Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cold-Start Recommendation Algorithm Based on New User's Implicit Information and Multi-attribute Rating Matrix

Traditional collaborative filtering recommendation algorithms face the cold-start problem. A collaborative filtering recommendation algorithm based on the implicit information of the new users and multi-attribute rating matrix is proposed to solve the problem. The implicit information of the new users is collected as the first-hand interest information. It is combined with other rating information to create a User-Item Rating Matrix (UIRM). Singular Value Decomposition is used to reduce the dimensionality of the UIRM, resulting in the initial neighbor set for target users and a new user-item rating matrix. The user ratings are mapped to the relevant item attributes and the user attributes respectively to generate a User-Item Attribute Rating Matrix and a User Attribute-Item Attribute Rating Matrix (UAIARM). The attributes of new items and UAIARM are matched to find the N users with the highest match degrees as the target of the new items. The attributes of the new users are matched with UAIARM to find the N items with the highest match degrees as the recommended items. Experiment results validate the feasibility of the algorithm.
Challenges of the Anatomy and Diffusion Tensor Tractography of the Meyer Loop

SUMMARY: This review addresses the complex and often controversial anatomy of the anterior bundle of the OR, also known as the Meyer loop. Before the advent of MR imaging, 2 main types of studies attempted to ascertain the “safe” distance for anterior temporal lobe resection to avoid postsurgical VFDs. There were those based first on postoperative VFD correlation and second on anatomic dissection studies. In the past decade, noninvasive diffusion MR imaging−based tractography techniques have been developed in an attempt to elucidate white matter connectivity. Although many of these techniques are still experimental, there are some clinical situations for which they may prove to be very helpful if properly performed and validated. The motivation for this review was to improve the outcome of patients with TLE undergoing temporal lobectomy: Would having anatomic information about the OR available to the neurosurgeon decrease the risk of postsurgical VFDs?
Moving Object Detection for Active Camera based on Optical Flow Distortion

The paper describes a novel method for moving object detection for an active camera using optical flows. In the images captured with an active camera, all of static and moving objects in the workspace are moving so that the camera moves around. In our approach, the analysis of optical flows is adopted for moving objects detection. The optical flow is resolved with using image information and camera motion in our method. The camera motion is controlled with robust controller, so that the information from the camera motion contributes well precise optical flow generation. The optical flows on moving objects are distorted to differ from the theoretical one because of the object motion. Since the precise optical flow is obtained in our method, the distortions are extracted, so that the moving object is detected. The validity of our method is confirmed in several physical experiments, and then good performance is achieved. The limitations of ability and performance of our method are also examined in the experiments.
MTForest: Ensemble Decision Trees based on Multi-Task Learning

Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noise-free data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we propose a new method namely MTForest, to ensemble decision tree learning algorihms by enumerating each input attribute as extra task to introduce different additional inductive bias to generate diverse yet accurate component decision tree learning algorithms in the ensemble. The experimental results show that in situations without classification noise, MTForest is comparable to Boosting and Random Forest and significantly better than Bagging, while in situations with classification noise, MTForest is significantly better than Boosting and Random Forest and is slightly better than Bagging. So MTForest is a good choice for ensemble decision tree learning algorithms in situations with or without noise. We conduct the experiments on the basis of 36 widely used UCI data sets that cover a wide range of domains and data characteristics and run all the algorithms within the Weka platform.
AutoIncSFA and vision-based developmental learning for humanoid robots

Humanoids have to deal with novel, unsupervised high-dimensional visual input streams. Our new method AutoIncSFA learns to compactly represent such complex sensory input sequences by very few meaningful features corresponding to high-level spatio-temporal abstractions, such as: a person is approaching me, or: an object was toppled. We explain the advantages of AutoIncSFA over previous related methods, and show that the compact codes greatly facilitate the task of a reinforcement learner driving the humanoid to actively explore its world like a playing baby, maximizing intrinsic curiosity reward signals for reaching states corresponding to previously unpredicted AutoIncSFA features.
The WAC Glossary Project: Facilitating Conversations between Composition and WID Faculty in a Unified Writing Curriculum.

: The Writing Across the Curriculum Program at Appalachian State University, founded in 2008, supports faculty instruction in a vertical writing curriculum which requires two courses in Composition and two in the disciplines, ensuring that students take a dedicated writing course in each year of undergraduate education. To address the challenges of student transfer of rhetorical knowledge and writing skills from one course to another, the WAC Program created opportunities for faculty conversation about writing in Composition and the disciplines. At the center of this conversation is the WAC Glossary Project, in which WAC consultant Dennis J. Bohr compiled pedagogical terms from faculty across the university for WAC conversations. The Glossary allows Composition faculty to anticipate writing tasks for students in the disciplines and encourages WID faculty to refer to basic, familiar terms in new writing contexts. The authors discuss the creation and evolution of the project and its practical applications.
An Analysis of the Social Process in Ideas Generation for Public Policies

Governments are moving toward developing crowdsourcing communities that facilitate ideas for public policies from a large "crowd. " Although this concept is promising, little is known about individuals' actions and behaviors when generating ideas within online communities in a public policy consultation setting. Previous research has shown that online crowdsourcing can generate innovation because of the interactive and diverse nature of the Internet. Building on existing theories and empirical findings, we suggest that the likelihood of proposing valuable ideas within an online community is positively correlated with boundary-spanning activities and feedback received. The Open Government Dialogue, which is an early consultation platform for the open government initiative, is used as an empirical case in which contributors conducted boundary-spanning activities and posted ideas that the government found valuable to include in the policy agenda. However, the amount of feedback and attention received from other members of the community do not show significant effects on the likelihood that a contributor proposes valuable ideas. Furthermore, for repeat contributors, the likelihood of proposing valuable ideas to the government consultation is positively related to prior success but negatively related to the number of ideas posted. Such findings provide implications for public administrators to understand how to design a public consultation platform that encourages contributors to generate usable and valuable ideas and avoids exploitation from people who seek to use the platform for personal gain.
A preliminary meta-analysis of SME ebusiness journal publications: current trends and future research opportunities moving beyond adoption factor studies

Previous meta-analyses of SME-eBusiness journal research focuses on analysing adoption factors, pre-2000 articles and a small number of journals. This paper departs from this research by analysing 100 articles published between 2003 and 2006 in 41 journals on the basis of the research approaches employed, countries and eBusiness technologies studied, and research objectives focused upon. The paper presents preliminary insights into current major research trends based on this analysis, such as the predominant focus on adoption factor by many studies. It also identifies future research opportunities, and proposes a research agenda which aims to progress SME-eBusiness research beyond adoption factor studies by outlining research objectives to help SMEs overcome barriers and exploit drivers.
1 Proactive Fault-Recovery in Distributed Systems

Supporting both real-time and fault-tolerance properties in systems is challenging because real-time systems require predictable end-to-end schedules and bounded temporal behavior in order to meet task deadlines. However, system failures, which are typically unanticipated events, can disrupt the predefined real-time schedule and result in missed task deadlines. Such disruptions to the real-time schedule are aggravated in asynchronous distributed systems by two main factors: first, delays in failure detection, and second , increased latencies due to the reactive fault-recovery routines that are set into motion once a failure is detected. In this thesis, we present a general framework for proactive (rather than the classical reactive) fault-recovery that reduces the latencies incurred by the fault-recovery routines. Proactive fault-recovery is a technique that exploits fault prediction mechanisms in order to compensate for failures even before they occur, thereby providing bounded temporal behavior in real-time and fault-tolerant systems for certain classes of faults. In our framework, we also show how to exploit knowledge of the underlying system topology to apply the benefits of proactive fault-recovery to multi-tiered distributed systems. We evaluate the impact of the design choices we faced when implementing a prototype of this framework in a distributed CORBA application. Our preliminary results show a promising 76% reduction in the worst-case fault-recovery latencies in our application. This demonstrates that proactive fault-recovery can indeed provide bounded temporal behavior in the presence of certain kinds of faults, thereby facilitating the development of real-time, fault-tolerant distributed systems.
A Hybrid Approach for Recommendation System in Web Graph Mining

amount of information is available on web and information extraction takes place in huge volume. When queries are submitted to search engines they are generally in natural languages and contain just one or two words. Search engine are unable to recognize natural language and thus it becomes difficult to extract the proper information from web according to user's interest. Here, the recommendation technique comes into picture. There are number of recommendation techniques, every technique has its advantages and disadvantages. Recommendation techniques are designed in such a way that they support various type or data sources. These data sources are in the form of text, images, audio, video etc. Efficient way to deal with all types of data sources is, model them in the form of graph and then apply recommendation algorithm on it. Initially the proposed system collects data from user's and graphs are constructed by using this data. Subsequently the system uses different algorithms for predicting user's interest. The algorithms are item to item base collaborative filtering algorithm, Pearson correlation base collaborative filtering algorithm. These are applied for finding similarities between item and users respectively. Slope one algorithm is used to find out the rating of un-rated items. In proposed hybrid method results of these algorithms are combined. The hybridization of Algorithms leads to efficient results.
Cosine Modulated Non-Uniform Filter Banks

Traditional designs for non-uniform filter bank (NUFB) are usually complex; involve complicated nonlinear optimization with a large number of parameters and lack of linear phase ([LP) property. In this paper, we describe a simple design method for multirate near perfect reconstruction (NPR) cosine modulated filter banks with non-uniform frequency spacing and linear phase property that involves optimization of only single parameter. It is derived from the uniform cosine modulated filter bank (CMFB) by merging some relevant band pass filters. The design procedure and the structure of the uniform CMFB are mostly preserved in the non-uniform implementation. Compared to other design methods our method provides very good design and converges very rapidly but the method is applicable, only if the upper band edge frequency of each non-uniform filter is an integral multiple of the bandwidth of the corresponding band. The design examples are presented to show the superiority of the proposed method over existing one.
Missing-feature approaches in speech recognition

In this article we have reviewed a wide variety of techniques based on the identification of missing spectral features that have proved effective in reducing the error rates of automatic speech recognition systems. These approaches have been conspicuously effective in ameliorating the effects of transient maskers such as impulsive noise or background music. We described two broad classes of missing feature algorithms: feature-vector imputation algorithms (which restore unreliable components of incoming feature vectors) and classifier-modification algorithms (which dynamically reconfigure the classifier itself to cope with the effects of unreliable feature components). We reviewed the mathematics of four major missing feature techniques: the feature-imputation techniques of cluster-based reconstruction and covariance-based reconstruction, and the classifier-modification methods of class-conditional imputation and marginalization. We also discussed the ways in which the common feature extraction procedures of cepstral analysis, temporal-difference features, and mean subtraction can be handled by speech recognition systems that make use of missing feature techniques. We concluded with a discussion of a small number of selected experimental results. These results confirm the effectiveness of all types of missing feature approaches discussed in ameliorating the effects of both stationary and transient noise, as well as the particular effectiveness of both soft masks and fragment decoding.
Computationally Efficient Hough Transform for 2D Object Location

This paper discusses the use of Hough transforms for feature-based object location. Though the GHT is highly robust and operates in polynomial (O(n2)) time, in real-time applications further reductions in computation are often required. A feature-orient ated HT has been designed which offers further speedup by factors of ~3, though robustness is also reduced. However, if objects are not immediately recognised, the new scheme can be made to revert gracefully to the normal GHT. Loss in robustness is most severe when objects exhibit a high degree of internal symmetry, but for objects with seven or more features arranged nonsymmetrically the new scheme should be especially valuable.
Univariate short term forecasting of solar irradiance using modified online backpropagation through time

In this paper an attempt has been made to predict the solar irradiance values for multiple look ahead time predictions with time intervals as small as fifteen minutes. The recurrent neural networks in the past have been implemented on datasets with an interval of at least 30 minutes. The recurrent neural network was trained using backpropagation through time and the prediction was done using only the past solar irradiance values. A sliding window implementation of the network was achieved and the training time for over 20000 data points was less than 5 minutes which is an improvement over the execution time of other deep learning architectures. The online form of back propagation through time was implemented with the modification that the network took into account both the past mistakes and the current direction to which it is moving. The performance of the proposed network was tested comprehensively using two years of data and it was compared with the performance of persistence model and the normal recurrent network. The modified backpropagation network outperformed the baseline models for different time intervals. The proposed network also showed improvement over results computed for dataset with 15 minutes interval which was not achieved by the earlier state of the art architectures.
Third Party Reward and Punishment: Group Size, Efficiency and Public Goods

Costly third party punishment has been interpreted as a tool for studying the enforcement of social norms. Experiments on this topic typically involve a third party observer who can pay to decrease the payoff of a player who has behaved selfishly (or generously) toward another. We investigate whether third parties are also willing to engage in costly rewarding, and whether third party responses are sensitive to the number of players affected by the selfish or generous action. Using the ‘dictator game’, where one player (the dictator) divides a sum of money between herself and a recipient, we allowed dictators to be selfish, fair, or generous. Unlike in other experiments, third parties then had the choice to either punish or reward the dictator. Across all variations, responses followed a consistent and intuitive pattern: selfish behavior was punished while generous behavior was rewarded. Not only were third parties willing to engage in costly rewarding, but rewards were in fact more common than punishments. Furthermore, third party response was more pronounced when the dictator transfer had the non-rivalrous character of a public good, in the sense that both (a) the number of recipients increased and (b) the dictator's transfer was multiplied by a constant factor, so that the larger number of recipients did not reduce potential payoff of each recipient. Third party response did not change significantly when either of these manipulations was performed alone, suggesting a particular sensitivity to situations involving a public good.
The impact of the confinement of reactants on the metal distribution in bimetallic nanoparticles synthesized in reverse micelles

Summary A kinetic study on the formation of bimetallic nanoparticles in microemulsions was carried out by computer simulation. A comprehensive analysis of the resulting nanostructures was performed regarding the influence of intermicellar exchange on reactivity. The objects of this study were metals having a difference in standard reduction potential of about 0.2–0.3 V. Relatively flexible microemulsions were employed and the concentration of the reactants was kept constant, while the reaction rate of each metal was monitored as a function of time using different reactant proportions. It was demonstrated that the reaction rates depend not only on the chemical reduction rate, but also on the intermicellar exchange rate. Furthermore, intermicellar exchange causes the accumulation of slower precursors inside the micelles, which favors chemical reduction. As a consequence, slower reduction rates strongly correlate with the number of reactants in this confined media. On the contrary, faster reduction rates are limited by the intermicellar exchange rate and not the number of reactants inside the micelles. As a result, different precursor proportions lead to different sequences of metal reduction, and thus the arrangement of the two metals in the nanostructure can be manipulated.
Improving the learning of physics concepts by using haptic devices

Haptic devices are electro-mechanical tools controlled by computers that allow to recreate the sense of touch. They enhance the sense of interaction with virtual objects from purely visual to haptic and visual. One of its application areas is in training environments, where the users can interact with virtual objects to learn procedures or tasks. In this paper we describe the use of haptic devices to improve the learning process of basic physics concepts from electromagnetism and the haptic tools via simulation of magnetic forces in 3D. We have created three scenarios with different distribution of charges: point charge, line charge, and plane charge. Each scenario was properly calibrated and has different force feedback (quadratic, linear, and constant) depending on the scenario. We wanted to investigate how forces are perceived by students. A user study was carried out to assess students' perception and knowledge acquired when they were working with the system. Results suggest that students from the treatment group achieved better understanding than those from the control group. Results also indicate that 95% of the students considered that the use of haptic devices combined with appropriate virtual environments facilitated them to understand the nature and origin of electrical forces.
Do We Feel the Same Way If We Think the Same Way? Shared Attitudes and the Social Induction of Affect

ABSTRACT Drawing on the literature on emotional mimicry, we argue that attitude similarity between a sender and a receiver influences the social induction of affect. Studies 1, 2, and 3 supported this reasoning by showing that similarity fostered, whereas dissimilarity blocked concordant reactions to a happy sender (but not to a sad sender). We also examined the mechanism behind these effects and found that similarity influenced liking of the happy sender but did not affect liking of the sad sender. Study 4 provided causal evidence for this idea by showing that similarity influenced the induction of positive affect through liking.