Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of an Adaptive Robot Encouraging Teamwork on Students’ Learning

In this work, we designed a teachable robot that encourages a pair of students to discuss their thoughts and teaching decisions during the tutoring session. The robot adapts to the students’ talking activity and adjusts the frequency and type of encouragement. We hypothesize that the robot’s encouragement of group discussion can enhance the social engagement of group members, leading to improved learning and enjoyment. We ran a user study (n = 68), where a pair of participants (dyad) worked together to teach a humanoid robot about rocks and minerals. In the adaptive condition, the robot uses reinforcement learning to maximise interaction between the dyad members. Results show that the adaptive robot was successful in creating more dialogue between dyad members and in increasing task engagement, but did not affect learning or enjoyment. Over time, the adaptive robot was also able to encourage both members to contribute more equally to the conversation.
Auto-Calibração de Câmeras em Visão Estéreo

Resumo—Este trabalho visa o estudo de métodos de calibração automática de uma única câmera e de câmeras utilizados em um sistema de Visão Estéreo. Esta auto-calibração tem a finalidade de reconstruir tridimensionalmente uma cena. A calibração de câmeras é necessária para se obter parâmetros que tornarão possível mapear coordenadas tridimensionais do mundo a partir de coordenadas bidimensionais de imagens. O primeiro método a ser estudado, o de uma única câmera, visa obter os parâmetros intrínsecos de uma câmera. Já o segundo tem como objetivo a obtenção de parâmetros extrínsecos de duas câmeras, o que permitirá a construção de um sistema de Visão Estéreo. Uma breve análises de complexidade de tempo é realizado sobre os dois métodos de calibração estudados, e também é exemplifica a forma de aplicação desses métodos.
A New Approach to Vision-Based Fire Detection Using Machine Learning

Computer vision - based fire detection has recently attracted great deal of attention from the research community. In this paper, the authors propose and analyses a new approach for identifying fire in videos. Computer vision techniques are largely used now a days to detect the fire. There are also many challenges in judging whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature color many other features have to be taken into consideration. This paper is a study of the recent techniques and features extracted by different existing algorithms. In this approach, we propose a combined algorithm for detecting the fire in videos based on the changes of the statistical features in the fire regions between different frames. The statistical features consist of the average of the red, green and blue channel, the coarseness and the skewness of the red channel distribution. These features are evaluated, and then classified by Bayes classifier, and the final result is defined as fire-alarm rate for each frame. Experimental results demonstrate the effectiveness and robustness of the proposed method. There is different method which focuses on various properties of fire like, color, shape, movement, spatio-temporal features etc. For real-time identification of fire from videos simple and accurate method is proposed as multi expert system, which uses color, shape and movement evaluation for detecting fire. The study refers different methods for fire detection and prefers integration of smoke analysis for early identification of fire.
ROLE OF ASSESSMENT OF SERUM LEVELS OF INTERLEUKIN-23 AND POLYMORPHISM OF INTERLEUKIN-12 RECEPTOR Β1 IN PATIENTS WITH ALLERGIC RHINITIS

Background:Allergic rhinitis (AR) is a worldwide health proplem that affect nearly 10–20% of whole population, therefore AR can be the most prevalent chronic non contagious disease. Several factors in the environment contribute to allergic diseases (for example, antibiotics, immunizations, cigarette smoke, pets, and diet); however, it is hard to understand the way of changing the surroundings to decrease the potential risks. Th17 cells might be involved in the process of neutrophil infiltration that occurs during the acute phase of the allergic reaction. The action of the proinflammatory cytokine IL-23 is dependent mainly on IL-12RB1, a type 1 transmembrane receptor that is associated with the p40-domain of IL-23 and promotes its signaling in complex with IL-23R. IL-23 was suggested to be a pivotal cytokine involved in the pathogenesis of AR and may become a novel target in the treatment of AR.
The canonical covers of normal two-dimensional rational triple points

Given Pinkham-Demazure’s description R = R(P , D) of normal twodimensional rational triple point R, we can calculate systematically the canonical cover S = R(Y, D̃) by our theory of graded cyclic cover [19] as in Theorem (1.5). If pg(S) = 0 then R is a quotient singularity and there are infinitely many such cases Theorem (2.3). However, in the case of pg(S) = 1, we can show the finiteness of such R in Theorem (3.1). Further in Theorems (2.4) and (2.6), some special infinite series of rational triple points of A-types, we will see the finiteness of R when the invariants a(S) = a′/s or pg(S) are bounded from above. Our results of the present paper give a good evidence of affirmative direction for finiteness problem about weights of singularity which have been developed in recent studies [17, 25]. 2010 Mathematics Subject Classification. Primary 14J17; Secondary 32S25, 14B05,32S05
A THEMATIC REVIEW ON POTENTIAL ANTIMICROBIAL POTENTIAL APPLICATIONS OF BACTERIOCINS FROM LACTIC ACID BACTERIA

The world-wide emergence of resistant pathogenic strains against the currently available antibiotics and the toxicity associated with them has compelled scientific community to increase their quests in finding other natural antimicrobial alternatives. In this regard recent discoveries on antimicrobial arsenals secreted by bacterial species against competing strains are gaining utmost importance not only because of their antimicrobial but also due to their anti-cancer potential. These natural bacterial arsenals are ribosomally synthesized low-molecular weight, heat stable, membrane active, proteolytically degradable and pore-forming, cationic peptides known as “Bacteriocins”. Owing to their anti-bacterial, anti-viral, anti-fungal, and anti-biofilm potential, bacteriocins seem to be the immediately available promising option for mitigation of AMR (antimicrobial Resistance) crisis and minimization of toxic effects of antibiotics on Host‟s microbiome. The non-toxic and Generally Regarded as Safe (GRAS) status of Lactic acid bacteria (LAB) bacteriocins signifies their huge commercialization potential, and research on their biology is currently the most extensively studied subject of biotechnology. Their intrinsic food preservative and therapeutic potential can be enhanced by combinatorial approaches with other antimicrobial substances. However, their potential to be used as AGP (antimicrobial Growth Promoter) in animal production must also be evaluated. The initial half of this review focuses on the significance and biological aspects of LAB bacteriocins, while the latter half addresses the potential antimicrobial aspects of bacteriocins in various sectors.
Study of Local Anaesthetics, Part 156: Some Physicochemical and Lipophilic Properties of Pyrrolidinoethyl Esters of o-, m-, p-Alkoxy-Substituted Phenylcarbamic Acid

In the series of pyrrolidinoethyl esters of o-, m-, p-alkoxy-substituted phenylcarbamic acid the values of lipophilic parameters (RM, log Papp, log k) increase with lengthening of alkoxy chain on benzene ring, each methylene group increases the lipophilicity. Comparing advantages and disadvantages of different methods for a drug’s lipophilicity determination, as well as experiences from the laboratory, the easiest and best method seems to be HPLC. According to the results, the highest local anaesthetic activity can be assumed by the compounds with 6—8 carbon atoms in the side chain.
Interactive comment on “Long-range transport patterns into the tropical northwest Paciﬁc during the CAMP 2 Ex aircraft campaign: chemical composition, size distributions, and the impact of convection” by Miguel

Response: We thank the two reviewers for their thoughtful suggestions and construc-tive criticism that have helped us improve our manuscript. Below we provide responses to reviewer concerns and suggestions. A major change to note at the outset is that we are re-submitting the paper as a Measurement Report to alleviate concerns about the lack of novelty and/or scientiﬁc implications. measurements and observations in the clean tropical free troposphere.
BIODEGRADABLE IN-SITU FORMING IMPLANTS AND METHODS OF PRODUCING THE SAME BACKGROUND OF THE INVENTION

[54] BIODEGRADABLE IN-SITU FORMING 4,582,640 4/1986 Smestad et al. . l28/DIG. 8 IMPLANTS AND METHODS OF 4,650,665 3/1987 PRODUCING THE SAME 4,677,139 6/1987 4,715,369 l2/l987 [76] Inventors: Richard L Dunn, 451 Boardwalk Dr. 4,772,470 9/1988 RLD, Apt° 501, Fort Collins, Colo. 4,774,227 9/1988 80526; James P. English, 2500 4,793,336 12/ 1988 Melinda Cir;, Birmingham, Ala. 35214; Don. “ R Co 9 4657 FOREIGN PATENT DOCUMENTS
The Practical Implementation of Graph Analysis to Monitoring Process of Information System Implementation in School

This paper considers the problem of implementation of information system in school. We present some techniques which were used in practice to analyze and manage the process of implementation using graph model of users activities. The ﬁrst problem which was solved is the problem of monitoring the process of information system implementation. To solve this problem we use clustering coefﬁcients (global and local) and evaluation of their dynamics. The second problem was to ﬁnd the co-operators who actively use information system and can help administrators to improve the system and involve other co-operators in the process of active use of the system. The results were obtained basing on real data of one of educational complexes in Moscow.
Study of Blacklisted Malicious Domains from a Microsoft Windows End-user Perspective: Is It Safe Behind the Wall?

The Internet is a dangerous place, ﬁlled with diﬀerent cyber threats, including malware. To withstand this, blacklists have been utilized for a long time to block known infection and delivery sources. However, through blacklisting the domain names we are leaving a landscape of threats to be unknown and forgotten. In this paper, ﬁrst, we investigate the current state-of-the-art in cyber threats available on such blacklists. Then, we study the corresponding malicious actors and reveal that those persistently appear since 2006. By shedding light on this part of the cyber threat landscape we target increased information security perception of the landscape from the perspective of the average end-user. Moreover, it is clear that the blacklisting the domains should not be one-way function and need to be regularly re-evaluated. Moreover, blacklisting might not be enforced by client applications in addition to outdated system software leaving real danger. For practical evaluation, we created a multi-focused experimental setup employing diﬀerent MS Windows OS and browser versions. This allowed us to perform a thorough analysis of blacklisted domains from the perspective of the published information, content retrieved and possible malware distribution campaigns. We believe that this paper serves as a stepping stone in a re-evaluation of the once found and then blacklisted domains from the perspective of minimal security protection of a general user, who might not be equipped with a blacklisting mechanism.
Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

Brain tumor detection or brain tumor classification is one of the most challenging problems in modern medicine, where patients suffering from benign or malignant brain tumors are usually characterized by low life expectancy making the necessity of a punctual and accurate diagnosis mandatory. However, even today, this kind of diagnosis is based on manual classification of magnetic resonance imaging (MRI), culminating in inaccurate conclusions especially when they derive from inexperienced doctors. Hence, trusted, automatic classification schemes are essential for the reduction of humans’ death rate due to this major chronic disease. In this article, we propose an automatic classification tool, using a computationally economic convolutional neural network (CNN), for the purposes of a binary problem concerning MRI images depicting the existence or the absence of brain tumors. The proposed model is based on a dataset containing real MRI images of both classes with nearly perfect validation-testing accuracy and low computational complexity, resulting a very fast and reliable training-validation process. During our analysis we compare the diagnostic capacity of three alternative loss functions, validating the appropriateness of cross entropy function, while underlining the capability of an alternative loss function named Jensen-Shannon divergence since our model accomplished nearly excellent testing accuracy, as with cross-entropy. The multiple validation tests applied, enhancing the robustness of the produced results, render this low-complexity CNN structure as an ideal and trustworthy medical aid for the classification of small datasets.
NER using machine learning approaches

Named Entity Recognition (NER) is a subtask of information extraction task that seeks to identify and classify parts in text that consult with predefined classes such as person names, organisations, places, time expressions, quantities and monetary values. This paper gives the detailed of various approaches and techniques that are used for Named Entity Recognition that identifies the named entities from the given knowledge. It explains the different machine learning models that are employed in the Named Entity Recognition. The performances of varied Named Entity Recognition systems that are developed using machine learning models are reviewed. Further, we examine the area and domain of its potential and conventional usage in modern times. We lastly conclude our multifaceted study by mentioning some of the common challenges that are found in the NER technology and suggest some practical solutions for improving the same.
SOCIAL MEDIA AND ITS IMPACT ON USER BEHAVIOR – A METHODOLOGICAL AND THEMATIC REVIEW

Social Media has invaded all walks of life including the way we consume content. The literature is rich with research on various aspects of social media and its impact on user behavior, organisations, and the society. This systematic literature review has been done to highlight the key themes and methodologies in extant literature for social media and its impact on user behavior. With 61 journal articles as the base of this review, 4 main research themes emerged, namely consumer motivation, effects of the platform format, content type, and the inverse impact of user behavior on the platforms. On exploration of the methodologies used in the relevant literature, a variety of approaches i.e. qualitative, quantitative, mixed, and non-empirical studies have been used to analyze social media usage and its impact on user behavior. Based on the methodological and thematic review, recommendations are provided for future research.
ASSESSMENT OF INTRAOVARIAN ARTERIAL DOPPLER ULTRASOUND OF POLYCYSTIC OVARIAN SYNDROME

Background: The fact indicates the importance of transabdominal color Doppler sonography of intra-ovarian arteries in the clinical diagnosis of polycystic ovary syndrome. At present, the lack of uniform data and absence of cut-off values make vascular indices impractical for discriminating between polycystic and normal ovaries. The aim of this study is to investigate the importance of transabdominal color Doppler sonography of intra-ovarian arteries in the clinical diagnosis of polycystic ovary syndrome. Patients & methods: A case control study done in Salah Al-Deen general hospital. The study carried out during the period (1st Jan-12th June 2021). A simple random sample of 35 patients with PCOs compared with 35 normal patients. Data were collected through:1-Questionnaire:containing information regarding demographic and general information of the patients: age, body weight, height, Marital status and BMI, clinical and obstetrical information’s. 2-Transabdominal US examination for identification of PCOS with standard sonographic criteria, & Intra-ovarian artery Pulsatility and Resistive indices measurement in both diseased and normal group.3-Laboratory exam of the luteinizing hormone, follicle stimulating hormone, LH/ FSH ratio & prolactin. Results: The mean ovarian PI was significantly lower among cases (1.29 ±0.94) compared to controls (1.98 ± 0.38). The mean ovarian RI was significantly lower among cases (0.62 ± 0.14) compared to controls (0.78 ± 0.07. The mean ovarian volume was significantly higher among cases (14.35 ± 3.17) compared to controls (6.10 ± 2.71). The ovarian PI show significant negative correlation with serum LH (mIU/L) (r=-0.65), LH/FSH ratio (r=-0.76), BMI (r=-0.34), prolactin (r=0. 41), ovarian volume (r=-0.57). Ovarian RI show significant negative correlation with serum LH (mIU/L) (r=-0.631), LH/FSH ratio (r=-0.775), BMI (r=-0.36), prolactin (r=0. 37), ovarian volume (r=-0.51). Conclusions: Ovarian artery Doppler analysis show decrease in PI & RI among PCOS patients & this indicate that it is a cheap, easily available, non-invasive technique in diagnosis of PCOS patients.
Controlling Certificates for Grid Security Authentication and Authorization System

— Grid authentication and authorization services are aimed at verifying the identity of an entity, managing certificates and to restrict from unauthorized accesses to grid resources. Hence, it plays a vital role to get the system availability as well as to prevent the attackers who tries to gain the unauthorized accesses to resources. In fact, this paper proposes the secure certificate framework to improve the security of the certificates based on Grid Security Infrastructure by making each server and each client keep track of how many times a certificate is used and accepted. The main contribution of this paper is using the counting process to secure Authorization and Authentication service for Grid Application.
A historical data analysis of water‐energy nexus in the past 30 years urbanization of Wuxi city, China

In this article, the historical data of water and energy consumption of Wuxi City, one of the main cities in the Taihu Lake Basin region in Eastern China, was reviewed and analyzed to map their dynamic changes in the rapid urbanization process of the past 30 years, as the urbanization ratio changed from 24.7% (1983) to 73.7% (2013). Results show that (a) water and energy resources have generally been managed separately in overall social‐economic development planning of the city and region; and (b) the increase of water and energy consumption closely correlates with the rate of urbanization and urban growth. A Water‐Energy Nexus Chart (WENC) is proposed as a potential tool to provide a snapshot on water and energy management to show relative efficiency of water use in energy production and energy use in water treatment and supply provision. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 46–55, 2018
Understanding Noise Injection in GANs Supplementary Material

In fact, we find that each weight matrix Wi has a low rank structure. We conduct PCA to the weight matrix W0, . ,W7. The results are reported in Fig. S2. We compute the number of components that have strength larger than 1% of that of the maximum component. The results are reported in Tab. S2. We can find that the valid components of each weight matrix are around 450, which means each of the weight matrix will drop around 60 dimensions of the inputs.
Conceptual design of a MeV Ultrafast Electron Diffraction Based on 1.4 Cell RF Gun

Ultrafast Electron Diffraction (UED) is a powerful tool to investigate the dynamic structure with temporal scale of 100 femtoseconds and spatial scale of atomic length. To achieve high quality diffraction patterns, the transverse emittance and the longitudinal length of electron bunches should be reduced. MeV UED, using photocathode RF gun instead of traditional DC gun, is being developed to produce high quality electron bunches with lower emittance and shorter length. We are developing a MeV UED facility based on a 1.4 cell photocathode RF gun that can provide higher acceleration gradient at Huazhong University of Science and Technology. In this paper, the conceptual design of the MeV UED is proposed with typical parameters of the system, as well as the ASTRA simulation results of optimization.
Wood Microscopic Image Identification Method Based on Convolution Neural Network

To prevent the illegal trade of precious wood in circulation, a wood species identification method based on convolutional neural network (CNN), namely PWoodIDNet (Precise Wood Specifications Identification) model, is proposed. In this paper, the PWoodIDNet model for the identification of rare tree species is constructed to reduce network parameters by decomposing convolutional kernel, prevent overfitting, enrich the diversity of features, and improve the performance of the model. The results showed that the PWoodIDNet model can effectively improve the generalization ability, the characterization ability of detail features, and the recognition accuracy, and effectively improve the classification of wood identification. PWoodIDNet was used to analyze the identification accuracy of microscopic images of 16 kinds of wood, and the identification accuracy reached 99%, which was higher than the identification accuracy of several existing classical convolutional neural network models. In addition, the PWoodIDNet model was analyzed to verify the feasibility and effectiveness of the PWoodIDNet model as a wood identification method, which can provide a new direction and technical solution for the field of wood identification.
F ASTER AND S MARTER A UTO A UGMENT : A UGMEN - TATION P OLICY S EARCH B ASED ON D YNAMIC D ATA - C LUSTERING

Data augmentation tuned to datasets and tasks has had great success in various AI applications, such as computer vision, natural language processing, autonomous driving, and bioinformatics. However, most of the specific parameter-based augmentation strategies are inefficient in finding suitable augmentation parameters to improve the model performance whenever the dataset changes. We introduce a dynamic data augmentation strategy called Faster and Smarter AutoAugment (FSAA) that separates the data augmentation method through the initial policy search result. Based on our policy branching principle, the augmentation policy dynamically clusters data points within the dataset according to the degree of performance change at the data split stage. With extensive experimentation on various datasets for image recognition task, we show that FSAA dramatically reduces the GPU computation cost, a problem in the existing automatic data augmentation strategies, by up to over 90%. It also ensures diversity and generalization of the dataset augmentation by searching for more diverse policies than existing autonomous methods in less time, resulting in consistent performance gains and robustness across multiple models.