Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on Performance Evaluation of Vertical Rotary Plough

In Uttarakhand state, about 70 % population is living in rural areas and depends directly or indirectly on agriculture. Uttarakhand (28°43' to 31° 27' N and 77°34' to 81°02' E) is located in the North Western Himalayas and is blessed with the climate favourable for the good International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 12 (2018) Journal homepage: http://www.ijcmas. com
A study on analysis of cardiovascular diseases

Commonly seen in adults, cardiovascular diseases are important health problems. In order to investigate the causes of the diseases which affect the heart and the blood vessels, two datasets were used. First, one of these datasets is publicly available dataset provided by the University of California, Irvine Machine Learning Repository. The effects of biochemistry and hemogram laboratory test results for the Cardiovascular Diseases were analyzed by using the second dataset which was taken from the cardiology and other services of Yildirim Beyazit University Ataturk Training and Research Hospital. ICD-10 (International Statistical Classification of Diseases and Related Health Problems) booklet was taken as a reference for the patient and control groups. The successes of the classifier algorithms indicated that working with the datasets which have only limited number of attributes is not right step.
Guided assembly using deep learning-based object recognition and augmented reality instructions

– Efficient assembly is vital to guarantee quality and celerity in manufacturing processes. Usually, the assembly tasks in a non-robotic process are expressed by assembly instructions that provide text and drawing information, both in 2D and 3D, about the form and order of the execution of tasks. However, these instructions are rarely interactive and, depending on their complexity, hard to follow. The main objective of this master’s work is to develop an approach capable of providing the user with interactive step-by-step guidance for assembly tasks. Deep learning techniques will be applied to recognize, in real-time, equipment parts, indicate the assembly order, and the coupling to other parts. The assembly instructions will be provided exploring augmented reality. We plan to implement the solution on a mobile device and assess and compare our solution with more traditional ones, considering assembly speed-up and perceptual feedback from users.
AJAR-06-2021-0097_proof 208..226

Purpose – The authors examine the impacts of corporate announcements on stock returns during the pandemic stress. Design/methodology/approach –The authors employ the event studymethodologywith themarket model on a sample of 90 events (announcement and ex-date). Findings – The authors find that all the corporate announcements do not impact the stock returns in a similar pattern.While the bonus announcement, ex-bonus and ex-split events led to positive significant abnormal returns on the event date, the rights issue and stock-split announcements failed to influence the stock returns. The findings suggest that before making such announcements, the corporates should wait until the market recovers because even the positively impacting events result in negative market responses during pandemic stress. Practical implications – This study will guide the policymakers to stimulate share prices during such pandemics with the help of various corporate announcements. The investors will be assisted in understanding the stockmarketmechanism andmakingwise decisions before reacting to corporate actions during a pandemic or emergency period.While the policymakers are concernedwith influencing the share prices, the investors are concerned with the composition of the risk-return parameters in their portfolio. This study will act as an essential investment tool for both. Originality/value – To the best of the authors’ knowledge, the authors conduct the first-ever study to examine the impacts of corporate announcements during a pandemic stress period that significantly contributes to the literature. The authors examine the announcement effects in India and accurately anticipate that this study will be a pioneer in this field. This study also paves the way for future researches in this area.
Towards CityMoS: A Coupled City-Scale Mobility Simulation Framework

—Simulation has become one of the primary tools for the evaluation of transportation systems. To investigate novel concepts such as autonomous vehicles or car-2-car communication, new simulation and modelling techniques are required to increase the meaningfulness and impact of simulation studies. To this end, we develop CityMoS, an easy-to-use, city-scale, microscopic, agent-based mobility simulation framework. The main intended use of this framework is the assessment of autonomous vehicles (AV) with a particular focus on the interaction with conventional vehicles (CV). In order to achieve this, we aim at revising conventional car-following models as well as introducing novel hierarchical models for AVs. A focus will lie on providing a validated city-scale scenario that researchers can parametrise and use to evaluate novel applications. Lastly, we put an emphasis on making our simulation environment interoperable with existing tools either by coupling them or by providing open interfaces to interact with CityMoS.
Prejudice Masquerading as Praise: The Negative Echo of Positive Stereotypes

a body of accumulating evidence across a range of groups suggests that hearing a positive stereotype in an intergroup interaction as praise and, the contrary, Abstract Five studies demonstrate the powerful connection between being the target of a positive stereotype and expecting that one is also being ascribed negative stereotypes. In Study 1, women who heard a man state a positive stereotype were more likely to believe that he held negative stereotypes of them than women who heard no stereotype. Beliefs about being negatively stereotyped mediated the relationship between hearing a positive stereotype and believing that the stereotyper was prejudiced. Studies 2 to 4 extended these results to Asian Americans and accounted for alternative explanations (e.g., categorization threat). In Study 5, the same positive stereotype (e.g., good at math) was directed to Asian American men’s racial or gender identity. Their perceptions about whether negative racial or gender stereotypes were being applied to them depended on the identity referenced by the positive stereotype. Positive stereotypes signal a latent negativity about one’s group, thereby explaining why they can feel like prejudice.
Prospective primary teachers’ knowledge of problem solving process

Studies on teachers’ understanding of processes such as problem solving have focused primarily on their beliefs and conceptions to the detriment of features indicative of their professional knowledge. This study analyses the knowledge about problem solving process revealed by prospective primary teachers. A questionnaire specifically designed for the study was administered to 61 undergraduates beginning and 53 ending their training. Although characterising solving as a dynamic process, both groups, in their description of stage characteristics and strategy identification, revealed that their knowledge was essentially theoretical.
Drivers of Sustainability, Sustainable Business Practices and their Impact on Firm Performance: An Exploratory Study of Indian Manufacturing Small and Medium Enterprises

Sustainability and sustainable business practices have become very relevant in today’s context in the backdrop of growing environmental pollution and social inequity. As far as industrial activities are concerned, large companies would seem more responsible than small and medium enterprises (SME) for harming the environment; however, SMEs representing well over 90% of worldwide businesses cannot shirk their responsibility either. Although the individual impact of SMEs on the environment may be minimal, collectively they cause a lot more impact on the environment than large companies. In this paper, an exploratory study of Indian manufacturing SMEs has been conducted in terms of the causal relationships among the drivers of sustainability, sustainable environmental and social practices, and firms’ financial/non-financial performance. The study develops a scale relevant in the context of Indian SMEs, which can be used in future research, not only for Indian SMEs, but also for SMEs belonging to other developed and developing countries. One of the most important contributions of the study, which distinguishes it from the extant literature, is that the study considers all the three dimensions – economic, environmental and social – of sustainability, and takes an integrative view in exploring the causal relationships among the drivers of sustainability, sustainable business practices and firm performance. Exploratory/confirmatory factor analysis and structural equation modelling have been employed for data analysis. Some of the major findings of the study are as follows. There is a strong positive impact of the leadership and ethical orientation of owners/managers and employees on sustainable waste management and human resource management (HRM) practices, environmental benefits realized by firms due to adoption of sustainable practices and firms’ financial/non-financial performance. No relationship has been found between sustainable waste management practices 1 Professor, IIM Calcutta, Email: subrata@iimcal.ac. in
Eﬃcient Computation of (2 n , 2 n )-Isogenies

Elliptic curves are abelian varieties of dimension one; the two-dimensional analogue are abelian surfaces. In this work we present an algorithm to compute (2 n , 2 n )-isogenies of abelian surfaces de-ﬁned over ﬁnite ﬁelds. These isogenies are the natural generalization of 2 n -isogenies of elliptic curves. Our algorithm is designed to be used in higher-dimensional variants of isogeny-based cryptographic protocols such as G2SIDH which is a genus-2 version of the Supersingular Isogeny Diﬃe-Hellman (SIDH) key exchange. We analyze the performance of our algorithm in cryptographically relevant settings and show that it signiﬁcantly improves upon previous implementations. Diﬀerent results deduced in the development of our algorithm are also interesting beyond this application. For instance, we derive a formula for the evaluation of (2 , 2)-isogenies. Given an element in Mumford coordinates, this formula outputs the (unreduced) Mumford coordinates of its image under the (2 , 2)-isogeny. Furthermore, we study 4-torsion points on Jacobians of hyperelliptic curves and explain how to extract square-roots of coeﬃcients of 2-torsion points from these points.
Estimating Efficient Production Functions Under Increasing Returns to Scale

AN earlier paper by one of us (Farrell, 1957) developed, inter alia, a method for estimating efficient production functions from observations of the inputs and outputs of a number of individual production units. The method consisted essentially in plotting these observations as points in a space of a suitable number of dimensions, forming the convex closure of this set of points and taking the appropriate part of the surface of this convex closure as the estimate of the efficient production function. In the case where n inputs are used to produce a single output under conditions of constant returns to scale, if output is represented by X and the inputs by xl, x2, . , Xw then each firm can be represented as a point in n-dimensional space with coordinates (xl/X, x2/X, ., x./X). This set of points is then augmented by the points at infinity on each of the n axes, and the convex closure of the augmented set is formed. The surface of this convex closure, omitting the facet at infinity, is our estimate of the efficient production function. The earlier paper was primarily concerned with this simple version of the method. Not only was it used for the basic exposition but a large part of the paper was devoted to applying it to observations on American agricultural production. The paper did, however, discuss at a formal level the relaxation of the assumptions of single product and constant returns to scale, and in Section 2.3 showed how the method could be generalized to the case of m outputs X1, X2, . , Xm by treating each observation as a point in n + m-dimensional space with coordinates (X,, X2, ., Xm, x1, x2, ., xn). A broadly similar procedure which would accommodate the possibility of decreasing returns to scale was indicated in Section 2.4. Underlying the use of the method in all these cases is the assumption that the efficient production function to be estimated is convex. In no case does this assumption hold necessarily, but it is one frequently made in economics, and will probably hold in most actual situations. Certainly there is nothing in the nature of multiple outputs or decreasing returns to scale that is inconsistent with the convexity assumption. This happy state of affairs ceases to obtain as soon as we consider a situation with increasing returns to scale, because this essentially involves a non-convex production function; indeed they are in practice probably the most important example of nonconvex production functions. The estimation of efficient production functions under
Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It

Thompson Sampling with Linear Payoffs (LinTS) is popular contextual bandit algorithm for solving sequential decision making problems. While LinTS has been studied extensively in the academic literature, surprisingly, its behavior in terms of reproducibility did not receive the same attention. In this paper, we show that a standard and seemingly correct LinTS implementation leads to non-deterministic behavior. This might go unnoticed easily, yet impact results adversely. This calls the reproducibility of papers that use LinTS into question. Further, it forbids using this particular implementation in any industrial application where reproducibility is critical not only for debugging purposes but also for the trustworthiness of machine learning models. We first study the root cause of the non-deterministic behavior. We then conduct experiments on recommendation system benchmarks to demonstrate the impact of non-deterministic behavior in terms of reproducibility and downstream metrics. Finally, as a remedy, we show how to avoid the issue to ensure reproducible results and share general advice for practitioners.
Ontology verbalisation hands-on exercise @ JOWO 2022

The main objective of this exercise is to introduce participants to the task of generating natural language text from an ontology. Participants will be tasked with designing templates for generating English, German, and isiZulu text. The three languages have been chosen chosen in order to expose participants to challenges, or lack thereof, that may be due to the complexity of each output language’s grammar. To complete this exercise, participants must form groups of 2-4 people, if possible.
Unifying Intensifiers Ourselves ∗†

Focusing on English herself , we propose a unified analysis of all its usages as intensifier, despite the different meaning contributions it displays, by exploiting the complex structure of VPs and the possibility for focus to project. We extend this analysis to own as in his own book which qua intensifier, displays the same range of meaning contributions. Furthermore, given that intensification and reflexivity triggers are often morphologically identical (e.g. reflexives), we briefly sketch how this analysis can also unify intensifiers with reflexives.
Estimation of Cell SOC Evolution and System Performance in Module-Based Battery Charge Equalization Systems

Large-scale battery systems have been applied to a number of grid-level energy storage services such as microgrid capability and distribution upgrade due to the penetration of solar/wind energy. In these battery applications, charge imbalance among battery cells/modules/packs becomes a common issue, which can reduce available battery capacity, accelerate battery degradation, and even cause some safety hazards. To tackle this issue, various battery charge equalization (BCE) systems have been proposed in recent decades, among which the module-based BCE system is widely viewed as a promising solution and has drawn increasing attention. In this paper, we study the module-based BCE systems by presenting a mathematical model that can characterize the charge transfer behavior in such systems, and then propose computationally efficient algorithms to estimate the instantaneous battery cell state of charge (SOC), charge equalization time, and charging/discharging time, based on given system parameters and various initial battery cell SOCs. In addition, the conditions are derived to ensure that all battery cells can reach charge equalization and then get fully charged/discharged together without overcharging/overdischarging. All theoretical results are illustrated and justified through extensive numerical experiments.
Mobile Robot Localization Through Online SLAM with Modifications

For autonomous navigation, we have thus far proposed MCL using environmental maps based on cadastral data. However, buildings in the cadastral data sometimes differ from their actual positions in the environment. As the environmental map is generated from the cadastral data, the inconsistency affects the localization performance. For this problem, we propose an online SLAM approach in the actual environment. A mobile robot simultaneously localizes the position and builds another online map using NDT scan matching. In contrast to other offline SLAM approaches, however, pose graph optimization for loop closure is not executed during online SLAM. As a result, the online map is distorted by localization errors. For this challenge inherent in online SLAM, the localization errors are modified using MCL and wheel odometry in a hybrid manner. As a contribution to autonomous navigation, the robot is enabled to localize the position even in a new place. In the experiments, we show that the localization performance of the robot in an outdoor environment with inconsistent buildings is improved compared to other online approaches with and without modifications.
Analyzing Twitter Conversations on Side Effects of Covid-19 Vaccine

This paper mines social media conversations collected using #sideeffects during the early phases of the Covid-19 vaccine roll out. The multi-dimensional analysis seeks to uncover post-vaccination symptoms and their intensity, latent underlying emotions, how the discourse spreads and is received, and ultimately separate the content into two groups according to their severity. The paper infers that people share their severe symptoms that cause major inconvenience and disruption using exaggerated emotions, and the follower networks of these authors are stronger. Tweets outlining mild symptoms are liked and retweeted many more times, and the friends networks of these authors are stronger. Both types of tweets (severe and mild) express heavy surprise and some fear. Classification of the tweets into mild and severe groups is attempted using boosting, neural network and BERT-based natural language transformer methods. BERT significantly outperforms the former two achieving a F1-score of 0.88. Given that the Covid-19 vaccine was developed and deployed rapidly, with limited clinical data on its side effects, this research fills an important gap in gathering such information under real-life settings. The paper thus concludes with a discussion of how these findings could be leveraged by public health organizations to combat vaccine hesitancy and maximize vaccine uptake.
A Multi-Information Detection Model for Violent Images based on YOLOv3-SPP and DenseNet

The Internet has become an important carrier of people's daily life, and it is constantly developing and growing. In order to create a healthy and benign Internet environment, these sensitive pictures need to be identified and filtered. In the field of sensitive image detection, the violent image detection technology is still in an immature stage, and there is still a lot of room for development in the detection accuracy in the real world. Current research mainly uses classification models ResNet, DenseNet and target detection model YOLOv3 to detect violent images. However, the classification model has the problem of paying attention to the global information and ignoring the target's location information and local target information. Target detection has the problem of only identifying specific local targets and ignoring the global information. In order to solve this problem, this paper proposes a multi-information violence image detection model based on YOLOv3-SPP and DenseNet. Combining the information of the two models through the multi-information model improves the detection accuracy of violent images by 1.3% compared with DenseNet and 2.2% compared with the ResNet model.
Multi-type road extraction and analysis of high-resolution images with D-LinkNet50

Road data form remote sensing is important for GIS modeling, vector analysis, and smart cities. Recently, there has been many scholars have successively combined deep learning with road extraction to meet practical needs. Based on the former research, this paper uses D-LinkNet50 which combines the pretrained LinkNet architecture with the dilation convolution. Training on the data set provided by DigitalGlobe, the results have shown that this D-LinkNet50 has achieved 83.1%, 79.7%, 81.3% in accuracy, recall, and F1-score, respectively, which is higher than that of D-LinkNet34 network 0.7%, 1.4%, 1.0%. So the extraction accuracy is significantly improved.
Binary Jaya Algorithm based on Dice Similarity for Cancer Classification using Microarray Dataset

Microarray datasets are very high-dimensional datasets used for cancer classification. The performance of the classification model deteriorates due to the curse of dimensionality. Feature selection techniques are used to deal with the curse of dimensionality and to improve the performance of the classification model. The dimensionality of microarray dataset is reduced using Jaya based wrapper method. The proposed technique is based on the concept of Dice similarity measure hence it is named Dice Jaya. Feature selection is a combinatorial optimization problem that operates in binary search space. The distance (dissimilarity) between solutions in the binary search space is calculated by calculating the dice dissimilarity between them. The assessment of the proposed technique has been done on five benchmark datasets. The performance of proposed approach is compared with five well regarded techniques and the performance improvement is observed in majority of the cases. The classification accuracy of more than 95% is achieved in 80 percent datasets. In addition, the computational time is reduced by 50% to 33% on all the datasets. Moreover, fast convergence is also achieved.
A review of CNN accelerators for embedded systems based on RISC-V

One of the great challenges of computing today is sustainable energy consumption. In the deployment of edge computing this challenge is particularly important considering the use of embedded equipment with limited energy and computation resources. In those systems, the energy consumption must be carefully managed to operate for long periods. Specifically, for embedded systems with machine learning capabilities in the Internet of Things (EMLIoT) era, the convolutional neural networks (CNN) model execution is energy challenging and requires massive data. Nowadays, high workload processing is designed separately into a host processor in charge of generic functions and an accelerator dedicated to executing the specific task. Open-hardware-based designs are pushing for new levels of energy efficiency. For achieving energy efficiency, open-source tools, such as the RISC-V ISA, have been introduced to optimize every internal stage of the system. This document aims to compare the EMLIoT accelerator designs based on RISC-V and highlights open topics for research.