Miracle-dz/UniFAR-FTUs huggingface dataset:
Connection Between Firebase and Raspberry Pie Through Wireless Local Area Networking

This paper presents an innovative redesign of a doorbell system in order to eliminate unnecessary ringing noise from users’ daily life. Employing artificial intelligence for face recognition, the IoT doorbell system define the visitors as complete strangers or someone who is expected. The next step operates based on this result; the doorbell system will either ring or send out notification to the users’ phone depending on the familiarity of the visitor and the user.
Dear Editor, Please find attached a second revised version of the manuscript entitled “Towards an assessment of riverine dissolved organic carbon in surface waters of the Western Arctic Ocean based on remote sensing and biogeochemical modeling” by V. Le Fouest,

Please find attached a second revised version of the manuscript entitled “Towards an assessment of riverine dissolved organic carbon in surface waters of the Western Arctic Ocean based on remote sensing and biogeochemical modeling” by V. Le Fouest, A. Matsuoka, M. Manizza, M. Shernetsky, B. Tremblay, and M. Babin. Based on your recommendations about the manuscript # bg-2017-286, we thank you to allow us providing a second revised version of the manuscript which takes into account all the reviewers’ comments. Following your request, we provide below a point-by-point response to the reviewers and a list of all relevant changes made in the manuscript. The changes corresponding to the major comments of both reviewers are coloured in red in the revised version.
Centroid-Based Lexical Clustering

Conventional lexical-clustering algorithms treat text fragments as a mixed collection of words, with a semantic similarity between them calculated based on the term of how many the particular word occurs within the compared fragments. Whereas this technique is appropriate for clustering large-sized textual collections, it operates poorly when clustering small-sized texts such as sentences. This is due to compared sentences that may be linguistically similar despite having no words in common. This chapter presents a new version of the original k-means method for sentence-level text clustering that is relay on the idea of use of the related synonyms in order to construct the rich semantic vectors. These vectors represent a sentence using linguistic information resulting from a lexical database founded to determine the actual sense to a word, based on the context in which it occurs. Therefore, while traditional k-means method application is relay on calculating the distance between patterns, the new proposed version operates by calculating the semantic similarity between sentences. This allows it to capture a higher degree of semantic or linguistic information existing within the clustered sentences. Experimental results illustrate that the proposed version of clustering algorithm performs favorably against other well-known clustering algorithms on several standard datasets.
MANAJEMEN KINERJA MTs MUHAMMADIYAH SONTANG DENGAN MENGGUNAKAN MALCOLM BALDRIGE CRITERIA FOR PERFORMANCE EXCELLENCE

This research is based on systematic self assesss based on quality monitoring system in schools is new and very rarely done systematically, it can be seen in educational institutions such as schools, in general schools have no assessment of school performance / madrasah as a whole. The research method used is the method with quantitative approach, while the data collection technique used is through the method of observation, interview and data processing with CalculationUsingSPSSVersion22. After doing research in the field and analyzed by supporting theories, it shows that In General MTs Muhammadiyah Sontang still have opportunity for improvement especially in giving response to learning and integration in process criteria, and for the result criteria only show very little comparison and integration reported by MTs Muhammadiyah Sontang. The value of points obtained by MTs Muhammadiyah Sontang is 285.25 points of the total value of 1000 are in the classification of poorly and included in the early result. It is therefore recommended that the School should create an application document that can be used as a guide in determining scores and improving school performance on an ongoing basis in accordance with Malcolm Baldrige Criteria for Performance Excellence. This research can be continued by integrating Malcolm Baldrige Criteria for Performance Excellence with 8 (eight) accreditation criteria in order to achieve better performance. Assessment of a school is subjective, so that the next assessment should be done in groups, so that the consensus from the results of research so that the results of research become more valid.
Tone mapping and blending method to improve SAR image visibility

It is possible to interpret the shape of buildings based on synthetic aperture radar (SAR) images acquired using airborne SAR systems. However, the pixel values of SAR images have a high dynamic range intensity, and SAR images cannot be displayed without dynamic range intensity compression. Thus, we have developed a tone mapping method and a blending method. Using our method, the tone of SAR images becomes seamless with no intensity gap.
Applying Machine Learning the Assessment of Problem-Solving Skills

This project explores how machine-learning algorithms could be applied to automate assessment of college students’ scientific-problem-solving skills, using log data generated in the PhET “Black Box Problem” simulation. Specifically, we investigated the performance of different machine-learning algorithms trained on summary vectors of students’ interactions to predict their problem-solving performance. The Artificial Neural Network (ANN) model achieved relatively high performance as measured by traditional binary classification metrics such as F! score.
Measurement of Culture & Organizational Effectiveness – A Study of Firms in India

Organizations today face challenge of designing not only an enabling organizational structure that drives performance but also to ensure sustainability by giving considerations to influences that drives the performance. Organizational culture is one such enabling factor that ensures the enabling and alignment between organization structure and also performance. In this study we have looked at measurement of organizational culture and organizational effectiveness of Indian firms by measuring perceptual indicators of employees working in Indian firms. The study adopts questionnaire where responses of employees of 134 Indian firms from NSE 500 were analysed using step wise approach of structural equation modelling.The study reveals high construct validity and reliability of measurement model of culture and organizational effectiveness.
A MULTI-LEVEL FRAMEWORK FOR DEMAND FULFILLMENT IN A MAKE-TO-STOCK ENVIRONMENT- A CASE STUDY IN CANADIAN SOFTWOOD LUMBER INDUSTRY A MAKE-TO-STOCK ENVIRONMENT- A CASE

: This paper proposes a demand fulfillment process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising, for a commodity market characterized by prices and demand seasonality. Considering differentiated customers, different products and multiple sourcing locations in a multi-period context, we define a multi-level decision framework in order to support short and medium term sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. Our research exhibits three valuable elements: (1) we developed an order promising model based on nested booking limits and which allows order reassignment i.e. changing decisions of how firm orders have to be fulfilled; (2) we used a rolling horizon simulation to evaluate performance of the demand fulfillment process proposed; and (3) we compare it with common fulfillment processes such first-come first-served order processing. In order to evaluate the demand fulfillment process proposed, a numerical application based on softwood lumber manufacturers located in Eastern Canada is conducted and provides evidence that better performances (overall service level, high-priority service level and overall net profit) can be achieved by using nested booking limits and reviewing previous order promising decisions whilst respecting sales commitments.
Synthesis of Graphene Functionalized with ( SDS ) for Removal of 137 Cs ( I ) and Ce ( III ) Ions from Radioactive Waste

Received January 10, 2016; Accepted March 29, 2016 Abstract: Sodium dodecyl sulfate (SDS) was chosen to obtain a sandwich type assembly of SDS and graphene (GR) sheets. The modified (SDS/GR) and unmodified GR were characterized by powder X-ray diffraction (XRD), highresolution transmission electron microscopy (HRTEM), Fourier transform infra-red (FTIR) spectroscopy, as well as thermal (TG) and TMA analyses. The SDS obviously prompts the dispersion of GR into a typical wrinkled layered structure. The addition of SDS into GR has lowering the coefficient of thermal expansion (CTE). 137 Cs is the most currently radioactive isotope generated in the area around the Chernobyl disaster. Cerium is quantitatively concentrated and separated from Egyptian monazite by leaching the monazite lanthanide oxides cake. So, SDS/GR was applied to remove Cs(I) and Ce(III) from aqueous solutions using a batch technique. The factors pH, contact time, temperature, and dosage were investigated. The highest adsorption capacity of SDS/GR is found to be 44 and 60 mg gfor Cs(I) and Ce(III), respectively. The adsorption of Cs(I) and Ce(III) onto SDS/GR fitted the pseudo-second-order model. It was found that the adsorption processes have reached their equilibrium state in about 2h, which is faster than most of carbonbased adsorbents can do. From the thermodynamic parameters the sorption process was endothermic and spontaneous in nature.
Nahid SHAHMEHRI NOVEL TRENDS AND TECHNIQUES USABLE FOR SOPHISTICATED APPLICATION LAYER DENIAL OF SERVICE ATTACKS DETECTION

As increasing number of security threats and attacks continuously appear and security in the network has become a basic requirement, the need of developing flexible, reliable and automated security mechanisms that can detect and respond to threats in real time has posed a big challenge for researches. This paper focuses on description of Application layer Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, which present a continuous critical threat to the Internet services. Over some period of time, researchers proposed many solutions to prevent the DoS/DDoS attacks from different OSI layers, but there has been done only a very small research on application layer. In this paper, we consider sophisticated attacks that utilize legitimate application layer requests from legitimately connected network machines to overwhelm Web server. In this paper we propose several known mechanisms to combat application layer DoS/DDoS attacks continuing with proposing most recent approaches and trends which are concurrently under the development.
Stability properties of a Fluid-Structure Interaction problem : towards Physics based stabilization techniques

Fluid-Structure Interactions is a very active research field and its potential and reach is out of question. In order to develop algorithms and coupling strategies to be employed in the modelling of Fluid Structure Interaction problems in a partitioned scheme, it is of special interest to gain insight in the regions where instabilities and other non-linear effects occur. The present contribution analyzes a well suited FSI case, complex enough to present all the physical relevant properties of the unstable region of interest, but simple enough to allow the use of linear stability analysis to identify and characterize the general unstable mechanisms common to FSI cases. The model will be introduced and results on the solid body trajectories, the synchronization mechanisms and the numerical stability of a loosely coupling algorithm will be presented. Similitudes with the canonical system of vortex induced vibrations of a two-degrees of freedom circular cylinder will be pointed out, suggesting interesting lines of future research.
Transfer Learning with Random Network Distillation Theory & Reinforcement Learning

Deep Reinforcement Learning methods have enjoyed great success on a variety of environments. One reason behind this success is the fact that a majority of the environments these methods have been trained on have dense rewards, allowing the algorithms to quickly learn better policies. When the environment has sparse rewards, however, this success is not replicated. DeepMind’s DQN, for instance, achieves 0% of human performance on Montezuma’s Revenge, an Atari 2600 game with notoriously sparse rewards (Mnih et al., 2015, p. 3). In situations such as these, we must change the manner in which our agents interact with the environment, such as by using exploration bonuses. One recent success within this domain is OpenAI’s Random Network Distillation (RND), a method which has achieved state of the art performance in Montezuma’s Revenge, capable of regularly discovering 24 rooms in the first level of the game and exceeding average human performance (Cobbe et al.) RND’s success is impressive, regularly achieving good results in hard exploration Atari games (Burda et al, 2018, p.8). We do not, however, want our methods to have good performance only on the environment that they have been trained on. A key feature of human intelligence is the ability to transfer insights acquired while solving one problem to more easily solve another. In other words, we want our policies to be generalizable. Cobbe et al., in the OpenAI blog post presenting RND, note that RND’s ability to generalize was not yet tested, specifically pointing out “train[ing] a curious agent on many environments without reward and investigat[ing] the transfer to target environments with rewards” as one future direction for research. In this project, my goal is to evaluate, albeit on a small scale, the capability of policies learned by RND to transfer to other tasks.
Student Academic Performance Prediction using Artificial Neural Networks: A Case Study

Students dropout and delay in graduation are significant problems at Katsina State Institute of Technology and Management (KSITM). There are various reasons for that, students’ performances during first year is one of the major contributing factors. This study aims at predicting poor students’ performances that might lead to dropout or delay in graduation so as to allow the institution to develop strategic programs that will help improve student performance and enable the student to graduate in time without any problem. This study presents a neural network model capable of predicting student’s GPA using students’ personal information, academic information, and place of residence. A sample of 61 Computer Networking students’ dataset was used to train and test the model in WEKA software tool. The accuracy of the model was measured using well-known evaluation criteria. The model correctly predicts 73.68% of students’ performance and, specifically, 66.67% of students that are likely to dropout or experience delay before graduating.
A ug 2 01 9 A new integral equation for the first passage time density of the Ornstein – Uhlenbeck process

The Laplace transform of the first passage time density of the Ornstein– Uhlenbeck process for a constant threshold contains a ratio of two parabolic cylinder functions for which no analytical inversion formula is available. Recently derived inverse Laplace transforms for the product of two parabolic cylinder functions together with the convolution theorem of the Laplace transform then allow to derive a new Volterra integral equation for this first passage time density. The kernel of this integral equation contains a parabolic cylinder function and the Fortet renewal equation for the Ornstein–Uhlenbeck process emerges as a special case, namely when the order q of the parabolic cylinder function is set at 0. The integral equation is shown to hold both for constant as well as time dependent thresholds. Moreover, the kernel of the integral equation is regular for q 6 −1.
Analysis of Abnormal Crowd Movements based on Features Tracking

Abnormal events in public places can be detected in real time by analyzing the video sequences from video posts, web or surveillance cameras. Sudden changes of speed and/or direction for all people moving in the scene may indicate the occurrence of an abnormal situation and in some conditions also the position in which the event occurred. The detection and tracking of the people in the scene require high computing resources which make this solution difficult to be applied for real time analysis especially in case of crowded scenes. In this paper is proposed an analyzer of scenes participants moving patterns. The Scale Invariant Features Transform (SIFT) is used for features extraction and the Lucas-Kanade (LK) algorithm for optical flow computation. The main advantage is that it requires low computing resources being suitable for real-time processing. The results obtained by applying the proposed method are compared to those obtained by other approaches presented in literature and also to the subjective evaluation of human observers. Key-words: Features tracking, Lucas-Kanade algorithm, Scale Invariant Features Transform.
Developing a low-cost 3 D plant morphological traits characterization system

A low-cost three-dimensional (3D) plant reconstruction and morphological traits characterization system was developed. Corn plant seedlings were used as research objects for development and validation of the 3D reconstruction and point cloud data analysis algorithms. In this application, precise alignment of multiple 3D views generated by a 3D time-of-flight (ToF) sensor is critical to the 3D reconstruction of a plant. Previous research indicated that there is strong need for high-throughput, high-accuracy, and low-cost 3D plant reconstruction and trait characterization phenotyping systems. This research produced a 3D reconstruction system for indoor plant phenotyping by innovatively integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard pattern beacon array to track the position and attitude of the 3D ToF sensor and, thus, accomplished precise 3D point cloud registration over multiple views. Specifically, algorithms for beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed for such a low-cost but high-performance 3D reconstruction solution. A plant analysis algorithm in a 3D space was developed to extract the morphological trait parameters of the plants by analyzing their 3D point cloud data. The phenotypical data obtained by this novel and low-cost 3D reconstruction based phenotyping system were validated by the experimental data generated by instrument and manual measurement. The results demonstrated that the developed phenotyping system has achieved promising measurement accuracy, fast processing speed while offering a low hardware cost, lending itself to a practical means of acquiring detailed 3D morphological traits for automated indoor plant phenotyping.
Bootstrapping N = 3 superconformal theories

We initiate the bootstrap program for N = 3 superconformal field theories (SCFTs) in four dimensions. We consider the problem from two fronts: the protected subsector described by a 2d chiral algebra, and crossing symmetry for half-BPS operators whose superconformal primaries parametrize the Coulomb branch of N = 3 theories. In order to describe a protected subsector of a family of N = 3 SCFTs, we study a 2d chiral algebra with super Virasoro symmetry that depends on an arbitrary parameter, identified with the central charge of the theory. Turning on to crossing, we work out the superconformal block expansion of the crossing equations and apply standard numerical bootstrap techniques in order to constrain the CFT data. We obtain bounds valid for any theory but also, thanks to input from the chiral algebra results, we are able to exclude solutions with N = 4 supersymmetry, allowing us to zoom in on a specific N = 3 SCFT.
Estimation of Electromagnetic Source Location in Multipath Environment

†Faculty of Engineering, Graduate school of Engineering, Tohoku University 6-6-05 Aoba, Aramakiaza, Aoba-ku, Sendai, 980-8579 Japan E-mail: †{nakatsu, chenq, sawaya}@ecei.tohoku. ac.jp Abstract A novel method to estimate the source location of electromagnetic (EM) waves in a complicated multipath urban areas is developed. The method is based on the multiple signal classification (MUSIC) algorithm and reciprocity of transmitting and receiving antennas. Results are shown to demonstrate the validity of the proposed method. This method can be applied to searching for the location of the illegal radio station in the urban areas. Keyword MUSIC,Antenna arrays,Multipath urban areas
NK combinations do not alleviate the effects of salt stress on gas exchange, photosynthetic pigments and growth of cotton (Gossypium hirsutum L.)

The water resources in the semi-arid region of Northeast Brazil commonly have high concentrations of salts, compromising the quality of the water used in irrigation. Thus, it is necessary to study the feasibility of using these resources in agriculture. The present study aimed to evaluate gas exchanges, photosynthetic pigments and growth of white cotton cv. BRS 368 RF using irrigation water of different salinity levels and fertilization with combinations of nitrogen and potassium, under greenhouse conditions in the municipality of Campina Grande-PB, Brazil. Treatments were arranged in randomized blocks in a 5 x 4 factorial scheme, with five levels of electrical conductivity of water–ECw (0.7, 2.2, 3.7, 5.2 and 6.7 dS m-1) and four combinations of fertilization with nitrogen and potassium (70/50, 100/75, 130/100, 160/125% of the recommended dose), with three replicates. There was degradation in chlorophyll a and b levels and in cotton growth, but the cultivar BRS 368 RF markedly increased carotenoid biosynthesis at 75 days after sowing. Nitrogen and potassium application in the combination of 100/75% of their respective recommendations led to increase in plant height. The supply of nitrogen in combination with potassium did not alleviate the deleterious effects of salt stress on gas exchange, photosynthetic pigments and growth of cotton.
Theoretical Insights into Contrastive Unsupervised Representation Learning Student :

Unsupervised Representation Learning has had a tremendous impact in several domains like computer vision and natural language processing (NLP). Contrastive unsupervised methods to learn representations of data points have led to a simplification of downstream classification tasks which use these representations. However, a theoretical understanding of when these methods will succeed is lacking. In this report, we use the framework presented in [Arora et al., 2019] to guide the construction of better training objectives which we further test with experiments. Furthermore, we highlight the limitations of unsupervised learning in the form of counter examples showing when unsupervised learning is doomed to fail. We verify our results by conducting experiments on commonly used function classes for classification in the domains of computer vision.