Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Type 2- Interval Fuzzy with LQR Controller for Continuous Stirred Tank Reactor

This paper proposes Type 2Interval Fuzzy with LQR (T2IFLQR) controller for a non-linear system. Type 2Interval fuzzy logic controller (T2IFLC) is self-possessed in such a way that it is an autonomous process. To decipher the influence, the impression of uncertainty on the controller execution to two different types of curves are outlined i.e. aggressive control curve and smoother control curve. A comparative of T2IFLQR and Type 2Interval Fuzzy PID (T2IFPID) controller are shown in this paper. Popov-Lyapunov approach is used to define the stability of the framework. It is explained how T2IFLC provides extra degrees of freedom which helps to enhance the control performance of linear as well as nonlinear systems. IndexTerms – Type 2Interval Fuzzy, PID, LQR, CSTR.
EVACUATION ROUTE GUIDANCE AND INDOOR NAVIGATION SYSTEM USING IOT

: With technological advancements, internet is evolving and so is the development of internet of things. Buildings are getting smarter and this trend is gaining momentum. Wireless sensor networks play a key role in this concept. This idea deals with one of the widely used applications of wireless sensor networks, that is, in the field of navigation. When an emergency occurs, wireless sensors detect the danger and directs the evacuees to areas far from it via cyber physical interaction. Emergency navigation is essential to evacuate users trapped in danger to nearby exit. Our focus is not only to guide users providing short path but also safe path. This helps in avoiding congestion as well as leads to the usage of other sub-optimal paths which are often left unused thereby improving the survival rate of evacuees. We suggest Intelligent Escape, a real-time, powerful, intelligent and easy-to-use exit system with a portable interface for emergency cases like fire. Unlike previous work, we examine changing conditions and calculate a personal way who escapes by looking at his features. Intelligent Escape, fast, cheap, multi-functional and mobile supported, collects a variety of natural sensory information and takes into account survivors' signals, using implants. The neural network (ANN) calculates the risk of personal use of each link on the site, eliminates that risk, and calculates the appropriate escape way under current conditions. After that, our program guides those who are excluded from the exit through statistics a way with voice and visual commands on a Intelligent phone. The consideration about the route coordination problem in emergency evacuation of large smart buildings is taken into account. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined.
Post-training quantization of neural network through correlation maximization

—In this paper, we propose a method for quantizing the weights of neural networks by maximizing the correlations between the initial and quantized weights, taking into account the distribution of the weight density in each layer. Quantization is performed after the neural network training without further post-training. We tested the algorithm using the ImageNet dataset for VGG-16, ResNet-50, and Xception neural networks [2]. In the case of ResNet-50 and Xception neural networks, 4-5 bits of memory are required for the weights of a single layer to obtain acceptable Top-5 accuracy, for VGG-16, 3-4 bits are sufficient to store the weights of a single layer.
Graphene Oxide Electroreduced onto Boron-Doped Diamond and Electrodecorated with Silver (Ag/GO/BDD) Electrode for Tetracycline Detection in Aqueous Solution

A new electrochemical sensor designed by modifying the commercial boron-doped diamond (BDD) electrode with graphene oxide (GO) reduced electrochemically and further electrodecorated with silver (Ag), named the Ag/GO/BDD electrode, was selected among a series of the BDD, GOelectroreduced onto BDD (GO/BDD) and silver electrodeposited onto BDD (Ag/BDD) electrodes for the detection of tetracycline (TC) in aqueous solution. The best results regarding the sensitivity of 46.6 µA·µM−1·cm−2 and the lowest limit of detection of 5 nM was achieved using square-wave voltammetry (SWV) operated at the step potential of 5 mV, modulation amplitude of 200 mV and the frequency of 10 Hz in alkaline medium. The application of the alkaline supporting electrolyte-based procedure is limited for water monitoring due to the presence of chloride that interferes with TC detection; however, it can be applied for quantitative determination of pharmaceutical formulations. 0.1 M Na2SO4 supporting electrolyte eliminated chloride interference and can be used for the application of Ag/GO/BDD in practical detection of TC in water.
Title: Dorsomedial prefrontal neural ensembles reflect changes in task utility that culminate in task quitting Abbreviated title: dmPFC activity corresponds to task utility Authors:

1 When performing a physically demanding behavior, sometimes the optimal choice is 2 to quit the behavior rather than persist in order to minimize energy expenditure for the 3 benefits gained. The dorsomedial prefrontal cortex (dmPFC), consisting of the anterior 4 cingulate cortex and secondary motor area, likely contributes towards such utility 5 assessments. Here, we examined how male rat dmPFC single unit and ensemble level activity 6 corresponded to changes in task utility and quitting in an effortful weight lifting task. Rats 7 carried out two task paradigms: one that became progressively more physically demanding 8 over time and a second fixed effort version. Rats could quit the task at any time. Dorsomedial 9 PFC neurons were highly responsive to each behavioral stage of the task, consisting of rope 10 pulling, reward retrieval, and reward area leaving. Activity was highest early in sessions, 11 commensurate with the highest relative task utility, then decreased until the point of quitting. 12 Neural ensembles consistently represented the sequential behavioral phases of the task. 13 However, these representations were modified over time and became more distinct over the 14 course of the session. These results suggest that dmPFC neurons represent behavioral states 15 that are dynamically modified as behaviors lose their utility, culminating in task quitting. 16
The MeSH-Term Query Expansion Models using LDA Topic Models in Health Information Retrieval *

: Information retrieval in the health field has several challenges. Health information terminology is difficult for consumers (laypeople) to understand. Formulating a query with professional terms is not easy for consumers because health-related terms are more familiar to health professionals. If health terms related to a query are automatically added, it would help consumers to find relevant information. The proposed query expansion (QE) models show how to expand a query using MeSH terms. The documents were represented by MeSH terms (i.e. Bag-of-MeSH), found in the full-text articles. And then the MeSH terms were used to generate LDA (Latent Dirichlet Analysis) topic models. A query and the top k retrieved documents were used to find MeSH terms as topic words related to the query. LDA topic words were filtered by threshold values of topic probability (TP) and word probability (WP). Threshold values were effective in an LDA model with a specific number of topics to increase IR performance in terms of infAP (inferred Average Precision) and infNDCG (inferred Normalized Discounted Cumulative Gain), which are common IR metrics for large data collections with incomplete judgments. The top k words were chosen by the word score based on (TP *WP) and retrieved document ranking in an LDA model with specific thresholds. The QE model with specific thresholds for TP and WP showed improved mean infAP and infNDCG scores in an LDA model, comparing with the baseline result.
fabsam @ AMI: A Convolutional Neural Network Approach (short paper)

The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pretrained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020.
Review on Steganography Techniques

With increase in use of internet, user wants to access the data easily and securely at any spot of time. So, user start saving and sharing the important information on cloud. Hence, the security of data on cloud computing become a major issue of concern. Steganography is used as a standard practice for both cloud service providers and cloud users as a tool against unauthorized surveillance. Steganography refers to guarding the information by concealing it in some other media such that only the owner has the ability to safely recover the information. The purpose of this paper is to give an overview of different techniques of steganography used in cloud computing and compare them on the parameters like, technique selection, carrier formats, payload capacity.
Demo: Knowledge Graph-Based Housing Market Analysis

The housing market is complex and multi-faceted, which makes its analysis challenging for users and professionals. We develop a four-step knowledge graph-based knowledge extraction approach for the housing market for efficient and accurate data analysis, consisting of data acquisition and cleaning, entity linking, ontology mapping, and question answering. The proposed system allows one to summarize the housing information for a selected geographical area, analyze the surroundings by collecting census data, understand the medical safety based on COVID-19 data, and the area attractiveness based on celebrities data from DBpedia. Our system can provide personalized recommendations given keywords and information about the market over time. A user-based evaluation demonstrates the utility of our system. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Real-time Tracking and Identification for Multi-Camera Surveillance System

This paper presents a solution for personal profiling system based on user-oriented tracking. Here, we introduce a new way to identify and track humans by using two types of cameras: dome and face camera. Dome camera has a wide view angle so that it is suitable for tracking human movement in large area. However, it is difficult to identify a person only by using dome camera because it only sees the target from above. Thus, face camera is employed to obtain facial information for identifying a person. In addition, we also propose a new mechanism to locate human on targeted location by using grid-cell system. These result in a system which has the capability of maintaining human identity and tracking human activity (movement) effectively.
DHPS_A_254723 145..149

1Eritrean Pharmacovigilance Centre, National Medicines and Food Administration, Asmara, Eritrea; 2BietMekae Community Hospital, Asmara, Eritrea Background: Penicillins have never been associated with any form of hearing disorders. The Eritrean Pharmacovigilance Centre recently received two cases of hearing disorder (including one case of deafness) following the intake of amoxicillin capsules. This study was conducted to assess the causal link between amoxicillin and deafness. Methods: Data mining was carried out in the WHO global database of individual case safety reports, VigiBase, and the Austin Bradford-Hill criteria were used to assess causality. Results: A total of 94 cases of ‘hearing disorders’ related to amoxicillin were retrieved from VigiBase. Of these, 49 (52.1%) were reported as “deafness,” and amoxicillin was marked as the sole suspected drug in 18 and the only drug administered in 13 cases. In the rest of the deafness cases (n=31), amoxicillin was concurrently administered with other drugs and reported as co-suspected. The median time to onset was three days and the outcome was marked as recovered (14), recovering (2), not recovered (16) and unknown (17). In five cases, deafness resolved following the withdrawal of amoxicillin. Conclusion: There appears a causal association between amoxicillin and hearing loss that requires further substantiation with better epidemiologic studies.
Probabilistic Gesture Control for a Robotic Arm

Human-Robot-Interaction and Learning from Demonstration require intuitive and ergonomic means of communication. Guiding a robot through the task of simple interaction with its environment usually requires, additionally to a domain expert, also a robotics expert. Human hand gestures are a valuable communication modality, since (i) humans are very skilled in producing them, and (ii) relevant geometrical quantities can easily be demonstrated. In this thesis, we develop a connection between the human hand and a robot manipulator arm. Different methods for gesture-based robot control, including Cartesian position, are explored, such as live mapping with the manipulator and its action-based control. Definition and implementation of several methods to perform various robotic manipulation use-cases (e.g., opening cabinet drawers, pushing a button, and manipulating objects). The approach was implemented using a selection of static and dynamic gestures and verified in isolated experiments as well as in a user study with seven participants. Two gesture detection mechanisms were implemented and compared: an engineered approach based on selected features and a probabilistic learning-based method. The whole system idea is to forge together different parts of the system, where independently they are widely used, but together, they are unused. The thesis originated for a purpose of implementation Human-Robot-Interaction into the everyday world with a possibility to facilitate the people life. That is where controlling the robot manipulator with a hand can be beneficial.
Overview of Cyber Crime and Cyber Security

: In the field of information technology, cyber security plays an important role. Securing information has become one of today's biggest challenges. The first thing that comes to our mind when we ever think of cyber security is 'cyber crimes,' which are increasing immensely every day. Various governments and companies are taking a number of measures to prevent this from happening. Cyberdelinquency. Cyber protection is also a very major problem for us beyond different initiatives. This paper focuses mainly on information security issues on the new technology. It also reflects on the current cyber security strategies, practices, and developments that transform the face of cyber security.
Building a High-Precision Classifier on Unbalanced Sets of Short Text Messages *

This paper considers the building of a classifier in the case of unbalanced training sets, which consist of very short text messages. We use machine learning to automatic classifier construction. High quality of classification is the main requirement for a classifier (we use precision and recall as the criterion of quality). Imbalance in sets means, that the number of texts belonging to one class is significantly lower than those belonging to the other classes. The development of classifiers on the base of unbalanced samples is a well-known complex problem in Text Mining. A lot of theoretical and experimental researches are devoted to searching for a solution. This is especially the case for relatively short text documents, for which the use of known approaches is not always effective. We give a brief review of methods and argue using Gradient Boosting Machine (GBM) for classification short text messages in the case of unbalanced training sets. We diagnose situations in which the classifier fails, analyze them, form a hypothesis on how we can improve text processing, and configure the settings of the classifier. Also, we investigate and compare two schemes of preprocessing - Bag of Words and Word2Vec. A final section is devoted to experimental results. Experiments were carried out on datasets collected in the process of the daily operation of one of the large bank's branches. These real data have a rather significant imbalance. In the case of the unbalanced training set our voting approach (GBM) improves performance considerably.
EXISTENCE AND EXTENT OF GENDER EQUALITY IN TALENT ACQUISITION AND RETENTION PRACTICES: A STUDY OF IT/

When the World is debating on the inclusion of third gender in every aspect of life and career, we in India are struggling to improve the situation of gender parity at all the levels. This shows why India is ranked 108 in Global Gender Gap Index of World Economic Forum, out of 144 countries. This has been a very extensively researched area. All the disciplines and field of studies take interest in Gender Equality and women inclusion. Be it economics, political science or management studies. This probably is the reason that we have come a long way since post stereotypes and standards of impartiality for women. Management is the area which actually can improve the situation and brighten the picture of gender equality at workplace by implementing and including the women friendly practices and gender parity in their human resource practices. The present paper tries to acquire an array of research from this field to compile a clear picture of the talent acquisition and retention practices in the light of gender equality in IT/ITES organizations based at Delhi/ NCR Region. The study is based on secondary sources of data collected through various databases in the field of management, social sciences and labour studies, etc. It has been proposed that this paper will help in moving ahead in the direction of making working environment ‘equal’ for both male and female employees and conducive for developing human resources the competitive advantage for the organizations in this sector.
The Hormetic Effect of Metformin: “Less Is More”?

Metformin (MTF) is the first-line therapy for type 2 diabetes (T2DM). The euglycemic effect of MTF is due to the inhibition of hepatic glucose production. Literature reports that the principal molecular mechanism of MTF is the activation of 5′-AMP-activated protein kinase (AMPK) due to the decrement of ATP intracellular content consequent to the inhibition of Complex I, although this effect is obtained only at millimolar concentrations. Conversely, micromolar MTF seems to activate the mitochondrial electron transport chain, increasing ATP production and limiting oxidative stress. This evidence sustains the idea that MTF exerts a hormetic effect based on its concentration in the target tissue. Therefore, in this review we describe the effects of MTF on T2DM on the principal target organs, such as liver, gut, adipose tissue, endothelium, heart, and skeletal muscle. In particular, data indicate that all organs, except the gut, accumulate MTF in the micromolar range when administered in therapeutic doses, unmasking molecular mechanisms that do not depend on Complex I inhibition.
Coordination and Control of Hierarchically Organized Interacting Agents

The coordination and control of hierarchically organized interacting agents is an important issue in many applications, e.g., harbor or warehouse automation. A formalism of agents as hierarchical input/output automata is proposed. A system of interacting agents is modeled as the parallel composition of their automata. We extend the usual parallel composition operation of I/O automata with a hierarchical composition operation for refining abstract tasks into lower-level subtasks. We provide an algorithm to synthesize hierarchically organized controllers to coordinate the agents' interactions in order to drive the system toward desired states. Our main contribution regards the formal definition, the representation, the theorems about its properties (i.e., the parallel and hierarchical composition are distributive operations), and the synthesis algorithm, proved to be complete and correct.
Comparative Evaluation of two Different Fractionation Radiotherapy Schedules in Post Mastectomy Cases of Carcinoma Breast

1 Senior Resident, Dept. of Radiotherapy, NSCB Medical College, Jabalpur, India 2 Professor and Head, Dept. of Radiotherapy, NSCB Medical College, Jabalpur, India 3 Demonstrator, Dept. of pathology, NSCB Medical College, Jabalpur, India Corresponding Author Dr Laxmi Singotia Email: singotialaxmi@gmail.com Abstract Background: Breast cancer is the most common malignant neoplasm and a leading cause of death in women worldwide. Radiation therapy for breast cancer is usually performed after surgery and is an integral component of breast-conserving therapy as well as after post mastectomy. The dose of radiation must be strong enough to ensure the eradication of cancer cells. Objectives: To compare two different dose fractionation radiotherapy schedules in terms of: a) Loco regional control & distant metastasis b) Acute and late radiation induced toxicities. Methodology: Study was prospective and patients were divided into two groupsGroup A and Group B. Group AThese patients were given Accelerated Hypofractionated Schedule (Study group) – 40 Gy/17 fractions/3.2 weeks @ 2.35 Gy/fraction. Group BThese patients were given Conventional Fractionation Schedule (Control group) 50 Gy/25 fractions/5 weeks @ 2 Gy/fraction. Results: The median age of patients in both groups was 47 years, and patients were followed up till 18 months. Treatment time in group A and group B were 3.2 weeks and 5 weeks respectively. Patients of both the groups tolerated radiation well with locoregional control, distant metastasis and acute and late radiation reactions comparable in both the groups and there was no difference statistically (p>0.05) . Grade 1 acute radiation dermatitis was the most common reaction. Acute radiation dermatitis in group A and group B was 87% and 85%, late reaction lymphedema was 17% and 15%, locoregional failure was 12% and 15%, distant metastasis was 7.5% and 5% respectively. Conclusion: From the present study we concluded that patients in both the regimen groups tolerated the treatment well with non significant difference (p value>0.05) in radiation toxicities. The overall treatment time (O.T.T.) in group A was significantly less in comparison to group B(p value<0.05).
Explainability and the Intention to Use AI-based Conversational Agents (Short Paper)

The use of conversational agents (CA) based on artiﬁcial intelligence (AI) is increasing in the ﬁeld of recruiting. Recruiting is considered a particular sensitive domain, especially if CAs also make (pre)selection decisions. The black box character of AI decisions may hinder the acceptance and use of CAs as they are not considered to be fair, accountable and transparent (FAT). Explainable AI (XAI) has the goal to make AI decisions more transparent and thus to increase its FAT. But little is known about the perception of XAI by potential job candidates and their intention to use CAs. To investigate this research gap, we conducted a vignette-style questionnaire survey ﬁlled out by 490 persons from a quota-representative population sample for Germany and Austria. Scenarios are varied by (a) the type of XAI approach and (b) by whether the explanations refer to measurable qualiﬁcation or soft skills. The results indicate that XAI increases the intention to use CA in recruiting, compared to CA relying on black box AI.