Miracle-dz/UniFAR-FTUs huggingface dataset:
Making Personalised Flight Recommendations using Implicit Feedback

As e-commerce has become more popular, the problem of information overload has come to the fore. Recommender systems that reduce the information overload problem are becoming more common. However, the problem with many recommender systems is that they are associated with a high cost of learning customer preferences (in terms of cognitive load). We describe the Personal Travel Assistant (PTA), a flight recommender application that uses case-based reasoning (CBR) to overcome these problems. The PTA allows users to search multiple flights providers concurrently and recommends flights based on their individual travel preferences. These preferences are implicitly learned from observations of user behaviour. When the user purchases a flight, the PTA uses the selection of a preferred flight to discover and refine the user’s overall travel preferences. These preferences are stored in a user-model as sets of cases representing their interactions, which are used to provide personalised recommendations. The PTA makes recommendations taking into account the context in which the flights were offered. It uses features from the request to determine this context, e.g. the duration of the trip. We perform evaluations of contextual recommendations that support our view that user preferences change depending on the context of the session. We further improve recommendation accuracy by storing and personalising similarity measures in the user-model. The PTA alters the relative importance of features in the personal similarity measure based on implicit user feedback, e.g. increasing the importance of price at the cost of stop-over time in a multiple hop flight. We also investigate cooperative components to extend our recommendation strategies. These allow users to reuse the information learned from other users when they encounter new situations. However, these techniques are not as successful as we had hoped. We discuss these components in relation to other work on collaborative recommendation and suggest that the standard approach is unsuited to the PTA’s context-based recommendation strategy. The strength of CBR in the e-commerce domain stems from its reuse of the knowledge base associated with a particular application. Since case data may be one aspect of a company’s entire knowledge system, it is important to integrate case data easily within a company’s IT infrastructure, providing in effect a case-based view on relevant portions of the company knowledge base. We describe CBML, an XML-based Case Mark-Up Language we have developed to facilitate
MSTperl parser (2015-05-19)

MSTperl is a Perl reimplementation of the MST parser of Ryan McDonald (http://www.seas.upenn.edu/~strctlrn/MSTParser/MSTParser.html). MST parser (Maximum Spanning Tree parser) is a state-of-the-art natural language dependency parser -- a tool that takes a sentence and returns its dependency tree. In MSTperl, only some functionality was implemented; the limitations include the following: the parser is a non-projective one, curently with no possibility of enforcing the requirement of projectivity of the parse trees; only first-order features are supported, i.e. no second-order or third-order features are possible; the implementation of MIRA is that of a single-best MIRA, with a closed-form update instead of using quadratic programming. On the other hand, the parser supports several advanced features: parallel features, i.e. enriching the parser input with word-aligned sentence in other language; adding large-scale information, i.e. the feature set enriched with features corresponding to pointwise mutual information of word pairs in a large corpus (CzEng); weighted/unweighted parser model interpolation; combination of several instances of the MSTperl parser (through MST algorithm); combination of several existing parses from any parsers (through MST algorithm). The MSTperl parser is tuned for parsing Czech. Trained models are available for Czech, English and German. We can train the parser for other languages on demand, or you can train it yourself -- the guidelines are part of the documentation. The parser, together with detailed documentation, is avalable on CPAN (http://search.cpan.org/~rur/Treex-Parser-MSTperl/).
Level of detail control of a virtual dental training system

This paper presents a level of detail control method for a virtual dental training system, the input of which is a triangulated surface model without the property of continuity commonly satisfied in other methods. This method uses relative curvatures of vertices as the criteria for multiresolution modeling. The primary strategy of this method is to produce tooth models with needed level of detail by vertex decimation. After illustrating our motivation and consideration to develop this method, the paper introduces the policy for vertex decimation and the computation method of relative curvature at first. Then the level of detail control method is presented in detail. And we adopt both maximum and average error metrics to measure the geometric fidelity between the original model and approximations. Finally, some sampled results are illustrated and analyzed in depth. The experiments demonstrate that the method can provide good level of detail control for the tooth models with reasonable time cost and geometric deviations.
Evolution In Changing Environments Some Theoretical Explorations

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Energy-Aware Virtual Machine Allocations using Bin Packing Algorithms in Cloud Data Centers

—Due to the high requirement of cloud services, the data centers are kept running all the time. This eventually increases energy consumption. In addition to this, high energy consumption leads to costlier business. In this work, for reducing energy consumption, energy-aware policy is introduced which performs virtual machine allocation through various bin-packing algorithms such as best-fit-decreasing and best-fit-unordered. Migration of virtual machines among physical machines is applied as basic technique for reducing the energy consumption which minimizes the number of running physical machines to optimize the required energy.
Effects of Anion Impurities on the Solubility and Nucleation of Borax Decahydrate Crystals in Carbonate-Type Brine

Measurements of the solubility and metastable zone width (MSZW) of borax decahydrate (disodium tetraborate decahydrate) within chloride, sulfate and carbonate solutions were carried out. The MSZW was measured by the conventional polythermal method with turbidity technology. The results showed that the addition of impurities decreased solubility. The main factor reducing solubility is the common ion effect, but the salt effect is also significant in the complex system. The addition of impurities broadened the MSZW. The MSZW was wider at first, then decreased with the addition of NaCl, yet the MSZW widened with the addition of Na2SO4 within the concentration range studied. This can be explained by adsorption and diffusion. The data of solubility and MSZW were analyzed by applying the Van’t Hoff equation and the classical 3D nucleation theory approach, respectively. By calculating the corresponding parameters, the experimental phenomena are further explained from the perspective of thermodynamics and dynamics.
Classifying Remote Sensing Image Based on BP Neural Network Technology

This paper focused on BP neural networks which was notable in the classification of remote sensing image processing application, and researched BP neural network classification algorithm in Matlab software platform. Finally, the classification was compared with supervised classification results under ERDAS software platform. The results show that BP neural network-based remote sensing image classification is more precise than the supervision of the general classification which under the total ERDAS software platform. It is an effective method of remote sensing image classification.
Research on the Top-down Visual Attention Method with Unknown Interference Target

Visual attention can prior to allocate the limited processing resources to several salient regions of the image, which can increase the efficiency of the relevant intelligence applications. Based on differences between task features and image background features, key features of task target can be reasoned, feature channels of visual attention are filtered top-bottom. The top-down visual attention method with unknown interference target is proposed, in order to ensure the hit rate of target as far as possible to shorten the generation time of visual attention saliency map, to improve the computing efficiency of visual attention
Predicting the Semantic Related words based on Hidden Markov Model

This paper presents a method of predicting the words with semantic relation based on Hidden Markov Model (HMM). Two words are set as an observation sequence, combined with HMM and the corpus, which has taken some works in Natural Language Processing, to calculate the most probable sequence with semantic relation by the given observation sequence. By Reducing the impact of high frequency words on the traditional method of semantic prediction based on the Text-window Co-occurrence. The experiment results show that this method is effective.
New Otsu threshold improvement method for detecting small infrared targets

In view of poor gray scale contrast of small IR targets and the requirement of real-time detection, combining with the behavior of small IR target image, a new Otsu threshold improvement method for detecting small IR targets is proposed. The method not only inherits the advantages of simplicity and fast calculation speed, but also solves the adjoining conglutination problems of multiple small IR targets when segmenting image with non-uniformity illumination, thus detecting small IR targets clearly. The experimental results show the accuracy of detecting small IR targets with the new Ostu threshold improvement method is 20% better than that of the old Otsu threshold detection method.
ARSDA: A new approach for storing, transmitting and analyzing high-throughput sequencing data

Two major stumbling blocks exist in high-throughput sequencing (HTS) data analysis. The first is the sheer file size typically in gigabytes when uncompressed, causing problems in storage, transmission and analysis. However, these files do not need to be so large and can be reduced without loss of information. Each HTS file, either in compressed . SRA or plain text . fastq format, contains numerous identical reads stored as separate entries. For example, among 44603541 forward reads in the SRR4011234.sra file (from a Bacillus subtilis transcriptomic study) deposited at NCBI’s SRA database, one read has 497027 identical copies. Instead of storing them as separate entries, one can and should store them as a single entry with the SeqID_NumCopy format (which I dub as FASTA+ format). The second is the proper allocation reads that map equally well to paralogous genes. I illustrate in detail a new method for such allocation. I have developed ARSDA software that implement these new approaches. A number of HTS files for model species are in the process of being processed and deposited at http://coevol.rdc. uottawa.ca to demonstrate that this approach not only saves a huge amount of storage space and transmission bandwidth, but also dramatically reduces time in downstream data analysis. Instead of matching the 497027 identical reads separately against the Bacillus subtilis genome, one only needs to match it once. ARSDA includes functions to take advantage of HTS data in the new sequence format for downstream data analysis such as gene expression characterization. ARSDA can be run on Windows, Linux and Macintosh computers and is freely available at http://dambe.bio. uottawa.ca/ARSDA/ARSDA.aspx.
Microsoft Win32 Developer's Reference Library

From the Publisher: Microsoft Win32 Developer's Reference Library is the life raft that can help developers chart a clear course on the sea of reference materials that support Microsoft Windows technologies. This well-conceived, completely indexed, value-added series of books organizes the vast ocean of Windows technical information sensibly, maintains consistency with electronic versions, and leverages the author's DDK publishing experience. Concise versions of the most commonly used Windowsr reference materials, synched with MSDNT and platform and tool releases, at a fraction of the cost of an MSDN subscription Provides a usefully abridged set of critical reference information for Windows programmers Helps developers quickly get the most out of programming in the Windows environment with intelligently indexed, well-presented, concise versions of key reference materials Provides guidance, tips, and tricks about how best to use Microsoft's electronic reference material, such as insider tips on how to use MSDN and its capabilities Five volume set includes: Base Services, User Interface, GDI, Common Controls, and Windows Shell
Image registration algorithm based on extraction of feature point and edge

A new algorithm about image registration is presented,which has combined feature point extraction and edge extraction,and has acquired main information of images.A relationship of point-to-point mapping is established by means of stereo vision and a im-proved binary-tree search tactics,so that the accurate data in 3-D space based on space several equations is gotten,then the data of other point is calculated.The efficiency and accuracy are improved obviously.
Mastering Linux

From the Publisher: Join the Linux Revolution! Newly revised and updated, Mastering Linux is the best all-in-one Linux resource available anywhere. Inside, you'll find everything you need to know to install, configure, use, and optimize this increasingly popular and respected operating system. You'll even find the operating system itself-the Publisher's Edition of Red Hat Linux 7, absolutely free on the enclosed CD! Whatever you want to achieve with Linux, Mastering Linux makes it easy, providing the clear instruction and unique insights of a Linux expert who understands the needs of the non-Unix world. Coverage includes: * Installing Linux * Configuring and using the GNOME and KDE graphical user interfaces * Configuring and using X-Windows v. 4 * Using Linux commands * Managing files in Linux * Connecting to the Internet and e-mailing with Linux * Using Netscape Communicator 6 for Linux * Faxing from Linux * Using Linux on a LAN * Using Linux as part of a Windows network * Connecting and configuring USB peripherals * Creating a Linux Web server * Setting up and using a Linux mail server * Running DOS and Windows applications using Wine * Configuring your Linux system with LinuxConf * Re-compiling the Linux kernel CD Description On the enclosed CD, you'll find the Publisher's Edition of Red Hat Linux 7, the world's most popular Linux distribution. This is a non-expiring version that you can install and use for as long as you like.
Sentiment Analysis Publicly available datasets

Sentiment analysis includes computational techniques to understand opinions and sentiment in texts. Several studies have explored supervised and unsupervised methods for sentiment classification covering natural language processing techniques, information retrieval and lexical resources. Training and testing data are appropriate from documents since each review has already labeled. This paper describes labeled datasets publicly available for sentiment classification problem. Keywords— sentiment analysis, dataset, public, online
Integrating Non-Mathematical Domains into Mathematical Development: Key Factors to Consider in Constructing Effective Interventions.

• Early Mathematical knowledge is a critical basis for developing more advanced knowledge (De Smedt et al., 2009). • Individual mathematics skills and domains build on one another (Purpura et al., 2013). • A range of non-mathematical factors (e.g., working memory and language) have been shown to be important for broad mathematics development (Purpura et al., 2011; Raghubar et al., 2010). • Working memory and language may be differentially related to specific mathematical skills, thus identifying whether these two domains have specific or general relations to specific early mathematical skills and concepts will provide a foundation by which to base the construction of interventions targeting mathematical growth.
AN INFORMATIVE PIPELINING WITH SCHEDULING REGULATOR TO SUPPORT RECOVERY

Within this work, we apply Razor to hardware accelerators that find growing application in System-on-Nick designs rich in-performance needs that must definitely be delivered under stringent power budgets. We exploit these traits usual for DSP and image-processing accelerators to apply Razor recovery in manner that's amenable to RTL validation and verification. We describe the implementation and plastic measurement is a result of a Razor-based hardware loop-accelerator (RZLA), applying the Sobel edge-recognition formula. Unlike microprocessors, the RZLA pipeline is data path-dominated with statically-scheduled control which has queue-based storage structures that are simply extended to aid check-pointing and recovery. The RFF is deployed along with an amount-sensitive latch-insertion based formula to deal with the minimum-delay constraint contained in all Razor systems. This formula enables using the time period for timing speculation resulting in robust error recognition and correction across a large dynamic current- and frequency-scaling range.
Effect of Size on the Saturable Absorption and Reverse Saturable Absorption in Silver Nanoparticle and Ultrafast Dynamics at 400 nm

Saturable absorption and reverse saturable absorption play an important role in the studies of the nonlinear optical properties of nanoparticles at resonant excitation. With this viewpoint, nonlinear absorption processes of chemically prepared silver nanoparticles in deionized water were studied using femtosecond laser pulses at 400 nm. Our nonlinear absorption study shows that there is competition between saturable absorption and two-photon absorption in prepared Ag NPs which depends on the size of the nanoparticles. We have also studied the ultrafast dynamics associated with nanoparticles which also results in the direct correlation between the ultrafast timescale and the size of the nanoparticle. The excitation of Ag NPs at 400 nm has shown the manifestation of damped oscillation which is attributed to the radial breathing mode oscillation due to acoustic vibration.
An Extractive Technique for Automatic Text Summarization Using

Text summarization is the method of compressing the source document into a shorter version that conveys the important information of original document and its overall meaning is preserved. It is not easy for human beings to manually summarize large documents of text. In this paper, we are proposing a domain independent and single document technique for text summarization which is using "Extended Boolean model" of Information Retrieval.
Exploring Deep Reinforcement Learning with Multi Q-Learning

Q-learning is a popular temporal-difference reinforcement learning algorithm which often explicitly stores state values using lookup tables. This implementation has been proven to converge to the optimal solution, but it is often beneficial to use a function-approximation system, such as deep neural networks, to estimate state values. It has been previously observed that Q-learning can be unstable when using value function approximation or when operating in a stochastic environment. This instability can adversely affect the algorithm’s ability to maximize its returns. In this paper, we present a new algorithm called Multi Q-learning to attempt to overcome the instability seen in Q-learning. We test our algorithm on a 4 × 4 grid-world with different stochastic reward functions using various deep neural networks and convolutional networks. Our results show that in most cases, Multi Q-learning outperforms Q-learning, achieving average returns up to 2.5 times higher than Q-learning and having a standard deviation of state values as low as 0.58.
Analysis of Influence of Datalink Time Delay on Beyond Visual Range Air Combat

Aiming at the problems in influence evaluation of beyond visual range air combat models,the process of it supported by datalink is analyzed.The influence of datalink on beyond visual range air combat is researched.On basis of this,the first shot exponential model and probability model are modified.The influences of time delay on first shot probability and kill rate in beyond visual range air combat are also studied.Experimental results show that because of the larger influence of time delay,it should be processed during the combat period.
Nickel‐catalyzed coupling of CO2 and amines: improved synthesis of carbamates

The electrochemical incorporation of carbon dioxide into amines catalyzed by an electrogenerated Ni complex afforded carbamates in moderate yields under very mild conditions (p = 1 atm, room temperature) without any addition of probases. Mechanistic and electrochemical studies revealed the role of reduced nickel species in the activation of CO2 and electrogenerated CO as a base in the synthesis of carbamates. The influence of number of faradays per mole of amine supplied to the electrodes (Q), cathode materials, temperatures, supporting salts and amounts of catalyst was studied to optimize the electrolytic conditions. A plausible reaction mechanism for the reduction of CO2 was proposed. Copyright © 2007 John Wiley & Sons, Ltd.
A Distributed Video Encryption Method Based on Spark

A large amount of data needs to be processed in the video encryption, and how to encrypt video efficiently is becoming a big challenge. In this paper, a distributed video encryption method based on Apache Spark framework is proposed. Videos are cut into smaller video units, and video units are distributed to server node for unit encryption. Hadoop MapReduce and Spark both are popular open-source platforms for large-scale data processing. So the comparison of efficiency between Map-Reduce and Spark for encryption processing is demonstrated. The method improves the execution efficiency of video encryption significantly.
ON FPGA BASED ACCELERATION OF IMAGE PROCESSING IN MOBILE ROBOTICS

In visual navigation tasks, a lack of the computational resources is one of the main limitations of micro robotic platforms to be deployed in autonomous missions. It is because the most of nowadays techniques of visual navigation relies on a detection of salient points that is computationally very demanding. In this paper, an FPGA assisted acceleration of image processing is considered to overcome limitations of computational resources available on-board and to enable high processing speeds while it may lower the power consumption of the system. The paper reports on performance evaluation of the CPU–based and FPGA–based implementations of a visual teach-and-repeat navigation system based on detection and tracking of the FAST image salient points. The results indicate that even a computationally efficient FAST algorithm can benefit from a parallel (low–cost) FPGA–based implementation that has a competitive processing time but more importantly it is a more power efficient.