforms to a polypeptide of Mr 15073. A 175-bp DNA fragment from the 5' region of the b/s gene had promoter activity in the promoter-probe plasmid plJ486.
Ontology-based Transfer Learning in the Airport and Warehouse Logistics Domains

This work is a position paper for the examination of ontology-based transfer learning in the context of business processes. We continue our preliminary work on transfering process-oriented knowledge from a well-known source domain to a less speciﬁed target domain. We outline our ideas on workﬂows from two speciﬁc contexts: passenger and baggage logistics at the airport on one hand and warehouse management logistics on the other hand. In the ﬁrst step we automatically transform BPMN ﬁles from these two domains in two separate ontologies. In the next step we intend to use ontology mapping as a means for the transfer. We plan to examine the concepts of generalization and abstraction to ease the transfer. We claim that the mentioned domains are feasible candidates for transfer learning, as we ﬁnd several analogies between the airport handling and warehouse management workﬂows. Additionally we discuss possible utilization resp. beneﬁts of the transfer learning within this two particular domains and draft the next steps for the future research.
Design of a Dual Axis Thermal Accelerometer using Single Axis Structure

In this paper, we are reporting a dual axis thermal accelerometer using a single axis structure. The structure of the device is very simple and can be easily realized. The device contains a bridge shaped heater with four temperature sensors to detect x and y axis acceleration. The device is designed in 3D and simulated using FEM simulator. In this accelerometer, when acceleration is applied along x-axis its sensitivity is 1.07 K/g and along y-axis 0.23 K/g when the heater temperature is 610K. However, this difference in sensitivity between two axes can be improved through on-chip amplifiers. Thus, we believe that this work may lead to a simpler high sensitive dual axis thermal