Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Data Mining Method for Finding Competitors from Large Unstructured E-Commerce Data

: Data mining is the popular area of research that facilitates the improvement process of the business, such as the preference of the user of the mining. Mining information from the web used to obtain the opinion on the products or services. In the current competitive business scenario, it is necessary to identify the competitive characteristics and factors of an item that most affect its competitiveness. The competitiveness assessment always uses the opinions of customers in terms of rating reviews and an abundant source of information from the web and other sources. The problem is to find the top competitors in other domain by considering the features of a particular domain. The system proposes a C 4.5 algorithm for better accuracy to find the top competitors. The unstructured data is structured by using the K-means algorithm. Then this structured data is clustered into an appropriate domain by using our proposed C 4.5 algorithm. For pattern matching previously used Apriori algorithm has some disadvantages so the system use FPgrowth algorithm. The rules provided by FPgrowth algorithm are more accurate than the Apriori algorithm. Finally the result shows that the proposed system is require less time to find top competitors and it more accurate than existing systems.
MPEG-1 Layer III Standard: A Simplified Theoretical Review

MP3 is a standard that is used for encoding/decoding audio data. The standard can lower audio bit rate significantly without any loss. For this reason, it is key to understand how it does so, and secondly, find out if it is doable. Raw audio signals carry large data quantities and are neither suitable for transmission nor storage [2]. Therefore it is necessary to compress audio and at the same time maintain its quality as required by the International Organization for Standardization (ISO). MP3 was developed by the Motion Pictures Experts Group for audio and video compression. It is composed of three modes; the third referred to as Layer III. It is this layer that lowers raw-audio data bit rates from i.e. 1.4 Megabits per second to just 128 kilobits per second and can still reconstruct the signals to a level comparable to the original [3]. The objective of this paper is to review and provide a simple idea of what Layer III does in relations to audio compression and decompression.
Implementasi Face Recognition Menggunakan Metode Haar-Cascade Classifier Untuk Sistem Keamanan Pintu

This paper contains the results of a trial of implementing a technology-based security system using Face Recognition with the haar-cascade classifier method and notifications using a telegram application that is applied to miniature doors using Raspberry Pi 2 as the main microcontroller. Where if you want to unlock the selenoid face the user must first be registered into the dataset. Based on the results of trials that have been done that the system can 100% distinguish faces that have been registered in the dataset with faces that are not registered in the dataset
Integrating Web 2.0 Tools in Writing Class to Promote Assessment for Learning

Technology has been implemented for instruction with various resources to explore course content and assess students’ learning. In this study, Web 2.0 tools was implemented in writing classroom for Higher education students. A Web 2.0 tools that was used was because it is known as an online means and has been used to perpetuate interactions and communication within a collaborative learning. Assessment is central to teaching and learning, from which a decision whether or not the goals of education are being met. At the same time, the demand of today’s era makes it quite challenging for educators to find the best way to assess their students. Action research method was employed in the study in which observation and individual semi-structured interviews we re used to collect the data. The result of the study is the knowledge of implementation of Web 2.0 tools in EFL (English as foreign language) classroom, from which a guidance of using Web 2.0 tools to promote assessment for learning will be arrived at. This study concludes that Web 2.0 tools can be applied to promote Assessment for learning, and it has several benefits to the student s to improve their learning quality.
Development of «whole» Evaluation Algorithm of the Control Quality of «cupola – Mixer» Melting Duplex Process

The object of research is the control of the «cupola – mixer» duplex process, the subject of research is the quality of the «whole» control system for melting. The term «whole» means that it is not individual control operations that are evaluated, but the ability of the control system operating in the workshop to provide specified requirements for the quality of cast iron. <br><br>One of the most problematic places is the lack of algorithms for the managerial staff to obtain objective information about the quality of management of duplex melting processes. This leads to the fact that it is not possible to get an answer to the question in which particular area or at which particular time point of the process implementation violations were made regarding the control of the heat. <br><br>In the course of the study, methods were used to calculate sample statistical functions, test hypotheses about the form of the distribution law, regression analysis, analysis of variance, and probability theory methods. These methods were integrated into a general algorithm, called the algorithm for assessing the «whole» quality of control of the duplex melting process. <br><br>The proposed algorithm allows to quantify the quality of duplex process control by such an indicator as the probability of deviations in chemical composition. As well as the duration of the unsteady mode corresponding to the stabilization time of the chemical composition by process control operations provided for by the control system adopted in the workshop. The practical implementation of the algorithm based on a series of industrial meltings has been completed. <br><br>The obtained results of testing the algorithm make it possible to consider it an effective tool for obtaining objective information about the quality of control of the duplex process of «cupola – mixer» melting. This is due to the fact that the correct formulation and solution of the problem ensured the receipt of adequate results. In contrast to existing empirical estimates, the proposed algorithm allows to develop options for improving control processes. As a justification for this conclusion, conceptual options for the technical implementation of the regulatory system are proposed. In particular, a simple regulator circuit is proposed, which provides the possibility of maintaining a constant temperature of cast iron and cupola productivity by regulating the level of idle and metal charges. In this case, the process is controlled by supplying control signals to the actuators of the hopper gates with coke and a metal charge formed by manual or automated control systems.
Time Allocation of Working Mothers with Preschool Children: Juggling Time between Work and Care

Mothers` labor market participation has increased significantly in Korea. This article explores how working mothers in Korea with children under the age of six allocate their time in order to spend time with children, and what substitutes they rely on when they are unable to provide childcare. Mothers who participate in the labor market have begun to narrow the gap in child-care time between themselves and mothers who are full-time homemakers; they often compensate for insufficient care time during the week by increasing care time on weekends and increasing the quality of time spent with their children. Substitute caregivers include grandparents and, to a much lesser extent, fathers, but the burden of childcare has not been effectively shared within the family, and working mothers continue to face significant time pressure.
Spectrogram Based Window Selection for the Detection of Voltage Variation

This paper presents the application of spectrogram with K-nearest neighbors (KNN) and Support Vector Machine (SVM) for window selection and voltage variation classification. The voltage variation signals such as voltage sag, swell and interruption are simulated in Matlab and analyzed in spectrogram with different windows which are 256, 512 and 1024. The variations analyzed by spectrogram are displayed in time-frequency representation (TFR) and voltage per unit (PU) graphs. The parameters are calculated from the TFR obtained and be used as inputs for KNN and SVM classifiers. The signals obtained are then added with noise (0SNR and 20SNR) and used in classification. The tested data contain voltage variation signals obtained using the mathematical models simulated in Matlab and the signals added with noise. Classification accuracy of each window by each classifier is obtained and compared along with the TFR and voltage PU graphs to select the best window to be used to analyze the best window to be used to analyze the voltage variation signals in spectrogram. The results showed window 1024 is more suitable to be used.
Body-Boundary-Refined Human Parsing Network based on Fully Convolutional Network and Conditional Random Fields

Human parsing is still a big challenge in computer vision task to accurately label every body part in an image. Current methods of sematic segmentation mainly focus on dividing each independent part and ignore the structural priors of human body. However, the position relationships of different parts, especially which around the area of boundary, indicate their associations and also contribute to the semantic segmentation. In our work, we propose a body-boundary-refined part to refine the segmentation result of human part edge by simply utilizing the structure priors around the body boundary. It puts a penalty mechanism on wrong marginal pixels to improve segmentation performance around the area of body boundary. The network achieves competitive performance on the PASCAL-Parts-dataset and especially the area around the body boundary has been refined.
A Comparative Study of Crime Investigation using Data Mining Approaches

: In the era of technology, various kind of research work has been conducted on detecting, predicting and analysis of crime by researchers. Many researches are based on data mining (DM) for prediction of crime. Crime prediction is an effort to identify and reducing the future crime. DM has the capacity of extracting hidden patterns & beneficial information from the big crime records sets. Existing models of crime records mining detect the crime pattern using identification of crime characterization. In this paper, Literature survey reveals the proposed crime detection techniques by various researchers. Hence, this paper is based on comparative study of clustering, entity extraction, deviation detection association rule mining sequential pattern mining, social network analysis & classification strategies of DM. So it is used for crime detection & prevention. This paper provide scope for identification of limitation in proposed existing researches in the field of crime records prediction by available DM techniques & observe the strength & weakness of existing data mining algorithm .
Target recognition in synthetic aperture radar image based on PCANet

Automatic targets recognition (ATR) for synthetic aperture radar (SAR) image is very important. ATR can be used in traffic management, national frontier safety, and so on. Traditional algorithms for SAR ATR is composed of extraction features and training classifier. The features are essential for the classification accuracy. However, choosing good features by hand is a hard task. The deep convolutional neural networks (CNNs) which can learn features automatically have got a great performance in natural images. However, the CNNs have many parameters and need a lot of data to train such networks. The remote-sensing data of SAR is limited. Then, the authors need a simple network which needs not much data and easy to train. The principal component analysis network (PCANet) is a shallow network that performs well in the recognition task and needs no hand features choosing. Though this network has produced a wide application in the natural images, it is rarely used in the SAR images. The experimental result of the moving and stationary target acquisition and recognition (MSTAR) dataset shows that the PCANet can achieve over 99% accuracy on ten-class targets. This result is better than traditional algorithms and is very close to the results of deep-learning methods.
LWS: A LoRaWAN Wireless Underground Sensor Network Simulator for Agriculture Applications

Wireless Underground Sensor Networks (WUSNs) is an emerging area of IoT applications. However, the current wireless sensor networks yield results that are not feasible nor scalable for a real-world application due to their short transmission range in an underground environment. The emerging technology of Low Power Wide Area Network (LPWAN) which promises large numbers of devices at long range and low energy consumption offers a potential solution for WUSN applications. Among the existing LPWAN technologies, LoRa/LoRaWAN is one of the most popular choice providing a reasonable data rate at long range and low power consumption. This paper focuses specifically on LoRa/LoRaWAN technology as a potential solution for WUSNs in agriculture applications. A novel discrete event LoRaWAN WUSN simulator, LWS, is designed to incorporate a soil path loss model, which enables more accurate modelling of WUSNs in a real deployment. The feasibility and scalability of LoRaWAN in WUSN applications are investigated and demonstrated through simulations.
Helicobacter pylori cagA-positive strains: gastric cancer susceptibility

Despite of decreasing in gastric cancer incidence in western countries, gastric cancer is known as a deadly disease in many of countries. Based on WHO categorize, north of Iran is known as a region with high gastric cancer incidence in the world. Helicobacter pylori (H. pylori) is identified as a risk factor in gastric cancer induction. cagA gene is one of the most important virulence genes of H. pylori. The aim of this research is to study the relation between H. pylori and gastric cancer and also the detection of cag- positive strains in Northern Iran. Participants in this study were 52 gastric cancer cases. For detection of H. pylori infection and presence of cagA gene we used PCR method, with specified primers. About 69% of gastric cancer patients were infected to H. pylori and 67% of H. pylori infected patients were cag-positive. Our results suggest that the H. pylori cagA positive strains increase the gastric cancer susceptibility.
Plug-in for creation of gamified courses in the e-learning environment moodle

The article examines the challenges associated with designing and delivering effective training. The authors propose the use of serious games in learning (in particular gamification) to achieve higher motivation in learners than traditional e-learning. Pedagogical approaches, theories and pedagogical models, suitable for the design and construction of gamified e-courses are presented and analysed. A plugin is presented for creating gamified e-courses in a Moodle e-learning environment. The plug-in can be used to automatically transform a standard e-course into a gamified one or the initial creation of a gamified course. It is shown how standard Moodle environment elements can be used in game contexts.
On the Need for New Anti-phishing Measures Against Spear Phishing Attacks

—We provide an extensive analysis of the (unique) characteristics of phishing and spear-phishing attacks. We argue that spear-phishing attacks cannot be well-captured by current countermeasures, and identify ways forward. We exemplify this by analyzing an advanced spear-phishing campaign targeting white-collar workers in 32 countries worldwide.
A Data-driven Approach for General Visual Quality Control in a Robotic Workcell

Setting up computer vision quality control tasks in a robot workcell is expensive, time consuming, and often requires expert knowledge. In this work, a highly adaptable approach to mitigate this issue is introduced. First, an ontology of atomic building blocks is defined, where each block represents one computer vision algorithm. Second, these blocks are used to generate a complex graph structure. The generation follows a set of rules, which can be shown to the inexperienced worker as a list of simple questions. Afterwards, this graph can be refined. Finally, the graph is integrated into the robotic assembly sequence, and the visual quality control procedure can be executed. Two industrial use cases show the feasibility of this approach. Thus, this work leverages computer vision aided quality control tasks in robot workcells.
An Efficient Elliptic Curve Cryptography-Based Without Pairing KPABE for Internet of Things

The increasing deployment of Internet of Things in diversified fields provides groundwork for smart living. However, at the same time, it brings challenges like security and privacy. Among the numerous proposed security schemes, attribute-based encryption (ABE) is considered as one of the most promising security and privacy preserving scheme in distributed environment (or cloud environment). Many variants of the ABE exist, but most of them employ the expensive bilinear pairing operations. In this article, we have proposed a lightweight elliptic-curve-cryptography-based key-policy ABE without bilinear pairing and having a feature of key refresh/update mechanism. Also, the key distribution is done by the authority, which incorporates direct attribute/user revocation. The scheme is secured under elliptic curve decisional Diffie–Hellman assumption. The performance analysis demonstrates that the proposed scheme is efficient as compared to the existing ABE schemes.
Experiments in Retinal Vascular Tree Segmentation using Deep Convolutional Neural Networks

Artificial Intelligence has been emerging as one of the most promising and relevant current domains. Thus, its study has been increasingly developed and deepened, generating new solutions in the most diverse areas of our society. Particularly, in the area of computer-aided diagnosis, an increasing number of studies and projects have arisen, demonstrating the growing interest raised by this type of technologies. This dissertation represents, in this context, an attempt to analyze and explore the usage of Deep Convolutional Neural Networks in the segmentation of the retinal vascular tree in ophthalmic images. To achieve such goal, a model of a Convolutional Neural Network was developed and several training cases were executed with different parameters, to evaluate its behavior. In an early stage, the images obtained from online image databases, namely DRIVE and STARE, were slightly pre-processed and then patches of size 32 by 32 pixels were extracted to train the neural network. Therefore, the developed algorithm is supervised, once previous information about the central pixel of each patch was used to train the network. In the following phase, the model was implemented, having been tested different architectures (number and type of layers) using the Keras API. The procedure described so far, as well as all the experiments conducted, are part of the training process. Afterwards there was a test phase, in which the model was tested in a new unseen set of images to evaluate the performance of the algorithm. The final classifier was tested on 20 images from DRIVE, having achieved an AUC of 0.87, accuracy of 80%, sensitivity of 85% and specificity of 79%, with a segmentation time of 13 minutes, which translates into 39 seconds per image.
KANTİL REGRESYON ANALİZİNDE BOOTSTRAP TAHMİNİ

Regresyon analizi, istatistik ve ekonometrik tahmin modellerinde siklikla kullanilan tahmin yontemlerinden biridir. Regresyon analizinde yardimci analiz yontemlerden biri de kantil regresyon yontemidir. Regresyon analizinde yardimci analiz yontemlerden biri de kantil regresyon yontemidir. Kantil regresyon modelinde herhangi bir dagilim varsayimi gerekmemektedir ve cesitli kantillere bagli olarak parametre katsayilarini tahmin ettigi icin asiri degerlerin bulundugu yapidaki veri setlerinde daha iyi tahminler vermektedir. Ayrica kantil regresyon degisen varyansin belirlenmesine imkân saglamaktadir. Dogrusal regresyon analizinde ise veri yapisinin model icin uygun olmasi gibi sartlar vardir. Veri yapisindaki asiri degerlere karsi iyi sonuclar sunmamaktadir. Bu calismada; Dogrusal regresyon ve Kantil regresyon yontemleri tanitilmis ve aralarindaki farklar belirtilmistir. Bootstrap yontemi hakkinda bilgiler verilmistir. Uygulama kisminda ise 2000-2017 yillari arasi aylik Uretici Fiyat endeksi(Ufe), Ufe(-2) donem gecikmesi ve Beklenti Anketi verileri kullanilmistir. Bu veriler Bootstrap yontemiyle belirli duzeylerde veri sayilari arttirilarak Dogrusal ve Kantil Regresyon yontemleri Ortalama Mutlak Sapma (OMS) ve Hata Kareler Ortalamasi Karekoku (HKOK) sonuclari karsilastirilarak hangi yontemin en uygun modeli tahmin ettigi uzerine calisilmistir. Sonuclar, Dogrusal ve Kantil Regresyon(50) yontemlerinin OMS ve HKOK degerleri birbirine en yakin ve en kucuk degerleri ile bu iki yontem en uygun modelleri tahmin ettigini gostermistir.
Optimal Priority-Based Allocation Mechanisms

This paper develops a tractable methodology for designing an optimal priority system for assigning agents to heterogeneous items while accounting for agents’ choice behavior. The space of mechanisms being optimized includes deferred acceptance and top trading cycles as special cases. In contrast to previous literature, I treat the inputs to these mechanisms, namely the priority distribution of agents and quotas of items, as parameters to be optimized. The methodology is based on analyzing large market models of one-sided matching using techniques from revenue management and solving a certain assortment planning problem whose objective is social welfare. I apply the methodology to school choice and show that restricting choices may be beneficial to student welfare. Moreover, I compute optimized choice sets and priorities for elementary school choice in Boston. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
Scoring-Based ML Estimation and CRBs for Reverberation, Speech, and Noise PSDs in a Spatially Homogeneous Noise Field

Hands-free speech systems are subject to performance degradation due to reverberation and noise. Common methods for enhancing reverberant and noisy speech require the knowledge of the speech, reverberation and noise power spectral densities (PSDs). Most literature on this topic assumes that the noise power spectral density (PSD) matrix is known. However, in many practical acoustic scenarios, the noise PSD is unknown and should be estimated along with the speech and the reverberation PSDs. In this article, the noise is modeled as a spatially homogeneous sound field, with an unknown time-varying PSD multiplied by a known time-invariant spatial coherence matrix. We derive two maximum likelihood estimators (MLEs) for the various PSDs, including the noise: The first is a non-blocking-based estimator, that jointly estimates the PSDs of the speech, reverberation and noise components. The second MLE is a blocking-based estimator, that blocks the speech signal and estimates the reverberation and noise PSDs. Since a closed-form solution does not exist, both estimators iteratively maximize the likelihood using the Fisher scoring method. In order to compare both methods, the corresponding Cramér-Rao Bounds (CRBs) are derived. For both the reverberation and the noise PSDs, it is shown that the non-blocking-based CRB is lower than the blocking-based CRB. Performance evaluation using both simulated and real reverberant and noisy signals, shows that the proposed estimators outperform competing estimators, and greatly reduce the effect of reverberation and noise.