Miracle-dz/UniFAR-FTUs huggingface dataset:
CREATE CAPTION BY EXTRACTING FEATURES FROM IMAGE AND VIDEO USING DEEP LEARNING MODEL

: The images, videos captured using various devices and images available in various sources like internet, news articles, social media does not have proper description through which a human can understand those without observing closely. It is also very difficult to write syntactically correct sentence describing the images for a large set of images and videos manually. Sometimes the description may vary based on each individual person’s perception, mood and interpretation at the time of interpreting the image in the form of sentence. This may lead to forming a caption with inaccurate, imperfect, error prone description for the images or videos which is not acceptable in some scenarios where the accuracy is the primary criteria for the other systems to react or act upon. The aim here is to generate a sentence for an image by identifying the features by using deep learning techniques and also for the video frames to generate sentence using the same model used for the generation of image captioning. The feature extraction from the images is done by using pre-trained deep learning model available like VGG16, Densenet121, InceptionV3 etc. The vocabulary was built by processing the description of the images that is available as a part of the dataset and removing the stop words. The deep learning model is built and trained to generate syntactically correct sentence by looking into the extracted features and predicting the next set of sequence of words and interpreting those sequence of word into sentence. The experiments done on several datasets on the model and measured the accuracy of the model by measuring the fluency of the language it learns solely from image descriptions. The accuracy of model and smoothness or command of language model learns from image training on various sources of data from Flickr8k, Flickr30k, Video frames etc. BLEU score is generated to compare the model performance. To extract the feature from the images, deep learning neural network technique convolution neuron network (CNN) is used and recurrent neural network (RNN), such as a Long Short-Term Memory network (LSTM) is used to generate the sequence of word. The generation of cation for the video, key-frames are extracted from the video by passing through the key frame extraction framework built inside the application. The sequence of key-frames extracted from video is fed into the same image captioning model that was trained for generating the caption for the images. The captions are generated from the frames extracted from the video by using the predefined pre-trained model by feeding the images.
Microelectrode cluster technology for precise interactions with neuronal circuits Towards highly specific adaptive deep brain stimulation

Neuro-electronic interfaces, which can be used for stable communication between neurons-computers over long periods of time, would be valuable for understanding and interacting with the nervous system. A major challenge has been to overcome the tissue reactions towards implanted electrodes. Flexible microelectrodes that cause less implantation injury and which can follow the micromotions of the brain have been considered as a solution to achieve stable neuronal recordings and stimulations. The aim of this thesis work was therefore to develop and evaluate biocompatible neuro-electronic interfaces, as well as introduce new implantation methods which together allow stable recordings and spatially precise stimulation of the brain To this end, we have developed a new generation of ultrathin flexible electrode arrays based on 12.5 μm thin wires embedded in a gelatin vehicle providing structural support during implantation. The gelatin embedded electrodes were implanted in rat brains via a narrow track line and spread out as a cluster in the target area. In the first study, we evaluated the performance of the neural recordings for eight weeks with respect to impedance, signal amplitudes and noise levels. We found impedance, and signal to noise ratio of single units to be quite stable, suggesting high biocompatibility. In the second study, we developed a gelatin embedded microelectrode array consisting of 16 microelectrodes, distally equipped with silicone cushions to reduce vascular damage. This array was implanted medial to the subthalamic nucleus, in 6-hydroxydopamine lesioned rats (a classical animal model for Parkinson’s disease), and the effects of deep brain stimulation were evaluated for 6 weeks. Stimulation with subsets of 4-8 electrodes evoked specific motor behaviors in all the tested rats. Depending on the exact electrode combination, stimulation elicited either improvement of locomotion, or grooming and rearing, increased turning, dyskinesia, or no movement. These results suggest that improved stimulation specificity can be obtained by choosing the right group of electrodes from the cluster. In the third study, we hypothesized that reducing the tissue resistance during the insertion of the electrodes would minimize the implantation injury. To address this problem, we coated gelatin embedded needles with a layer of ice, which on melting, provided a super slippery surface during insertion into the brain. The addition of a layer of melting ice decreased the insertion force by approximately 50%, significantly reduced neuronal loss, as well as the astrocytic response, but did not have any obvious effect on microglial activation. In conclusion, this thesis presents a novel design for implantable and biocompatible neuro-electronic interfaces comprising highly flexible microelectrodes rendering stable recording properties and improved stimulation specificity. In addition, a novel implantation vehicle was developed to reduce the acute tissue reactions in response to the implantation
Virulence Diversity and Physiological Race Composition of Wheat Stem Rust (Puccinia graminis f.sp. tritici) in Tigray Region, Northern Ethiopia

Wheat stem rust (black rust) is one of the most important airborne diseases of wheat (Triticum aestivum) caused by Puccinia graminis f. sp. tritici remains a constraint to the world’s wheat production. Because of the sudden changes in stem rust race patterns, commercial varieties tend to become vulnerable globally at large and particularly in Ethiopia. It was responsible to cause 6.2 million metric tonnes per year or higher losses under severe epidemics at global level. Therefore, this study was initiated to identify the physiological races and virulence diversity of Puccinia graminis f. sp. tritici in Tigray region. Race identification through inoculation of stem rust isolates, multiplication of single-pustule of the pathogen and race designation by inoculating on a set of wheat differential lines were done in the greenhouse. Forty-seven stem rust isolates were analyzed on the twenty stem rust differentials and resulted in the identification of six races namely; TTTTF, TKTTF, TRTTF, TTRTF, RRTTF and TKPTF. In this study, race TTRTF was detected for the first time in Ethiopia during 2017 cropping season. Out of the six races identified, TTTTF was detected from 25 (53.19%) isolates and TKTTF from 15 (31.91%) isolates. The most virulent race that made 18 stem rust resistant genes non-effective was TTTTF, which virulent on 90% of stem rust resistance genes. Differential hosts carrying Sr24 and Sr31 were effective genes which confers resistance to all of the races identified. Hence, the stem rust resistance gene Sr24 and Sr31 can be used as sources of resistance in the wheat breeding program.
Perfectionism Means a Heavy Burden of Self

Perfectionism means a heavy burden of self-assessment in the light of performance results. Even though the literature on its influence on cognitive functioning is minimal, notably in not therapeutic communities, perfectibility has been extensively researched. The goal of this research is for college students with high or low rates of perfectionism to use a comprehensive neuropsychological battery to test their cognitive functions. 98 university students performed a neuropsychological battery that was tested for psychiatric classification and assessed for their performance and associated symptoms were taking part. Tests have shown that the high negatives of excellence is considerably higher than the low negatives of perfectionism. Neuropsychological results did not however show differences in the sample. The variations in gradients on clinical outcomes have been identified by contrasting three classes of high adaptativity, high maladaptive and mixed excellence. Neuropsychological tests showed no variations. Such findings suggest that higher levels of depression are correlated with significant psychopathological stress, but with stable test performance in neuropsychology.
MDP Playground: A Design and Debug Testbed for Reinforcement Learning

We present MDP Playground, an efficient testbed for Reinforcement Learning 1 (RL) agents with orthogonal dimensions that can be controlled independently 2 to challenge agents in different ways and obtain varying degrees of hardness in 3 generated environments. We consider and allow control over a wide variety of 4 dimensions, including delayed rewards, rewardable sequences, density of rewards, 5 stochasticity, image representations, irrelevant features, time unit, action range 6 and more. We define a parameterised collection of fast-to-run toy environments 7 in OpenAI Gym by varying these dimensions and propose to use these for the 8 initial design and development of agents. We also provide wrappers that inject 9 these dimensions into complex environments from Atari and Mujoco to allow for 10 evaluating agent robustness. We further provide various example use-cases and 11 instructions on how to use MDP Playground to design and debug agents. We 12 believe that MDP Playground is a valuable testbed for researchers designing new, 13 adaptive and intelligent RL agents and those wanting to unit test their agents. 14
THE ERBIL PLAIN ARCHAEOLOGICAL SURVEY: PRELIMINARY RESULTS, 2012–2020

The Erbil Plain Archaeological Survey (EPAS) investigates settlement and land use from the Neolithic to the present in the Erbil Governorate of the Kurdistan Region of Iraq, which includes a large portion of the core of the Assyrian Empire. In seven field seasons, it has documented a broad settlement landscape in a region of great social and political importance, especially in the Bronze and Iron Ages, including 728 archaeological sites. Its field methodology combines traditional surface collection with the use of historical aerial and satellite photographs, mobile GIS, and UAV (drone) photogrammetry. Preliminary results show some unexpected patterns: a high density of culturally Uruk settlements in the fourth millennium B.C., variable urban morphologies in the Early Bronze Age; and large but low-density settlements at the end of the Sasanian period or the early Islamic period. The project is explicitly testing several hypotheses about centralized Neo-Assyrian landscape planning in the imperial core. These hypotheses appear to be confirmed, although the situation was more complex than in surrounding provinces, probably due to the longer history of continuous settlement.
Supplementary Material for ‘Pruning Filter in Filter’

This is the supplementary material for the paper ‘Pruning Filter in Filter’. Section 1 shows that we can use SWP to continue pruning the network pruned by other FP (ﬁlter pruning) based methods. Section 2 shows that Filter Skeleton is better than lasso regularization in the SWP framework. Section 3 displays more visualization results
Teachers ’ Perception Towards Women Leadership in Education in Malaysia

The purpose of this study is to survey the perception of teachers towards headmistress’ leadership dan recognize the level of motivation among teachers in that particular school. This study is modelled in qantitative and descriptive manner which involves 20 randomly chosen teachers in a primary school in Batu Pahat district under a headmistress leadership. Questionaire is used as an instrument to identify the work structure in women leadership, leader’s compassion and teachers’ level of motivation. In this study, descriptive analysis namely mode, min and percentage is used to in study outcomes. The study outcome that on the whole, teachers’ perception towards headmistress leadership in primary school in the district of Batu Pahat is positive ans satisfactory. The min score for all respondents for all items in the questionnaire is 4.07. The value shows basically these respondents have positive and constructive perception towards leadership of a headmistress. Nevertheless, there is a significant association between teachers’ motivation level and perception towards leadership of a headmistress. This verifies that the higher the teachers’ perception towards leadership of a headmistress, the more elevated their level of motivation. This study supports the outcome pertaining general assumption that the leadership style of a headmistress indeed influences teachers’ working motivation level. © 2014 Elixir All rights reserved ARTICLE INF O Articl e h istory: Received: 1 December 2013; Received in revised form: 15 December 2013; Accepted: 16 January 2014;
DNA Methylation-Based Estimates of Circulating Leukocyte Composition for Predicting Colorectal Cancer Survival: A Prospective Cohort Study

Simple Summary Inflammation is involved in the evolution of cancer. Leukocytes, of which the proportion can be estimated using epigenome-wide methylation data, may serve as a prognostic marker in colorectal cancer (CRC). Our aim was to investigate whether DNA methylation-based estimates of circulating leukocytes is associated with all-cause and disease-specific mortality in a prospective CRC patients’ cohort. Significant associations with CRC prognosis were observed for CD4+ T cells, CD8+ T cells, B cells, NK cells, and lymphocytes, independent of age, sex, tumor stage, tumor subsite, and therapy. CD4+ T cells outperformed other leukocytes and provided added predictive value in comparison to age, sex, and tumor stage. Although cell counting is commonly used in clinical practice, DNA methylation-estimated cell proportions could be a promising tool in understanding the role of leukocytes as CRC prognostic biomarkers when using stored blood samples. Abstract Leukocytes are involved in the progression of colorectal cancer (CRC). The proportion of six major leukocyte subtypes can be estimated using epigenome-wide DNA methylation (DNAm) data from stored blood samples. Whether the composition of circulating leukocytes can be used as a prognostic factor is unclear. DNAm-based leukocyte proportions were obtained from a prospective cohort of 2206 CRC patients. Multivariate Cox regression models and survival curves were applied to assess associations between leukocyte composition and survival outcomes. A higher proportion of lymphocytes, including CD4+ T cells, CD8+ T cells, B cells, and NK cells, was associated with better survival, while a higher proportion of neutrophils was associated with poorer survival. CD4+ T cells outperformed other leukocytes in estimating the patients’ prognosis. Comparing the highest quantile to the lowest quantile of CD4+ T cells, hazard ratios (95% confidence intervals) of all-cause and CRC-specific mortality were 0.59 (0.48, 0.72) and 0.59 (0.45, 0.77), respectively. Furthermore, the association of CD4+ T cells and prognosis was stronger among patients with early or intermediate CRC or patients with colon cancer. In conclusion, the composition of circulating leukocytes estimated from DNAm, particularly the proportions of CD4+ T cells, could be used as promising independent predictors of CRC survival.
Performance Evaluation of some basic cooperative communication protocol for wireless networks

- In the frequency-limited cellular networks, the underlaying Device-to-Device (D2D) communications is an efficient way to enhance the user access probability and network capacity performance. To satisfy the communication requirements for users in different network sizes, a cooperative transmission strategy based on multi-relay D2D communications is proposed. Specifically, to increase the serving users simultaneously and meanwhile maintain the QoS of them, a joint admission control and cooperative nodes selection scheme is proposed, and then the power control and resource allocation scheme is provided correspondingly. Simulation results show that the proposed strategy can enhance the user access probability efficiently in frequency-limited cellular networks. There are several relaying strategies to implement cooperative diversity, and some of the basic strategies are Amplify-and-Forward, Decode-and-Forward and Compress-and Forward. Each of them have their own advantage and they can provide different performance results with different environment. Variation of probability of error can be seen with the change in signal combining strategy.
On the convergence of an approximate method for solving the problem filtration consolidation with a limiting gradient

A one-dimensional initial-boundary value problem modelling the process of joint motion of a viscoelastic porous medium and a liquid saturating the medium is considered. In the filtration theory, this process is called filtration consolidation. A finite element in spatial variable and time-implicit difference scheme is constructed. Its solvability is established, the convergence piecewise-constant filling of an approximate solution in the variable t to generalized solution of problem is proved.
Evaluation of reproduction performance and calf sex ratio of dairy cattle in selected locations of South-East Oromia

Dairy farmers in many parts of the world complain about the deviation from the normal calf sex ratio following Artificial Insemination (AI). The prospective and retrospective studies were conducted to compare the sex ratio of the calves born following Artificial Insemination (AI) and Natural Mating (NM) under institutional and smallholder-managed dairy farms. This study also compared the reproductive performance of dairy cattle under institutional management and smallholder systems. The data encompassed farm records maintained between 1996-2014 G.C by dairy farms at Arsi University-Asela, crossbreeding center at Gobe ranch, and Agarfa ATVET College. These farms reared both F1 and crossbreds with higher (>75%) exotic inheritances (grade crosses). The study also considered household and progeny history surveys on smallholder dairy farms (n=90) adjoining to the institutional farms which were randomly selected from Tiyo, Kofele, and Sinana districts. The result indicates that most of the respondents at Tiyo (96.8%) and Kofale (100%) were concerned about the differences in the sex ratio of calves born from AI service and natural mating, while most of the respondents at Sinana (29%) did not report such differences. The respondents in Tiyo and Kofale districts reported higher numbers of male calves (58%) and higher numbers of female calves (58.1%) born from cows inseminated artificially respectively. The Average Age at First Calving (AFC) had no significant difference between genotypes (F1 and grade crosses) reared at the institutional farms. There were significant differences (p<0.05) in AFC across the ranches. The AFC was lower in the institutional farms when compared to those managed at smallholder management conditions. The number of services per conception (NSC) varied p<0.05) across the dairy farms. No significant differences were observed among the sex ratio of the calves born through natural and AI mating systems either at the institutional or smallholder farms. However, differences due to season and year were observed, indicating the effect of non-genetic factors influencing the sex ratio of the calves. In some seasons the numbers of male calves were higher while the reverse was true for some other seasons. Therefore, the reproduction traits of the cattle differed (P<0.05) across the institution and smallholder production systems having lower values observed among the cattle raised at the dairy farms and higher for the F1 crossbred than grade crosses, while production trait of the cattle differed (P<0.05) across the three dairy farms and there were no significant differences in calf sex ratio among the calves born from AI and NM.
L EARNING TO S AMPLE WITH L OCAL AND G LOBAL C ONTEXTS FROM E XPERIENCE R EPLAY B UFFERS

Experience replay, which enables the agents to remember and reuse experience from the past, has played a significant role in the success of off-policy reinforcement learning (RL). To utilize the experience replay efficiently, the existing sampling methods allow selecting out more meaningful experiences by imposing priorities on them based on certain metrics (e.g. TD-error). However, they may result in sampling highly biased, redundant transitions since they compute the sampling rate for each transition independently, without consideration of its importance in relation to other transitions. In this paper, we aim to address the issue by proposing a new learning-based sampling method that can compute the relative importance of transition. To this end, we design a novel permutation-equivariant neural architecture that takes contexts from not only features of each transition (local) but also those of others (global) as inputs. We validate our framework, which we refer to as Neural Experience Replay Sampler (NERS)1, on multiple benchmark tasks for both continuous and discrete control tasks and show that it can significantly improve the performance of various off-policy RL methods. Further analysis confirms that the improvements of the sample efficiency indeed are due to sampling diverse and meaningful transitions by NERS that considers both local and global contexts.
Volatility dynamics and the risk-return relationship in South Africa: A GARCH approach

ARTICLE INFO Nitesha Dwarika, Peter Moores-Pitt and Retius Chifurira (2021). Volatility dynamics and the risk-return relationship in South Africa: A GARCH approach. Investment Management and Financial Innovations, 18(2), 106-117. doi:10.21511/imfi.18(2). 2021.09 DOI http://dx.doi. org/10.21511/imfi.18(2). 2021.09 RELEASED ON Friday, 07 May 2021 RECEIVED ON Wednesday, 06 January 2021 ACCEPTED ON Monday, 05 April 2021
Proceed with Caution

Abstract It is becoming more common that the decision-makers in private and public institutions are predictive algorithmic systems, not humans. This article argues that relying on algorithmic systems is procedurally unjust in contexts involving background conditions of structural injustice. Under such nonideal conditions, algorithmic systems, if left to their own devices, cannot meet a necessary condition of procedural justice, because they fail to provide a sufficiently nuanced model of which cases count as relevantly similar. Resolving this problem requires deliberative capacities uniquely available to human agents. After exploring the limitations of existing formal algorithmic fairness strategies, the article argues that procedural justice requires that human agents relying wholly or in part on algorithmic systems proceed with caution: by avoiding doxastic negligence about algorithmic outputs, by exercising deliberative capacities when making similarity judgments, and by suspending belief and gathering additional information in light of higher-order uncertainty.