Miracle-dz/UniFAR-FTUs huggingface dataset:
Free-viewpoint video rendering for mobile devices

Free-viewpoint video renderers (FVVR) allow a user to view captured video footage from any position and direction. Despite the obvious appeal of such systems, they have yet to make a major impact on digital entertainment. Current FVVR implementations have been on desktop computers. Media consumption is increasingly through mobile devices, such as smart phones and tablets; adapting FVVR to mobile platforms will open this new form of media up to a wider audience. An efficient, high-quality FVVR, which runs in real time with user interaction on a mobile device, is presented. Performance is comparable to recent desktop implementations. The FVVR supports relighting and integration of relightable free-viewpoint video (FVV) content into computer-generated scenes. A novel approach to relighting FVVR content is presented which does not require prior knowledge of the scene illumination or accurate surface geometry. Surface appearance is separated into a detail component, and a set of materials with properties determining surface colour and specular behaviour. This allows plausible relighting of the dynamic FVV for rendering on mobile devices.
IS A NEW AND GENERAL THEORY OF MOLECULAR SYSTEMATICS EMERGING?

The advent and maturation of algorithms for estimating species trees—phylogenetic trees that allow gene tree heterogeneity and whose tips represent lineages, populations and species, as opposed to genes—represent an exciting confluence of phylogenetics, phylogeography, and population genetics, and ushers in a new generation of concepts and challenges for the molecular systematist. In this essay I argue that to better deal with the large multilocus datasets brought on by phylogenomics, and to better align the fields of phylogeography and phylogenetics, we should embrace the primacy of species trees, not only as a new and useful practical tool for systematics, but also as a long-standing conceptual goal of systematics that, largely due to the lack of appropriate computational tools, has been eclipsed in the past few decades. I suggest that phylogenies as gene trees are a “local optimum” for systematics, and review recent advances that will bring us to the broader optimum inherent in species trees. In addition to adopting new methods of phylogenetic analysis (and ideally reserving the term “phylogeny” for species trees rather than gene trees), the new paradigm suggests shifts in a number of practices, such as sampling data to maximize not only the number of accumulated sites but also the number of independently segregating genes; routinely using coalescent or other models in computer simulations to allow gene tree heterogeneity; and understanding better the role of concatenation in influencing topologies and confidence in phylogenies. By building on the foundation laid by concepts of gene trees and coalescent theory, and by taking cues from recent trends in multilocus phylogeography, molecular systematics stands to be enriched. Many of the challenges and lessons learned for estimating gene trees will carry over to the challenge of estimating species trees, although adopting the species tree paradigm will clarify many issues (such as the nature of polytomies and the star tree paradox), raise conceptually new challenges, or provide new answers to old questions.
Optimal Measurement Placement with Single Measurement Loss Contingency for Power System State Estimation Using Refined Genetic Algorithm

This paper proposes a refined genetic algorithm (RGA) for solving optimal measurement placement with single measurement pair loss contingency for power system state estimation. Even though the global minimum measurement pair number is , their proper positions are required to make the system observable. The observability is checked by the topological analysis method and triangular factorization analysis method. The results of 10-bus, IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and IEEE 118-bus with single measurement pair loss have yielded the optimal results. ( 1 N − Weerakorn Ongsakul and Thawatch Kerdchuen
What are students’ learning and experiences in an online learning tool designed for cognitive and metacognitive skill development?

Organic chemistry has its own representational language and as with any language, any barrier with the language makes understanding the message difficult. In this article, we describe the design of an open education resource that empowers students to master this language, the Organic Mechanisms module in orgchem101.com. We also describe the structure and findings of a pilot study designed to measure students’ learning and experiences using the module, with a focus on principles, structure, and key findings, rather than on the chemistry-specific elements.
Isolating Side E ects in Sequential

It is well known that adding side eeects to functional languages changes the operational equivalences of the language. We develop a new language construct, encap, that forces imperative pieces of code to behave purely functionally, i.e., without any visible side eeects. The coercion operator encap provides a means of extending the simple reasoning principles for equivalences of code in a functional language to a language with side eeects. In earlier work 36], similar coercion operators were developed, but their correctness required the underlying functional language to include parallel operations. The coercion operators developed here are simpler and are proven correct for purely sequential languages. The sequential setting requires the construction of fully abstract models for sequential call-by-value languages and the formulation of a weak form of \monad" suitable for expressing the semantics of call-by-value languages with side effects .
Enabling Incremental Query Re-Optimization

As declarative query processing techniques expand in scope — to the Web, data streams, network routers, and cloud platforms — there is an increasing need for adaptive query processing techniques that can re-plan in the presence of failures or unanticipated performance changes. A status update on the data distributions or the compute nodes may have significant repercussions on the choice of which query plan should be running. Ideally, new system architectures would be able to make cost-based decisions about reallocating work, migrating data, etc., and react quickly as real-time status information becomes available. Existing cost-based query optimizers are not incremental in nature, and must be run “from scratch” upon each status or cost update. Hence, they generally result in adaptive schemes that can only react slowly to updates. An open question has been whether it is possible to build a costbased re-optimization architecture for adaptive query processing in a streaming or repeated query execution environment, e.g., by incrementally updating optimizer state given new cost information. We show that this can be achieved beneficially, especially for stream processing workloads. Our techniques build upon the recently proposed approach of formulating query plan enumeration as a set of recursive datalog queries; we develop a variety of novel optimization approaches to ensure effective pruning in both static and incremental cases. We implement our solution within an existing research query processing system, and show that it effectively supports cost-based initial optimization as well as frequent adaptivity.
Localizing Google SketchUp models in outdoor 3D scans

This work introduces a novel solution for localizing objects based on search strings and freely available Google SketchUp models. To this end we automatically download and preprocess a collection of 3D models to obtain equivalent point clouds. The outdoor scan is segmented into individual objects, which are sequentially matched with the models by a variant of iterative closest points algorithm using seven degrees of freedom and resulting in a highly precise pose estimation of the object. An error function evaluates the similarity level. The approach is verified using various segmented cars and their corresponding 3D models.
Classification of follower/stationary interference and multitone interference/partial-band interference in slow frequency-hopping systems

In slow frequency hopping systems, it is critical to detect, classify, and mitigate interference, as it degrades the system performance. Generalized likelihood ratio test algorithm was proposed for the detection of interference. However, it could not classify the type of interference. In this paper, we propose algorithms to classify between follower/stationary interference and multitone interference/partial-band noise interference. Classification of interference in various types of modulation schemes is performed based on a ratio-testing approach, where the ratio is compared with a predetermined threshold value. It is critical to set the threshold at an optimal value in order to achieve a good classification performance. The optimal value and classification performance are obtained through simulation results.
Urgency Analysis of Audible Alarms in The Operating Room

Recent studies by researchers, governmental agencies, and safety organizations have recognized a deficiency in the performance of medically related audible alarms [1‐4]. In the clinical setting, care providers can suffer from alarm fatigue, a condition in which audible alarms in an operating room are perceived as a nuisance. In this study, we explore the auditory features associated with current audible alarms using tools from the music information retrieval community, and then we examine how those auditory features correlate to listeners’ perception of urgency. The results show that aperiodic changes in the auditory spectrum over time are the most salient contributor to the perception of urgency in sound. These results could inform the development of a novel standard regarding the composition of medical audible alarms.
Combustion Quality Estimation in Power Station Boilers using Median Threshold Clustering Algorithms

The estimation of combustion quality in power station boilers is of great importance in the present scenario as it plays an important role in controlling the air pollution. The harmful gases like NOx and CO from the chimney causes air pollution. The amount of NOx and CO concentration in flue gas can be maintained within admissible limits by analyzing the flame colour. The colour of the flame is affected by combustion quality. When complete combustion takes place the amount these gases at the exit are within tolerance. Hence if the quality of combustion was estimated from the flame using image processing technique, it is possible to minimize the air pollution. This new strategy developed uses median threshold for feature reduction and various clustering algorithms to estimate the quality of combustion.
A novel fuzzy compromise optimization method to evaluating the strategies of R&D consortia in Taiwan

While entering WTO, the medium & small enterprises (M&SE) in Taiwan, accounts for 98 % of industries, are facing even more competition due to insufficient capital, human resources, and limited organization scale. To strengthen their competitiveness for globalization environment, they should have to enhance efficiency in operational management and efficiency in strategy development. Meanwhile, M&SE should aggressively form strategic R&D alliance to effectively consolidate resources to jointly achieve technological capabilities to multiply the synergy. In this study we introduce fuzzy set theory with multiple objective compromise optimization technique called fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to explore the roadmap of R&D alliance of machinery industry in Taiwan. Firstly, fuzzy hierarchical framework was employed to establish the evaluation system for determining the relative importance of evaluated criteria. Secondly, fuzzy geometric mean method was utilized to aggregate the performance score by individual judgment of participated evaluators under fuzzy environment. Thirdly, the fuzzy synthetic value of each development strategies was integrated. Fourthly, fuzzy TOPSIS was exploited to find the optimal compromise solution which should have the shortest distance from the positive ideal solution (PIS) as well as the farthest distance from the negative ideal solution (NIS). Finally, we successfully demonstrate that fuzzy TOPSIS is appropriate assessment for evaluation of multiple objective decision making (MODM) problems under fuzzy environment.
Analytic Antialiasing for Selective High Fidelity Rendering

Images rendered using global illumination algorithms are considered amongst the most realistic in 3D computer graphics. However, this high fidelity comes at a significant computational expense. A major part of this cost arises from the sampling required to eliminate aliasing errors. These errors occur due to the discrete sampling of continuous geometry space inherent to these techniques. In this paper we present a fast analytic method for predicting in advance where antialiasing needs to be computed. This prediction is based on a rapid visualisation of the scene using a GPU, which is used to drive a selective renderer. We are able to significantly reduce the overall number of aniti-aliasing rays traced, producing an image that is perceptually indistinguishable from the high quality image at a much reduced computational cost.
Image Classification by Transfer Learning Based on the Predictive Ability of Each Attribute

Machine learning is the basis of important advances in artificial intelligence such as image and speech recognition and natural language processing. Unlike general machine learning, which uses the same task for training and testing, transfer learning uses the results trained by other tasks to learn a new task. Among the various transfer learning algorithms have been proposed to date, we focus on attribute-based transfer learning. This algorithm realizes transfer learning by introducing attributes and transferring the results of training. However, the existing algorithm does not consider the extent to which attributes contribute to predicting the target class (called the predictive ability in this study). Here, we weighted each attribute by the extent to which it contributes to the evaluation equation. We confirmed that the accuracy rate of the proposed technique was higher than that of the preceding work.
Person Identification by Iris Recognition Using 2-D Reverse Biorthogonal

Iris Recognition System being highly accurate and reliable is superior to all the biometric systems for person identification. This paper describes a Hough Transform and Reverse Biorthogonal Wavelet Transform based iris recognition system. Performance of the system has been evaluated on various levels of decomposition. For this, grayscale images of UBIRIS ver.1 database have been used.
A Microsaccadic Account of Attentional Capture and Inhibition of Return in Posner Cueing

Microsaccades exhibit systematic oscillations in direction after spatial cueing, and these oscillations correlate with facilitatory and inhibitory changes in behavioral performance in the same tasks. However, independent of cueing, facilitatory and inhibitory changes in visual sensitivity also arise pre-microsaccadically. Given such pre-microsaccadic modulation, an imperative question to ask becomes: how much of task performance in spatial cueing may be attributable to these peri-movement changes in visual sensitivity? To investigate this question, we adopted a theoretical approach. We developed a minimalist model in which: (1) microsaccades are repetitively generated using a rise-to-threshold mechanism, and (2) pre-microsaccadic target onset is associated with direction-dependent modulation of visual sensitivity, as found experimentally. We asked whether such a model alone is sufficient to account for performance dynamics in spatial cueing. Our model not only explained fine-scale microsaccade frequency and direction modulations after spatial cueing, but it also generated classic facilitatory (i.e., attentional capture) and inhibitory [i.e., inhibition of return (IOR)] effects of the cue on behavioral performance. According to the model, cues reflexively reset the oculomotor system, which unmasks oscillatory processes underlying microsaccade generation; once these oscillatory processes are unmasked, “attentional capture” and “IOR” become direct outcomes of pre-microsaccadic enhancement or suppression, respectively. Interestingly, our model predicted that facilitatory and inhibitory effects on behavior should appear as a function of target onset relative to microsaccades even without prior cues. We experimentally validated this prediction for both saccadic and manual responses. We also established a potential causal mechanism for the microsaccadic oscillatory processes hypothesized by our model. We used retinal-image stabilization to experimentally control instantaneous foveal motor error during the presentation of peripheral cues, and we found that post-cue microsaccadic oscillations were severely disrupted. This suggests that microsaccades in spatial cueing tasks reflect active oculomotor correction of foveal motor error, rather than presumed oscillatory covert attentional processes. Taken together, our results demonstrate that peri-microsaccadic changes in vision can go a long way in accounting for some classic behavioral phenomena.
Human-Animal Relationships: A Cross-Cultural Comparison of Human Attitudes towards Animals

Today humans, animals, and the environment are remarkably interconnected and interdependent at a global level. How humans and animals relate to each other is a moral, material and cultural matter, and accordingly, their relationship can never be grasped in isolation. This dissertation contributes to the body of knowledge about the sustainable human-animal relationships in Europe and Asia. It analysed Dutch, Chinese and Japanese people’s attitudes toward animals and the emotional and environmental relationship between companion animals and owners from cultural and economic perspectives. Results show the emotional relationships between companion animals and owners on the one hand, and acknowledge the significant environmental impacts of food consumption by companion animals on the other hand. Therefore, finding strategies to balance the positive emotional connection and the negative environmental impacts between companion animals and owners is an excellent prelude to achieving the sustainable human-animal relationships.
Spectrum allocation using fuzzy logic with optimal power in wireless network

This paper presents an autonomous method which is based on the fuzzy logic and optimal power in orthogonal frequency division multiple access (OFDMA). Our proposed method assigns resource blocks (RBs) to users using the fuzzy logic, where transmitted power is calculated for each RB, without any communication between cells. Gathering information such as, required rate of a user, desired signal level, fading on each RB, and the interference level seen of each RB results in completely autonomous approach. Using fuzzy logic our proposed method determine the optimal RB assignment. Moreover, this method calculates the required power to achieve desired RB taking into account the signal quality. Therefore, the presented method potentially satisfies user rate and provides appropriate quality of service (QoS). Simulation results show that proposed method reduces average power while achieving a high sum rate for users with low computational complexity. Therefore, the proposed methods' solution is reasonably close to optimal solution.
A Fully Analytical Model for the Series Impedance of Through-Silicon Vias With Consideration of Substrate Effects and Coupling With Horizontal Interconnects

This paper introduces a fully analytical and physical model capable of extracting high-frequency series impedance of through-silicon vias (TSVs) in 3-D integrated chips with consideration of the eddy currents in the surrounding Si substrate and coupling with horizontal interconnects. The model employs the vertical-to-vertical and horizontal-to-vertical 3D vector potential Green's function in layered media and is concise and sufficiently accurate in the entire range of interest for both the frequency and the center-to-center distance between TSVs. Along with the series impedances between horizontal wires, which are extracted from the discrete complex image method, as well as the TSV and horizontal wire capacitance values, the total loop impedance can be obtained. Our approach is verified against a full-wave finite- element-method electromagnetic solver High Frequency Structure Simulator, and it shows good accuracy (<; 7% error) in the entire frequency range examined (up to 100 GHz). Given the fact that the formulated TSV series impedance model is purely analytical, the model could be efficiently used for system-level interconnect impedance extraction in emerging 3-D integrated systems.
Personalized tag recommendation based on generalized rules

Tag recommendation is focused on recommending useful tags to a user who is annotating a Web resource. A relevant research issue is the recommendation of additional tags to partially annotated resources, which may be based on either personalized or collective knowledge. However, since the annotation process is usually not driven by any controlled vocabulary, the collections of user-specific and collective annotations are often very sparse. Indeed, the discovery of the most significant associations among tags becomes a challenging task. This article presents a novel personalized tag recommendation system that discovers and exploits generalized association rules, that is, tag correlations holding at different abstraction levels, to identify additional pertinent tags to suggest. The use of generalized rules relevantly improves the effectiveness of traditional rule-based systems in coping with sparse tag collections, because: (i) correlations hidden at the level of individual tags may be anyhow figured out at higher abstraction levels and (ii) low-level tag associations discovered from collective data may be exploited to specialize high-level associations discovered in the user-specific context. The effectiveness of the proposed system has been validated against other personalized approaches on real-life and benchmark collections retrieved from the popular photo-sharing system Flickr.
The effect of moderately increased intakes of complex carbohydrates ( cereals , vegetables and fruit ) for 12 weeks on iron and zinc metabolism

Fifteen adult women were given diets in which the intake of complex carbohydrates was increased from 20 to 30 g over a 12 week period. Metabolic balances were carried out, iron and zinc absorption tests performed using stable isotopes, and Fe and Zn status monitored. Although effects on bowel function were observed, the changed diet had no influence on any of the previously described variables. It was concluded that a moderate increase in cereals, fruit and vegetables did not have an adverse effect on Fe or Zn nutrition.
Blocking Foxy Phishing Emails with Historical Information

Unlike most of the spams targeting for advertisements only, phishing spams try to cheat the email recipients with bogus sender addresses so as to obtain confidential information of the recipients. This paper presents a Sender Authentication Protocol (SAP) which aims to filter out this kind of crafty spoofing emails. To this end, SAP challenges the claimed-sender with the historical emails so as to verify the authenticity of the sender. As it does not change the email protocol, and is able to be embedded into the off-the-shelf email software such as Microsoft Outlook, SAP is not only easy to be deployed, but also fully compatible with other anti-spam technologies. To illustrate its feasibility, we develop a SAP add-in for enhancing Microsoft Outlook. The SAP add-in will be started automatically as long as Outlook is started. As the enhanced Outlook has the same user interface as the original one, the add-in is transparent and friendly to the users.