Miracle-dz/UniFAR-FTUs huggingface dataset:
Using document similarity networks to evaluate retrieval systems

Information Retrieval System Evaluation is important in order to determine the performance of a system’s ability in satisfying the information needs of users. Evaluation of retrieval systems requires constructing test collections, which consists of obtaining documents, topics (information needs) and a set of relevance judgments that indicate the relevant documents for a topic. For a small collection of documents, it is possible to judge every document for relevance. A large document collection will demand an enormous judging effort, which is unrealistic. We present a novel technique for evaluating retrieval systems using minimal human judgments. Our proposed solution initially selects a small ‘seed-set’ of documents which are judged for relevance. The relevance information from this set is then propagated through an adhoc network of document similarities to determine the relevance of other unjudged documents. The original judgments combined with the inferred judgments constitute the complete set of judgments that is used to compare the relative performance of different systems. Our results show that we can effectively compare different retrieval systems using very few relevance judgments and at the same time achieve a high correlation with the true rankings of systems.
Genetic algorithms for clustering, feature selection and classification

Traditional methods for clustering problems, such as the K-means algorithm and its variants, usually ask the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. Therefore, the clustering becomes a tedious trial-and-error work and the clustering result is often not very promising especially when the number of clusters is large. In this paper we propose a genetic algorithm for the clustering problem. This algorithm can automatically cluster the data according to their similarities and automatically find the proper number of clusters. In traditional classification methods, usually a set of parameters is used to represent a class. But in many case, although belonging to the same class, the data may be divided into several clusters and the data in each cluster may have different characteristics. Hence, we also apply the genetic algorithm to the classification problem and obtain good results especially when the situation stated above happened. Another genetic algorithm is proposed for the feature selection problem. This algorithm can not only search for a good set of features but also find the weight of each feature such that the application of these features associated with their weights to the classification problem will achieve a good classification rate. Experimental results are given to illustrate the effectiveness of these genetic algorithms.
BOARD OF STUDIA UNIVERSITATIS BABE Ş-BOLYAI NEGOTIA

Recent developments in theories of Base of Pyramid (BoP) focus on engaging low‐income population as business affiliates in developing countries. Theories of BoP cluster linkages and firms’ internationalization are well established in the literature yet there is scarcity of theoretical postulations which may connect the two. This study, using inductive qualitative methods, aims to extend network theory of internationalization by identifying linkage between inclusive supply chain cluster linkages with BoP firms and internationalization strategy of MNEs in developing host markets. This study focuses on the social sustainability of MNEs operations in host markets. Proposed model postulates sustainable/inclusive supply chain linkages with BoP enterprises as an exogenous variable that generates sustainable value creation process i.e. inclusive innovation and inclusive entrepreneurship. These sustainable/inclusive supply chain linkages, moreover, influence key determinants of MNEs’ internationalization strategy such as market knowledge competence, cooperative capability and native capability/ local embeddedness. The proposed model offers significant addition to the literature on internationalization of and shared value creation in MNEs.
Adapted Physical Activity , 7 ( 1 ) , 32 – 48 © European Federation of Adapted Physical Activity , 2014 MULTIPLICITY AND DEVELOPMENT OF ACHIEVEMENT MOTIVATION : A COMPARATIVE STUDY BETWEEN GERMAN ELITE ATHLETES WITH AND WITHOUT A DISABILITY

While achievement motivation influences success in elite sports, the empirical knowledge on its manifold aspects and its development is quite rudimentary, especially in disability sports. This study analyzes motivation of athletes with a disability (N=73) as opposed to athletes without a disability (N=156). We surveyed these German elite athletes (N=229) in a 2x3 design retrospectively, regarding various facets of achievement motivation during three career phases (initiation, development and mastery). Results showed that enjoying the sport and improving one’s personal performance level have great relevance for athletes with a disability, whereas incentives such as career opportunities and earning money are less important. MANOVAs revealed differences between athletes with and without a disability regarding incentives as well as personal factors. For the latter, nearly all personal factors developed significantly over the career phases. These results provide empirical knowledge for new implications regarding psychological consultations in elite disability sports.
A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.
A randomized trial of acupressure on pain management after cardiac surgery

This randomized controlled study aimed to determine the effects of acupressure on pain management after cardiac surgery. The sample included 100 patients who underwent cardiac surgery, divided into two groups: the intervention group (n=50), who received acupressure and daily nursing care, and the control group (n=50), who only received daily nursing care. Randomization was done by assigning the patients in even-numbered rooms to the intervention group and assigning the patients in odd-numbered rooms to the control group. In the postoperative period, the severity of pain was evaluated using the Visual Analogue Scale (VAS) for 3 days starting from the day that the patients returned to the clinic. Acupressure application decreased the level of pain by 1.86 points in the intervention group in comparison to 0.38 points in the control group. It was found that the acupressure intervention had a significant effect on relieving postoperative pain after cardiac surgery in the intervention group (P<0.001), while the difference in the control group was not statistically significant (P>0.05). Moreover, in the intervention group, acupressure was found to be 59.2% effective for pain management (P<0.001).
Analysis and Visualization of Students’ Learning Based on Multi-Topic Chat Text

The popularization of online chatrooms facilitates students to discuss, question and answer based on their learning content. The chat text reflects students' learning concerns, learning progress, learning enthusiasm and so on, which has important reference and analysis value. But the previous studies on students' chat text were mainly to explore the participation behavior and interaction patterns, while less attention was paid to the dynamic process of students' learning. Meanwhile, the existing technology lacked effective analysis methods for the highly variable and multi-topic commingled chat text. Therefore, this paper proposes the Multi-topic Analysis by Extraction and Combination (MtAEC) architecture. On one hand, MtAEC locks the questioner and respondent in the chat, tracks and collects their text, and calculates the text similarity to verify whether the lock result is correct. This method confirms the role of the questioner and the respondent in the chat. On the other hand, to solve the problem of topic analysis in the multi-topic commingled chat text, MtAEC points the parent pointer to the interrogative sentence, and traverses down the child pointer to search the answer. In addition, this paper analyzes the topic of the students' chat and visualizes the experimental results in the form of charts based on the group chat text of a real course. Then, we summarize the students' learning concerns, learning progress and learning enthusiasm, which are helpful for teachers to better understand the dynamic changes of the students' learning process.
A Novel Implementation of a Hyperspectral Anomaly Detection Algorithm For Real Time Applications With Pushbroom Sensors

Anomaly detection is an increasingly important task when dealing with hyperspectral images in order to distinguish rare objects whose spectral characteristics substantially deviates from those of the neighboring materials. In this paper, a novel technique for accurate detection of anomalies in hyperspectral images is introduced. One of the main features of this method is its ability to process pushbroom data on-the-fly (i.e., line-by-line), being clearly suitable for real time applications in which memory resources are restricted as there is no need to store the whole hypercube. Diverse quality metrics have been applied on testing with real and synthetic hyperspectral data sets in order to compare the accuracy of the proposed algorithm over the state-of-the-art, showing the goodness of our proposal.
Design and Implementation of an Embedded System to Observe the Atmospheric Condition using a Helium Balloon

For conducting any space-related research such as surveillance and communication, launching high power rocket, geographical imaging or even space exploration, it is necessary to know the optimal weather condition at different levels of the atmosphere. One of the most commonly used methods for measuring the atmospheric conditions is using a weather balloon. Of course, this can be done by using modern-day satellite technology. But satellite, for example, provides temperature only on the land surface. As a result, it limits the possibility of providing accurate measurements on temperature profile at different levels of atmosphere or wind speed of the jet stream. An embedded system consists of different sensors is capable of providing profiles of temperature, pressure and relative humidity at different altitude with the help of a helium balloon. In this paper, we have designed and implemented a remotely controlled embedded system. The RF transmitter is linked with the sensors on board of the balloon payload, which sends the measurements back to a ground tracking antenna on a set radio frequency. The display section receives the transmitted data for further processing and acquiring graphical results. In previous research papers on this topic, there was no graphical representation of acquired weather data from the experiment where we have discussed the outcome of the experiment with graphical representation.
Predictive Data Modeling: Educational Data Classification and Comparative Analysis of Classifiers Using Python

Due to an increase in the number of data sources and digital community, there is a huge amount of unstructured data at almost every synergy and in such outline, data mining becomes an important field of Machine Learning. Machine learning can be used for data mining following its two approaches i.e. Supervised learning and Unsupervised learning to find out meaningful information from huge accumulated unstructured data. To increase the quality of education and to find a solution to problems soaring from the complicated educational dataset and contentious environment among the academic institutions, educational data mining is receiving great attention. Educational data mining helps in facilitation and utilization of resources related to student performance, predicting placement results and finding new educational trends. In this paper, classification of student's data in terms of internal assessment given by faculty members and visualization of an educational dataset using Python following multiple Data based classification prediction models and comparative results of classifier models are performed. The classifier models using python which can transform learning are compared and the model having best accuracy measure is considered for predictive analytics and classification of the overall performance of class.
Two phase fuzzy based prediction model to predict Soil nutrient

Soil nutrients play significant role in the crop productivity. The ‘pH’ is one of the main factor which can be used to predict the concentration of nutrients in the soil. In this article, authors have proposed two phase approach to predict soil nutrients. Phase 1 predicts pH value of soil depending on previous time series pH value of soil by using fuzzy model. Phase 2 employs regression model to predict soil nutrients such as Nitrogen (N), Phosphorus (P), potassium (K) based on predicted pH value. The result analysis shows that the experimental results outperforms over statistical parameters.
Standards and Techniques to remove Data Remanence in Cloud Storage

Data security is one of the main concerns for organizations when organizations deploy cloud to store data. Particularly, data leakage is a critical issue in public clouds due to data remanence. The retentive property of storage media is observed as a major threat because it may include sensitive information such as user id, user passwords, secret keys, web URLs, etc. Healthcare records, financial records, defense records are some of the examples where data remanence has serious repercussions. Though consumers vacate cloud volumes, vendors do not securely recycle storage devices. Hence, cold boot attack, harvesting attacks, side-channel attacks are likely to occur in the cloud. It is required to ensure about data integrity and confidentiality of sensitive data within a cloud by organizations or users. It is observed that encryption and data persistence avoidance technology for different memory devices like Hard Disk Drive (HDD), Static and/or Dynamic Random-Access Memory (SDRAM) are widely used. In this paper, we discuss and provide various standards and techniques proposed in the literature to remove the data remanence in the cloud storage. Moreover, we present a comparison of parametric evaluation of overwriting standards to remove data remanence.
Novelty detection on a very large number of memories stored in a Hopfield-style network

Summary form only given, as follows. The Hopfield-style network does not forget any stored memory. Its error-correcting performance, however, degrades rapidly with a large number of stored memories because of the rapid development of spurious memories. The author shows that in spite of this, the network performs well in the case of error detection, specifically novelty detection, even on very large collections of stored memories. The speed with which the network can detect novelty, even on very large collections of stored memories, is the main advantage of this scheme. Efficient distributed storage of large collections of memories is another important advantage. <<ETX>>
A Review on Data locality in Hadoop MapReduce

MapReduce has emerged as a strong model for processing parallel and distributed data for huge datasets. Hadoop an open source implementation of MapReduce has approved MapReduce widely. Hadoop fragments the input file into number of data blocks to allocate them to various DataNodes in cluster. Hadoop must provide effective scheduling to process these data blocks in efficient way. One of the issues that play vital role in efficient processing of MapReduce is Data Locality which is caused due to overhead of network. Data locality is equipped for moving the computation adjacent to the data where it dwells. It is a key resource in distributed environment which influences the tasks accomplishing time. The issues which troubles data locality are cluster and network load, resource sharing, cluster environment, size of data blocks, number of mappers and reducers. This paper aims to review various algorithms that are aware of data locality in scheduling, along with their strengths and weaknesses.
Consumer Perception About Prepaid Energy Meter System-A Study In Khulna City

This research attempts to find out the consumer's perception about prepaid energy meter in Khulna city. In this research 100 consumers of different category like domestic, commercial, small industries were selected in where prepaid meter is installed already for various period of time. This paper reveals that consumer prefer prepaid energy meter to postpaid meter & this is because of very much useful & attractive feature of it.
Human Activity Recognition Using Mixture of Gaussians and Pair-wise Oriented Local Binary Pattern

Tracking and recognizing human activities from video is a very challenging task in the field of Computer Vision. In this paper, we aim to recognize human activities by coping with the existing challenges. At first, the background and the foreground of images in videos are detected using the mixture of Gaussian distributions and the binary silhouette images are obtained. We propose a feature descriptor named Pair-wise Oriented Local Binary Pattern (POLBP) and an improved version of DLBP feature descriptor for images. POLBP is capable of encoding more information than intensity differences of LBP by incorporating orientation information with the intensity difference. This Pair-wise Oriented Local Binary Pattern (POLBP) extracts local orientation information from binary silhouette images. These feature vectors are sent to Support Vector Machine (SVM) classifier for classification. The proposed method has been used in the area of Human Activity Recognition (HAR) and the result of recognition rate is very encouraging.
Premise-part adaptation laws for adaptive fuzzy control and its application to vehicle speed control

In adaptive fuzzy control, approximation accuracy of the designed fuzzy system plays a key role in the overall system performance. Up to now, a linear parameterization method has been used to derive suitable adaptive laws, even in the adaptation of premise-part membership functions. However, the premise-part adaptation schemes with linear parameterization have some fundamental limitation due to the inadequacy of the gradient algorithm for general nonlinearly parameterized functions. In the paper, a new adaptive fuzzy control method with adaptation both of the premise-part and consequence-part membership functions is presented. The proposed adaptive fuzzy control scheme does not suffer from the problems appearing in conventional premise-part adaptation by using a nongradient strategy. The global stability as well as performance enhancement is given via simulations and application results of vehicle speed control.
Optimal Operating Frequency of Inductive Power Transfer through Metal Barriers

In this paper, we firstly establish the model of inductive power transfer (IPT) through metal barriers by using the electromagnetic model. Then we theoretically analyze the operating frequency range of IPT through metal barriers, which includes the optimal operating frequency. After that, the theoretical results are proved by simulation in MAXWELL and SIMPLORER.
MBPSKA: Multi-Biometric and Physiological Signal-Based Key Agreement for Body Area Networks

A body area network (BAN) consists of wireless sensors and actuators deployed on a patient’s body for real-time health monitoring and personalized medical care. It is essential and challenging to secure wireless communications in a BAN to protect the patient’s privacy while also allowing the authorized healthcare practitioners (e.g., emergency room doctors and nurses) to easily communicate with and configure the BAN devices transparent to the patient or even when the patient loses consciousness. With the existing schemes, the devices are based on a pre-installed secret password or a physiological signal feature to authenticate each other and to agree upon a cryptographic key for secure communications. The former requires a patient’s input to access and configure the BAN, and the latter is not sufficiently reliable or secure due to signal dynamics. This motivates us to design a new key agreement scheme in this paper, called multi-biometric and physiological signal-based key agreement (MBPSKA), to achieve more secure and reliable authentication and communication session establishment between the BAN devices while providing flexibility to authorized personnel to access, control, and adjust the BAN without patient involvement. The proposed scheme exploits both the reliable biometric traits and the time-variant physiological signal features of a patient along with the efficient fuzzy crypto-algorithms and key distribution protocols. The devices use multiple biometric and physiological features for mutual authentication and cryptographic key protection. We analyze the security characteristics of MBPSKA, including its capabilities against various attacks. Our evaluation results using the real-world datasets demonstrate that MBPSKA outperforms the existing physiological signal-based key agreement schemes in terms of security, authentication reliability, and accuracy.
An Analysis of Bangladesh One Day International Cricket Data: A Machine Learning Approach

Nowadays Data mining is an emerging field in sports analysis. To choose a most effective team or to predict suitable formation for winning a game or to analyze weakness of the opponent, data mining plays a vital role. However, no research has been done yet for the Bangladesh cricket team. So, we analyzed One Day International cricket data of Bangladesh, based on seventeen features and find out the most important features that are enough for better prediction, not only important features but also can take much decision in our analysis. Our analysis divided into three sections; before starting the game, after one innings played and continuous fall of wickets which leads to the probable prediction of the chances of winning and losing even while the game is in progress. In our analysis, we used the latest version of the decision tree algorithm that is C5.0 on our own collected data set and successfully get the accuracy of 63.63% for before starting the game, 72.72% and 81.81% when Bangladesh played in the first and second innings, finally 80% and 70% for fall of wicket analysis. We also used other classification algorithms and shown the accuracy level of our data set.
Removing Disabilities: Controlling Personal Computer Through Head Movements and Voice Command

At present, several computer systems are available to assist people with special needs (i.e. without hands). However, most of these interfaces are costly or they cause discomfort during prolonged use. In this paper, an algorithm is proposed, that provides a hands-free interface between a user and a computer. It provides effective communication with the computer by interfacing the camera with the user. The head detections movement is based on the AdaBoost algorithm that effectively translates the actions into mouse cursor movements on the screen. During analysis, it was found that the proposed algorithm was highly enhanced with the integration of the AdaBoost algorithm.