Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms Designs and Implementations for Page Allocation in SSD Firmware and SSD Caching in Storage Systems

The emerging flash memory based SSD technology poses a new challenge for us to effectively use it in storage systems. Despite of its many advantages that promise a performance breakthrough for the next generation storage systems, the technical limitations of flash memory write operations call for innovative designs that can overcome the long latency of small random write pattern and the limited lifetime of flash memory cells. Previous studies have attempted to address the write performance limitations by trading ca- pacity at the firmware-level such that the write access pattern can be transformed to avoid small random write patterns. Although it may improve the write performance, it does not utilize the data access locality information available at system level to achieve optimal performance. In this thesis, we study techniques to improve the performance via. system-level designs. We consider a major application scenario of flash SSDs – SSD based disk cache. Based on a new disk cache model for SSD based cache, we propose cache management algorithms which utilize the data access local- ity for write access pattern transformation with minimal cost to cache hit ratio. We implemented the proposed algorithm in an SSD based second-level disk cache system. The results show that the proposed algorithm can significantly improve the performance of existing cache management algorithms. To maximize the lifetime of flash SSDs, we study techniques that can reduce the number of write operations, which consume the life-cycle of flash memory cells. Unlike previous ap- proaches of flash memory life-cycle management, which balance the wear-level of flash memory ii cells through data migration, we focus on reducing the internal data movement overhead introduced by flash memory SSD firmware to mask the long latency of small random write. We found that the internal data movement overhead can be managed effectively by capacity over-provisioning based on the write request working-set size, and we show a theoretical analysis to illustrate the rela- tionship between the minimal over-provisioning capacity and the page allocation policy to achieve zero data movement overhead. Based on these findings, two SSD firmware page allocation al- gorithms are proposed to improve the capacity efficiency. Our trace-driven simulation using the Microsoft SSD extension of the well-known Disksim simulator shows that the proposed algorithm can significantly reduce the internal data movement overhead.
Opinion Mining and Analysis of Movie Reviews

Background/Objective: Customer reviews are important for various fields (e.g. Movies,Products, Services). Moviereviews plays vital role in describing its success and failure. People have now become very specific on what movies to watch and what not to watch. Hence people don't want to waste time on a movie that has bad reviews. Nowadaysonline reviews are important for personal recommendation. Methods/Statistical Analysis: There are various works done on opinion mining and text mining. This paper focuses on analyzing sentiment from semantic orientation of words that occur in a text by manually defining dictionary for positive, negative and intensifier words stored in different text file. In this method we extract data from three different sources. Opinions that has to be analyzed is preprocessed and stored in text file. The data are then compared with our bag of words in order to find the number of positive and negative sentiments in the reviews of that particular movie. To predict movie rating three machine learning algorithms have been used to create three different classifier using a trained data set. Findings: After rating prediction, the accuracy of each classifier is calculated on the data set containing predicted rating of various movies given by each classifier. Out of all three machine learning algorithms used, Naive Bayes is found to be more accurate than Decision Tree and K Nearest Neighbour algorithm. Improvements: There can be more many ways where we understand how the users write reviews as the reviews only support English language. Hence we can also bring in multiple languages, so that we do not even miss out on a single review. Most of the time,a single user may not provide review for all the movies. So, our database will resemble a sparse matrix. A prediction algorithm may be designed to mathematically guess the rating for movies that are not originally reviewed by the user.
ANALISIS PERANCANGAN HOTEL BTS PADA MASS RAPID TRANSPORT DI SURABAYA

Pertumbuhan pelanggan yang ada sekarang ini mengakibatkan perlunya pertambahan infrastruktur Base Transceiver Station (BTS) agar mendapatkan kinerja operator yang maksimal. Pada kota-kota besar banyak mengalami kendala masalah perizinan lahan sehingga tidak memungkinkan dibangun tower base station baru. Di lain pihak, operatoroperator telekomunikasi harus dapat melayani trafik dari pelanggan yang semakin banyak. Distributed Antenna System atau dikenal dengan Hotel BTS menawarkan teknologi base station yang dipusatkan pada satu lokasi yang kemudian akan melayani beberapa remote site. Pada tiap remote site tersebut akan dihubungkan langsung oleh sebuah single fiber untuk menjangkau area tersebut. Sehingga memungkinkan cakupan area base station dapat lebih luas dan menjangkau daerah yang terdapat halangan bangunan/gedung. Tujuan dari Tugas Akhir ini adalah untuk merancang Hotel BTS dengan teknologi LTE pada jalur Mass Rapid Transport (MRT) di Surabaya sebagai solusi dari perluasan area dan peningkatan pelayanan dengan melakukan perencanaan capacity planning, coverage planning dan jaringan serat optik. Hal ini dilakukan untuk mengestimasi jumlah eNode B dan luas cakupan dari tiap remote site. Pada perancangan Hotel BTS dengan teknologi LTE 1800 MHz yang telah dilakukan pada jalur tram didapatkan nilai OBQ 14,45 Mbps/Km2 dan kapasitas sel 17,78 Mbps. Luas cakupan sel sebesar 1,16 Km2, jumlah eNode B 24 buah, dan radius 668 m. Jaringan fiber optik yang dirancang memiliki 5 link dan telah memenuhi parameter margin daya diatas nol.
Forecasting of Volumes of Power Consumption in the Electrical Distribution Networks Based on Hybrid Models

The article deals with increasing the prediction accuracy for electricity consumption in electrical grids under conditions of the modern wholesale electricity and capacity market and actual performance of grid operators. It contains the analysis of currently applied electricity consumption forecast methods. The article shows the necessity of accounting unpredictable and hardly definable factors, such as technical and economic factors (production schedule, end-consumer’s equipment load characteristics, electric power sector support and development costs), as well as climate factors (environment temperature, luminance, light day length, natural disasters). The most accurate accounting of these factors provides an opportunity to increase supervisory control quality and reduce electricity losses by reducing balancing market share, as well as output costs of generating company (end consumer). It is proposed to establish the use of Mamdani algorithm-based fuzzy neural networks as a core principle. The results show that electricity consumption forecast, based on using fuzzy neural networks is more accurate than the method currently specified in power supplier regulations. The article also contains an expert assessment of the economic viability of using said method for power supply to large regional industrial plants.
Investment on wheat research and its effect: A case of Nepal

Abstract. Although the agriculture sector markedly contributes to the Nepalese economy, very little is known about the government’s investments in agricultural research activities and how these investments have impacted the sector. In this study, we picked a case of wheat crop as it is the third largest crop of Nepal in terms of total annual production. We took government’s annual wheat research investments of Nepal from Fiscal year 2005 to 2016 to analyze the effects of investment on wheat research based on the availability of data. We used compound growth rate, averages, trend line, and bar diagram to present the data and interpret the results. Results revealed that the share of operational budget was lower than supposed to be allocated for the development of wheat research. The pace of increment of wheat production and productivity were observed due to improved wheat investment and technologies. Result shows the negligible spillover effect from neighboring countries in wheat production. Even though the Nepal Agricultural Research Council (NARC) has fulfilled source seed target by producing a surplus, private sectors should involve proactively in coordination with NARC to meet the projected demand of wheat seed in national seed vision. The huge gap was observed between yield potential and average national productivity. To achieve greater impact of wheat research in Nepal, it is necessary to bridge the gap by awareness program, making the availability of improved seed with an improved package of practices.
Improved learning of basic mechanics by student active teaching methods

Background The teaching of the basic courses in mechanics at Lulea University of Technology is carried out as lessons. However, the tightening of the economic resources has resulted in larger classes and a considerable reduction of the number of lessons. With well over 30 students in the class most of the advantages with lessons are lost. The lessons tend towards formal lectures. The teaching loses in quality and will not be effective for the students' learning. Aim of the project Mechanics is a subject in which the theory is very close coupled to our everyday surroundings (e.g. vehicles, constructions, machines etc.). To be able to put the theory into practice a deep understanding is important. The main purpose of this project is therefore to get the student to take a larger responsibility for acquiring knowledge and under-standing, and also gain experiences of how to organise the learning and how to work in groups, i.e. an overall method for studying. A second aim is to make the teaching more effective in that the teacher will be involved mainly in those items which the students find difficult and need help with. Important is then to produce methods to find those items on which the teaching should be focused. • We expect to have a pedagogical method, which will impart deeper knowledge in mechanics to the students. • That the students acquire a method to organise their own studies and gain knowledge of working in group, i.e. a universal technique of studying that will be of great use further on. • A clear picture of what role the teacher will have in this teaching method and in what way the work of the teacher will change. • Clarity of what the responsibilities for the students are and what the students can manage themselves. 2001-12-29 Department of Mechanical Engineering Final report on the project Improved Learning of Basic Mechanics by Student-Active Teaching Methods Anders Wahlin Division of Experimental Mechanics Department of Mechanical Engineering Lulea University of Technology SE-971 87 Lulea Sweden Marie Finnstrom Division of Fluid Mechanics Department of Mechanical Engineering Lulea University of Technology SE-971 87 Lulea Sweden
Le digital storytelling comme mode d'expression citoyen

Cet article restitue les travaux de recherche-creation d'un atelier-laboratoire consacre au storytelling, organise par le programme IDEFI-CreaTIC et l'universite Paris 8, en partenariat avec l'universite d'Athenes et en collaboration avec la mairie d'Athenes. L'objectif est d'experimenter de nouvelles methodes d'expression civique, de penser l'exercice de la politique et du devoir citoyen dans un monde numerique, a travers la pratique originale du digital storytelling.
Eye-Gaze Estimation Systems for Multi-Applications: An Implementation of Approach Based on Laptop Webcam

Eye-gaze is the mirror of speech, the language of nonverbal communication. The research investigations in eye-gaze estimation systems may reveal what persons think about on the basis of where they are looking. Nowadays, the eye-gaze estimation systems represent a combination of appropriate hardware and related software for estimating the eye-gaze directions to provide new domains of applications. The purpose of this paper is to provide an inclusive expansion in the recently existing applications based on the eye-gaze estimation systems by considering different applications; driver assistance systems, Smartphone as an input mechanism, controlling smart homes/TV, virtual space and entertainment, medicine, human robot interaction, sports, security, and authentication. Additionally, an implementation of an accurate, reliable and a low cost eye-gaze estimation approach based on Laptop Webcam is presented. This paper contains the main preliminaries, up-to-date advantages, and disadvantages, in addition to the necessity of future expansion in the domain.
Decoupling Information and Connectivity in Information-Centric Networking

on page 2. Follow this and additional works at: https://openscholarship.wustl. edu/cse_research Part of the Computer Engineering Commons, and the Computer Sciences Commons Recommended Citation Ben Abraham, Hila; Parwatikar, Jyoti; Dehart, John; Drescher, Adam; and Crowley, Patrick, "Decoupling Information and Connectivity in Information-Centric Networking" Report Number: WUCSE-2017-001 (2017). All Computer Science and Engineering Research. https://openscholarship.wustl. edu/cse_research/1169 Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160. This technical report is available at Washington University Open Scholarship: https://openscholarship.wustl. edu/
Développement de la production en continu dans l'industrie pharmaceutique : étude bibliographique et expérimentale de la mise en forme de poudres par extrusion

Apres l’introduction de la Qualite par la Conception (« Quality by Design, QbD ») au debut des annees 2000, l’industrie pharmaceutique doit faire face a un nouveau defi : celui de l’emergence de la production en continu (« continuous manufacturing ») de formes orales solides. Si l’idee de produire des comprimes sur une ligne fonctionnant pleinement en continu remonte aux annees 1970, ce n’est qu’en 2016 que les premieres Autorisations de Mise sur le Marche (AMM) ont ete octroyees pour des medicaments concus selon ce mode de production. L’operation de granulation humide, souvent inherente a la production de comprimes, etait le principal obstacle a l’apparition du concept « De la poudre vers le comprime » (« From powder to tablet »). Les procedes d’extrusion, deja presents au sein de l’industrie pharmaceutique, ont permis de lever cette entrave. La granulation humide par extrusion est etudiee au travers de cette these d’exercice. Une analyse critique de la demarche experimentale appliquee ainsi que des resultats obtenus est proposee. Un plan d’experiences a mis en evidence la forte influence de la vitesse de rotation des vis et du ratio liquide/solide sur la Distribution des Temps de Sejour (DTS) au sein d’une extrudeuse. Par ailleurs, les zones de convoyage et de melange sont correctement modelisees par des cascades de reacteurs parfaitement agites avec volume mort. Les grains fabriques par extrusion presentent une distribution de taille et une forme tres differentes de ceux issus d’une production par un granulateur a fort taux de cisaillement. Des comprimesproduits a partir de grains fabriques par extrusion ont une contrainte de rupture diametralesignificativement superieure a celle de comprimes issus de grains obtenus par une operation de granulation a fort taux de cisaillement.
A Sinusoidal Modeling Method Based on Matching-Pursuits with Perceptual Gradient

As an adaptive algorithm of signal decomposition, matching pursuits provides a new framework for sinusoidal modeling of speech and audio signal. In this paper, the procedure of sinusoidal modeling using matching pursuits is analyzed as well as the sinusoidal modeling algorithm using perceptually weighted matching pursuits. And a method of sinusoidal modeling with perceptual gradient is proposed. The proposed method, which adopts the adaptive feature of matching pursuits, computes dynamically a masking threshold from the currently synthesized signal using the psychoacoustic model. With the threshold, it extracts the most perceptually significant component from the residual signal. Therefore, the perceptual information contained in the synthesized signal increases as quickly as possible. The quality of the synthesized speech by this approach is rather high even if the model precision is low. Experiments prove that the method in this paper uses the features of hearing system in a better way, and the modeling is reasonable and efficient. Both the objective compare of SNR and the subjective listening test show the rationality and superiority of the new method.
Perception based Object Recognition using SP Theory

Object recognition is a process of detecting the object present in an image or a video sequence, with the help of some recognition technique or methods. Object recognition is one of the techniques of digital image processing where we can process any image by applying some of the operation. This paper is about how the SP theory of intelligence and its realisation in the SP machine may help in the design of the ‘brains’ of autonomous robots which can detect and recognize objects based upon their perception, meaning robots that do not depend on external intelligence or power supplies, are mobile, and have human-like versatility and adaptability in intelligence Keywords— Sp theory, Object detection
Predicting Quality Of Experience For Online Video Systems Using Machine Learning

As the expansion of the online video broadcasting continues in every area of the modern connected world, the need for measuring and predicting the Quality of Experience for content delivery has never been this important. This demo paper has designed and developed a real-time and continuously trained machine learning model in order to predict QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to a cluster of users simultaneously while objective video metrics are collected into a database. At the end of each video, each user is queried with a subjective survey about their experience. Both quantitative statistics (video metrics) and qualitative information (user surveys) are used continuously as training data to machine learning model. The overall results show that proposed QoE estimation system provides an average Mean Opinion Score (MOS) precision with an error rate ranging from 12% to 15%. This methodology can efficiently answer the problem of predicting user experience for any online video delivery system, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative metrics.
A deep learning approach to the analysis of retinal images

This work is focused on the possible applications of Deep Learning in retinal fundus images analysis. Deep Learning is an advanced machine learning technique that is revolutionizing all data-based disciplines with unheard-of performances in signal analysis. In particular Artificial Intelligence, Computer Vision and Image Analysis are benefiting from huge improvement in image and sound classification, segmentation and pattern recognition
Prediction for pathological image with convolutional neural network

Background and Objectives: The diagnosis of cancer is concerned, and the prediction of cell carcinoma is of great importance for the treatment. Materials and Methods: First, we obtain a series of slices of tumor cell pathology in clinical data, with being followed training sets and test sets gained by adding data model. Then, we design a convolutional neural network training and prediction model. After that, we optimize parameters for training and prediction model, combining experience. Results: In experiment, the accuracy of the model predicting for cell carcinoma is 87.38%. Conclusions: This study provides a reference that predicts the extent of cell carcinoma progression by using deep learning model.
Enhanced Automatically Mining Facets for Queries and Clustering with Side Information Model

---In this paper describe a specific type of summaries that Query facet the main topic of given text. Existing summarization algorithms are classified into different categories in terms of their summary construction methods (abstractive or extractive), the number of sources for the summary (single document or multiple documents), types of information in the summary (indicative or informative), and the relationship between summary and query (generic or query-based). QD Miner aims to offer the possibility of finding the main points of multiple documents and thus save users’ time on reading whole documents. The difference is that most existing summarization systems dedicate themselves to generating summaries using sentences extracted from documents. In addition, return multiple groups of semantically related items, while they return a flat list of sentences. In this paper, adding these lists may improve both accuracy and recall of query facets. Part-of-speech information can be used to check the homogeneity of lists and improve the quality of query facets. The side-information could not be incorporate into the mining process, because it can either improve the quality of the representation for the mining process, or can add noise to the process. Therefore, a principle way is required to perform the mining process, so as to maximize the advantages from using this side information. This dissertation proposes an algorithm which combines classical partitioning algorithms with probabilistic models in order to create an effective clustering approach.
Password Entry Errors: Memory or Motor?

As we increasingly rely upon our computer information systems to store and operate on sensitive information, the methods we use to authenticate user identity also become more important. One of the most important such methods is the password. However, passwords that provide better security also tend to be more difficult to remember. They also tend to be difficult to type, and typing errors can have negative consequences such as being locked out of a critical information system. We present a computational cognitive model of password rehearsal and a typing extension to the ACT-R cognitive architecture intended to study humancomputer interaction issues in the usable security domain.
Factors Affecting Cyber Risk in Maritime

To ensure the safety of ships and ports, groups and individuals, at all levels of the maritime sector, use analysis to identify potential hazards and their outcomes. One of the most relied upon methods is using a risk assessment tool to define and prioritise threats. A disadvantage of most existing assessment frameworks, however, is their inability to update risks dynamically as factors, such as the environment, change. In the maritime sector, a range of dynamic factors is needed to measure risks, but most conventional frameworks are unable to use them to revise and update their risk profiles. In addition to static and dynamic, maritime operational risks can be affected by elements classified as cyber, cyber-physical, or physical in nature. This demonstrates the relatively equal presence of information and operational technology (i.e. IT/OT) used, however most quantitative risk assessment frameworks are normally limited to one or the other. This article explores the full range of cyber-related risk factor types within maritime in order to evaluate applicable risk frameworks and suggest improvements that could help each of those tools assess maritime-cyber risks specifically.