Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining user tasks from print logs

With lots of applications emerging in World Wide Web, many interaction data from users are collected and exploited to discover user behavior or interest patterns. In this paper, we attempt to exploit a new interaction data, namely print logs, where each record is printing URLs selected by a user using a popular web printing tool. Users usually print web contents based on an intention (subtask or task). Apparently, mining common print tasks from print logs is able to capture users' intentions, which undoubtedly benefits many web applications, such as task oriented recommendation and behavior targeting. However, it is not an easy job to perform this due to the difficulty of URL topic representation and task formulation. To this end, we propose a general framework, named UPT (Users Print Tasks mining framework), for mining print tasks from print logs. Specifically, we attempt to leverage delicious (a social book marking web service) as an external thesaurus to expand the expression of each URL by selecting tags associated with the domain of each URL. Then, we construct a tag co-occurrence graph where similar tags can be clustered as subtasks. If we view each subtask as an item, then the print log is transformed to a transaction database, on which an efficient pattern mining algorithm is proposed to induce tasks. Finally, we evaluate the effectiveness of the proposed framework through experiments on a real print log.
ID-based tripartite key agreement with signatures

This paper proposes a new identity based tripartite key agreement protocol which is more efficient than the existing ID-based tripartite protocol. This protocol is based on the Joux's protocol for key agreement, and introduces signature along with key agreement to overcome man-in-the-middle attacks and to provide authentication. The new protocol resists existential forgeries against adaptively chosen message attacks under the random oracle model.
On the verge of liberal arts education: The case of MISH in Poland

The following article analyses MISH college, a liberal education program at the University of Warsaw (UW). The article is based on a critique of existing research on the issue, and proposes an inductive approach to defining liberal education. Reconstructing the coherent idea behind MISH contributes to both understanding of European liberal education and related methodological debate. In 1993 MISH offered a small group of selected candidates an opportunity to develop cross-disciplinary, individual curricula and work with an academic tutor. The model spread to 9 Polish research universities, as well as 6 other institutions in Ukraine, Belarus and Russia. Despite subsequent creation of a separate degree-granting program in Kolegium Artes Liberales UW in 2008, MISH continues to operate as a self-identifying liberal education program. The Author claims that in the case of MISH, the aim (active, engaged and trusting academic community) and principles (critical thinking, communal thinking, and operational independence) are related to values (opposition, republicanism, autonomy). Organization of MISH complicates its classification, while its idea bears marks of a unique “variation on the theme” of liberal education in Europe. The article advocates for greater emphasis on diverse ideas of liberal education in the pluralist European higher education landscape.
Highly sensitive imaging of renal microcirculation in vivo using ultrahigh sensitive optical microangiography

Studying renal microcirculation and its dynamics is of great importance for understanding the renal function and further aiding the diagnosis, prevention and treatment of renal pathologies. In this paper, we present a potentially useful method to provide high-sensitive volumetric imaging of renal microcirculations using ultrahigh-sensitive optical microangiography (UHS-OMAG). The UHS-OMAG image system used here is based on spectral domain optical coherence tomography, which uses a broadband light source centered at 1300 nm with an imaging speed of 150 frames per second that requires ~6.7 sec to complete one 3D scan of ~2.5 × 2.5 mm2 area. The technique is sensitive enough to image capillary networks, such as peritubular capillaries within renal cortex. We show the ability of UHS-OMAG to provide depth-resolved volumetric images of capillary level renal microcirculation. We also show that UHS-OMAG is capable of monitoring the changes of renal microcirculation in response to renal ischemia and reperfusion. Finally, we attempt to show the capability of OMAG to provide quantitative analysis about velocity changes in a single capillary vessel (down to tens of microns per second) in response to the ischemic event.
Feature preserving consolidation for unorganized point clouds

We introduce a novel method for the consolidation of unorganized point clouds with noise, outliers, non-uniformities as well as sharp features. This method is feature preserving, in the sense that given an initial estimation of normal, it is able to recover the sharp features contained in the original geometric data which are usually contaminated during the acquisition. The key ingredient of our approach is a weighting term from normal space as an effective complement to the recently proposed consolidation techniques. Moreover, a normal mollification step is employed during the consolidation to get normal information respecting sharp features besides the position of each point. Experiments on both synthetic and real-world scanned models validate the ability of our approach in producing denoised, evenly distributed and feature preserving point clouds, which are preferred by most surface reconstruction methods.
Prediction of Stock Market in Nigeria Using Artificial Neural Network

Prediction of Nigerian stock market is almost not done by any researcher and is an important factor which can be used to determine the viability of Nigerian stock market. In this paper, the prediction models were developed using Artificial Neural Network. The result of the prediction of Nigerian Stock Exchange (NSE) market index value of selected banks using Artificial Neural Network was presented. The multi- layer feed forward neural network was used, so that each output unit is told what its desired response to input signals ought to be. This work has confirmed the fact that artificial neural network can be used to predict future stock prices. The data collection period is from 2003 to 2006.
Minimum common outage probability for the broadcast relay channel

In this paper the broadcast relay channel (BRC) is studied. In the BRC model, the source communicates with two destinations with the help of a single relay. The minimum common outage probability for four different transmission protocols, direct transmission (DT), multihop (MH), path selection (PS) and link combination with path selection (LCPS) is investigated under long-term power constraint for constant rate transmission. In addition, the ε-outage rate region for a fixed common outage probability is computed. Based on the cut-set bound (CSB), a lower bound on the minimum common outage probability and an upper bound on the ε-outage rate region are also found. Numerical results suggest that enforcing the relay to help both destinations simultaneously is limiting. The dominant factor in the gains obtained with respect to DT is due to path selection and link combination is not necessary when the relay is close to the source.
Automatic summarization of changes in image sequences using algorithmic information theory

An algorithmic information theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG), whose connected subgraphs are compared using an adaptive information distance measure, aided by a closed-form multi-dimensional quantization. The summary is the clustering result and feature subset that maximize the gap statistic. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. When applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain. When analyzing intra-cellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification. Finally, it was able to differentiate wild type from genetically modified thymocyte cells.
Scalable serializable snapshot isolation for multicore systems

Since 1990's, Snapshot Isolation (SI) has been widely studied, and it was successfully deployed in commercial and open-source database engines. Berenson et al. showed that data consistency can be violated under SI. Recently, a new class of Serializable SI algorithms (SSI) has been proposed to achieve serializable execution while still allowing concurrency between reads and updates.
Countering Reactance in Crisis Communication: Incorporating Positive Emotions via Social Media

Based on the psychological reactance theory, this study examined the effect of incorporating sympathy via social media on countering reactance in crisis communication. The study employed a 2 (expressing sympathy: yes vs. no) × 2 (medium: Twitter vs. news release) between-subject experimental design. Two hundred and fifty-three (N = 253) American consumers recruited from Amazon Mechanical Turk participated in this online experiment. Results indicated that using Twitter and expressing sympathy significantly lowered reactance. There was no interaction effect between the two factors, such that their influence on reactance was not contingent on one another. There was a partial yet significant mediation effect of reactance between medium and two outcomes (perceived crisis responsibility and organizational reputation). There was no mediation effect of reactance between sympathy expressed and two outcomes. The theoretical and managerial implications were discussed, as well as limitations and suggestions for future research.
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example

Estimating the strength of causal effects from observational data is a common problem in scientific research. A popular approach is based on exploiting observed conditional independences between variables. It is well-known that this approach relies on the assumption of faithfulness. In our opinion, a more important practical limitation of this approach is that it relies on the ability to distinguish independences from (arbitrarily weak) dependences. We present a simple analysis, based on purely algebraic and geometrical arguments, of how the estimation of the causal effect strength, based on conditional independence tests and background knowledge, can have an arbitrarily large error due to the uncontrollable type II error of a single conditional independence test. The scenario we are studying here is related to the LCD algorithm by Cooper [1] and to the instrumental variable setting that is popular in epidemiology and econometry. It is one of the simplest settings in which causal discovery and prediction methods based on conditional independences arrive at non-trivial conclusions, yet for which the lack of uniform consistency can result in arbitrarily large prediction errors.
Detecting motion using the Structure-Texture Image Decomposition and Space-Time Interest Points

Among all the features which can be extracted from videos, we propose to use Space-Time Interest Points (STIP), these ones are particularly interesting because they are simple and robust. They allow a good characterization of a set of regions of interest corresponding to moving objects in a three-dimensional observed scene. In this paper, we propose to apply the algorithm of the detection of spatiotemporal interest points for a good detection of moving objects on both components of the decomposition: a geometric structure component and a texture component. Proposed results are obtained from very different types of videos, namely sport videos and animation movies.
Implementing hardware for new genetic algorithms

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don’t this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.
The Current Awareness of Just-In-Time Techniques within the Libyan Textile Private Industry: A Case Study

Almost all Libyan industries (both private and public) have struggled with many difficulties during the past three decades due to many problems. These problems have created a strongly negative impact on the productivity and utilization of many companies within Libya. This paper studies the current awareness and implementation levels of Just-In-Time (JIT) within the Libyan Textile private industry. A survey has been applied in this study using an intensive detailed questionnaire. Based on the analysis of the survey responses, the results show that the management body within the surveyed companies has a modest strategy towards most of the areas that are considered as being very crucial in any successful implementation of JIT. The results also show a variation within the implementation levels of the JIT elements as these varies between Low and Acceptable levels. The paper has also identified limitations within the investigated areas within this industry, and has pointed to areas where senior managers within the Libyan textile industry should take immediate actions in order to achieve effective implementation of JIT within their companies. Keywords—Industry, Questionnaire, JIT, Textile I. INTRODUCTION IBYA is a large African country, bounded in the North by the Mediterranean Sea, in the East by Egypt and Sudan, in the South by Niger and Chad, and in the West by Algeria and Tunisia. It is mainly petroleum producing and exporting country. The economy within Libya was formerly based on agriculture, producing fruit, barely, olives, and dates (1). Libya was relatively poor until the discovery of Oil and Gas in the very beginning of the sixties of the last century. Since then, the country has turned to industrialisation by engaging in petroleum processing as well as some other industries such as Cement and Textile industries. Libya is committed to develop enhanced abilities to produce goods that meet the quality requirements of present markets with all the possible products and services, which could be achieved by providing the most proper and highest technology available (1), (2). Very little research has been carried out over the recent years in the area of textile industry. However, a little of the published work was in the area of modelling and simulation of the manufacturing operations rather than studying the strategies and implementation status within this industry.
Comparing text entry methods for interactive television applications

We evaluate and compare the user acceptance of alternative text input methods for an interactive TV application. We are interested in whether remote control-based input methods are acceptable alternatives to the standard keyboard, when the user is assisted with intelligent text entry methods when entering a search term. The users are asked to search for music videos by entering text using a remote control or a standard QWERTY keyboard. The text input methods are evaluated with the HED/UT questionnaire. The results show that the standard keyboard with intelligence is better rated in these dimensions than the methods using the remote control. However, there is no negative attitude towards the methods using the remote control when using intelligent text input.
Sensor management for multistatics

This paper focuses on sensor management for distributed sensor fields in deep water. This paper will investigate the sensor placement problem for the barrier scenario. Field performance is often based solely on sensor coverage or probability of detection evaluations, which provide only a fleeting snapshot of how the field is performing at a particular time. This paper utilizes an efficient time dependent approach to evaluating the performance of distributed sensor fields. Given a priori target probability distributions and sensor measurements distributed in time and space, the approach directly scores entire sensor fields based on their ability to detect threats over time. This formulation is used as an optimization metric for sensor management.
Identifying Semitic Roots: Machine Learning with Linguistic Constraints

Words in Semitic languages are formed by combining two morphemes: a root and a pattern. The root consists of consonants only, by default three, and the pattern is a combination of vowels and consonants, with non-consecutive slots into which the root consonants are inserted. Identifying the root of a given word is an important task, considered to be an essential part of the morphological analysis of Semitic languages, and information on roots is important for linguistics research as well as for practical applications. We present a machine learning approach, augmented by limited linguistic knowledge, to the problem of identifying the roots of Semitic words. Although programs exist which can extract the root of words in Arabic and Hebrew, they are all dependent on labor-intensive construction of large-scale lexicons which are components of full-scale morphological analyzers. The advantage of our method is an automation of this process, avoiding the bottleneck of having to laboriously list the root and pattern of each lexeme in the language. To the best of our knowledge, this is the first application of machine learning to this problem, and one of the few attempts to directly address non-concatenative morphology using machine learning. More generally, our results shed light on the problem of combining classifiers under (linguistically motivated) constraints.
Communicating pictures: The test of future wireless networks

Before wireless multimedia applications can be truly successful, a number of technologically demanding problems must still be resolved. For example, future services will need to be delivered at a range of bit rates, dependent on network and channel characteristics, platform type, application and material content. End to end provision of multimedia information across network components of varying capability and performance will therefore impose the need for coding regimes which are inherently robust to different forms of loss and error. This paper addresses one of the most challenging coding problems- associated with the delivery of real time video over wireless communication networks. Specific examples will be based on H.264 transport over the 802.11 family of wireless LAN standards. The lecture will also describe how aspects of wireless video research in the University of Bristol have been successfully extracted from the laboratory and commercially exploited through a spin-out company, Provision Communications.
EFFICIENCY OF MAGNETIC TO KINETIC ENERGY CONVERSION IN A MONOPOLE MAGNETOSPHERE

Unconfined relativistic outflows from rotating, magnetized compact objects are often well modeled by assuming that the field geometry is approximately a split-monopole at large radii. Earlier work has indicated that such an unconfined flow has an inefficient conversion of magnetic energy to kinetic energy. This has led to the conclusion that ideal magnetohydrodynamical (MHD) processes fail to explain observations of, e.g., the Crab pulsar wind at large radii where energy conversion appears efficient. In addition, as a model for astrophysical jets, the monopole field geometry has been abandoned in favor of externally confined jets since the latter appeared to be generically more efficient jet accelerators. We perform time-dependent axisymmetric relativistic MHD simulations in order to find steady-state solutions for a wind from a compact object endowed with a monopole field geometry. Our simulations follow the outflow for 10 orders of magnitude in distance from the compact object, which is large enough to study both the initial “acceleration zone” of the magnetized wind as well as the asymptotic “coasting zone. ” We obtain the surprising result that acceleration is actually efficient in the polar region, which develops a jet despite not being confined by an external medium. Our models contain jets that have sufficient energy to account for moderately energetic long and short gamma-ray burst (GRB) events (∼1051–1052 erg), collimate into narrow opening angles (opening half-angle θj ≈ 0.03 rad), become matter-dominated at large radii (electromagnetic energy flux per unit matter energy flux σ < 1), and move at ultrarelativistic Lorentz factors (γj ∼ 200 for our fiducial model). The simulated jets have γjθj ∼ 5–15, so they are in principle capable of generating “achromatic jet breaks” in GRB afterglow light curves. By defining a “causality surface” beyond which the jet cannot communicate with a generalized “magnetic nozzle” near the axis of rotation, we obtain approximate analytical solutions for the Lorentz factor that fit the numerical solutions well. This allows us to extend our results to monopole wind models with arbitrary magnetization. Overall, our results demonstrate that the production of ultrarelativistic jets is a more robust process than previously thought.
Dynamic Hand Gesture Recognition With Leap Motion Controller

Dynamic hand gesture recognition is a crucial but challenging task in the pattern recognition and computer vision communities. In this paper, we propose a novel feature vector which is suitable for representing dynamic hand gestures, and presents a satisfactory solution to recognizing dynamic hand gestures with a Leap Motion controller (LMC) only. These have not been reported in other papers. The feature vector with depth information is computed and fed into the Hidden Conditional Neural Field (HCNF) classifier to recognize dynamic hand gestures. The systematic framework of the proposed method includes two main steps: feature extraction and classification with the HCNF classifier. The proposed method is evaluated on two dynamic hand gesture datasets with frames acquired with a LMC. The recognition accuracy is 89.5% for the LeapMotion-Gesture3D dataset and 95.0% for the Handicraft-Gesture dataset. Experimental results show that the proposed method is suitable for certain dynamic hand gesture recognition tasks.