Miracle-dz/UniFAR-FTUs huggingface dataset:
A Necessary and Sufficient Condition for Deadlock-Free Routing in Cut-Through and Store-and-Forward Networks

This paper develops the theoretical background for the design of deadlock-free adaptive routing algorithms for virtual cut-through and store-and-forward switching. This theory is valid for networks using either central buffers or edge buffers. Some basic definitions and three theorems are proposed, developing conditions to verify that an adaptive algorithm is deadlock-free, even when there are cyclic dependencies between routing resources. Moreover, we propose a necessary and sufficient condition for deadlock-free routing. Also, a design methodology is proposed. It supplies fully adaptive, minimal and non-minimal routing algorithms, guaranteeing that they are deadlock-free. The theory proposed in this paper extends the necessary and sufficient condition for wormhole switching previously proposed by us. The resulting routing algorithms are more flexible than the ones for wormhole switching. Also, the design methodology is much easier to apply because it automatically supplies deadlock-free routing algorithms.
Study of the Social Network's Impact on College Students Purchasing Behavior

With the fast development of internet, more and more college students become regular visitors of different kinds of social networks. This paper takes college students in Wuhan as a research object and tries to find out how tie strength and emotion extent of information influence their purchase intension and redistribution of information. In the meanwhile this paper provides some implications of marketing according to the research conclusion.
CONTROL CHART FOR MONITORING NONPARAMETRIC PROFILES WITH ARBITRARY DESIGN

Nonparametric profile monitoring (NPM) is for monitoring over time a functional relationship between a response variable and one or more explanatory variables when the relationship is too complicated to be specified parametrically. It is widely used in industry for the purpose of quality control of a process. Existing NPM approaches require a fundamental assumption that design points within a profile are deterministic (i.e., non-random) and they are unchanged from one profile to another. In practice, however, different profiles often have different design points, and in some cases they might even be random. NPM is particularly challenging in such cases because it is difficult to combine data in different profiles properly for data smoothing and for process monitoring. In this paper, we propose a novel exponentially weighted moving average (EWMA) control chart for handling this problem, based on local linear kernel smoothing. In the proposed chart, the exponential weights used in the EWMA scheme at different time points are integrated into a nonparametric smoothing procedure for smoothing individual profiles. Because of certain good properties of the charting statistic, this control chart is fast to compute, easy to implement, and efficient to detect profile shifts. Some numerical results show that it works well in applications.
Practical PET Respiratory Motion Correction in Clinical PET/MR

Respiratory motion during PET acquisition may lead to blurring in resulting images and underestimation of uptake parameters. The advent of integrated PET/MR scanners allows us to exploit the integration of modalities, using high spatial resolution and high-contrast MR images to monitor and correct PET images degraded by motion. We proposed a practical, anatomy-independent MR-based correction strategy for PET data affected by respiratory motion and showed that it can improve image quality both for PET acquired simultaneously to the motion-capturing MR and for PET acquired up to 1 h earlier during a clinical scan. Methods: To estimate the respiratory motion, our method needs only an extra 1-min dynamic MR scan, acquired at the end of the clinical PET/MR protocol. A respiratory signal was extracted directly from the PET list-mode data. This signal was used to gate the PET data and to construct a motion model built from the dynamic MR data. The estimated motion was then incorporated into the PET image reconstruction to obtain a single motion-corrected PET image. We evaluated our method in 2 steps. The PET-derived respiratory signal was compared with an MR measure of diaphragmatic displacement via a pencil-beam navigator. The motion-corrected images were compared with uncorrected images with visual inspection, line profiles, and standardized uptake value (SUV) in focally avid lesions. Results: We showed a strong correlation between the PET-derived and MR-derived respiratory signals for 9 patients, with a mean correlation of 0.89. We then showed 4 clinical case study examples (18F-FDG and 68Ga-DOTATATE) using the motion-correction technique, demonstrating improvements in image sharpness and reduction of respiratory artifacts in scans containing pancreatic, liver, and lung lesions as well as cardiac scans. The mean increase in peak SUV (SUVpeak) and maximum SUV (SUVmax) in a patient with 4 pancreatic lesions was 23.1% and 34.5% in PET acquired simultaneously with motion-capturing MR, and 17.6% and 24.7% in PET acquired 50 min before as part of the clinical scan. Conclusion: We showed that a respiratory signal can be obtained from raw PET data and that the clinical PET image quality can be improved using only a short additional PET/MR acquisition. Our method does not need external respiratory hardware or modification of the normal clinical MR sequences.
Design limitations and its effect in the performance of ZC1-DPLL

The paper studies the dynamics of a conventional positive going zero crossing type digital phase locked loop (ZC1-DPLL) taking non-ideal responses of the loop constituent blocks into account. The finite width of the sampling pulses and the finite propagation delay of the loop subsystems are properly modeled mathematically and the system dynamics is found to change because of their inf luence considered separately. However, when these two are taken simultaneously, the system dynamics can be made nearly equivalent to that of the ideal system. Through an extensive numerical simulation a set of optimum parameters to overcome design limitations have been obtained.
Advanced Deep Web Crawler Based on Dom

Due to the fact that large amount of data today can only be stored in deep web. In view of the work done by others on deep web crawlers, it is extinct that no perfect, or even complete crawlers for deep web data has been made. To meet the needs of deep web search, we have worked out a new structure of crawler, currently concerned most on extracting data from forms - the most common type of deep web interface. Our crawler's makes some innovative parts such as the mainframe extracting module and the algorithm to distinguish different websites with the same url using improved Bayesian classification and to expand the function to AJAX form dealing and so on. Also, Dom Tree is used to make easier and more visual the analysis and treatment of downloaded web pages.
Semantic Class Labeling Based on CRF for Limited Domain Searching Service

In the process of spoken language understanding (SLU), the first step is to label semantic concept (or class). Since there are variations induced by automatic speech recognition (ASR) or missing field of keywords in limited domain, extracting the main semantic concepts will be the key problem in SLU. In this paper, we utilize some effective features based on conditional random field (CRF) to segment Chinese named entities, which include people's name and other names in domain. The goal of this paper is to acquire the key semantic concept regardless of whether it is a complete or fragment concept. Experiments show that the whole of performance of named entities recognition arrives at 96.67%.
The Stochastic Conditional Duration Model: A Latent Factor Model for the Analysis of Financial Durations

A new model for the analysis of durations, the stochastic conditional duration (SCD) model, is introduced. This model is based of the assumption that the durations are generated by a latent stochastic factor that follows a first order autoregressive process. The latent factor is pertubed multiplicatively by an innovation distributed as aWeibull or gamma variable. The model can capture a wide range of shapes of hazard functions. The estimation of the parameters is performed by quasi-maximum likelihood, after transforming the original nonlinear model into a space state representation and using the Kalman filter. The model is applied to stock market price-durations, looking at the relation between price durations, volume, spread and trading intensity. (This abstract was borrowed from another version of this item.)
The Recommendation Dashboard: A System to Visualise and Organise Recommendations

Recommender systems are becoming common tools supporting automatic, context-based retrieval of resources. When the number of retrieved resources grows large visual tools are required that leverage the capacity of human vision to analyse large amounts of information. We introduce a Web-based visual tool for exploring and organising recommendations retrieved from multiple sources along dimensions relevant to cultural heritage and educational context. Our tool provides several views supporting filtering in the result set and integrates a bookmarking system for organising relevant resources into topic collections. Building upon these features we envision a system which derives user's interests from performed actions and uses this information to support the recommendation process. We also report on results of the performed usability evaluation and derive directions for further development.
Wizard chess: An autonomous chess playing robot

Robotic chess is an emerging problem domain in the field of human-robot interaction. In this paper, Wizard Chess, an autonomous chess playing robotic system is introduced, which is capable of recognizing all possible chess board states, generating subsequent moves as well as executing those moves. Existing chess playing robots use camera for the detection of game states and articulated robotic arm for the movement of pieces on the board which make these robots particularly complex and highly expensive. Compared to these prior works, Wizard Chess is architecturally simple, low-cost and feasible in case of personal use, educational and recreational purposes. In this system, an X-Y Cartesian table is used for moving chess pieces along the board. Additionally, sixty-four magnetic reed switches are used for determining the current game state. A prototype of the proposed robotic chess is implemented and an extensive performance evaluation is presented in this paper which shows the feasibility of the system in real-life situations.
Elo Ratings for Structural Credit Assignment in Multiagent Systems

In this paper we investigate the applications of Elo ratings (originally designed for 2-player chess) to a heterogeneous nonlinear multiagent system to determine an agent's overall impact on its team's performance. Measuring this impact has been attempted in many different ways, including reward shaping; the generation of heirarchies, holarchies, and teams; mechanism design; and the creation of subgoals. We show that in a multiagent system, an Elo rating will accurately reflect the an agent's ability to contribute positively to a team's success with no need for any other feedback than a repeated binary win/loss signal. The Elo rating not only measures "personal" success, but simultaneously success in assisting other agents to perform favorably.
Assessing the Impact of Past Distributional Shifts on Global Poverty Levels

Despite an increased awareness for income distribution, much of the literature still focuses exclusively on growth as a means of poverty reduction and argues that the poverty impact of distributional change is so minor that it can be neglected. This article questions these policy recommendations. Based on World Bank data, it shows that if all countries had maintained their most equitable income distribution of the past few decades, the global poverty headcount could have been reduced from 1.2 billion to around 900 million. When the growth effects of lower inequality are incorporated into the model, the figure is further reduced to approximately 800 million. Although these calculations should be interpreted with some caution, they clearly warn policy makers that stabilizing or improving income distribution must be an integral part of any feasible poverty reduction strategy.
Integrating Concern for Animals into Personal Values

ABSTRACT Recent scholarship in human–animal relationships demonstrates that new theoretical insights emerge when these relationships are seriously considered. We suggest that the most prominent measures of values in the environmental decision-making research literature, the Schwartz approach, may not adequately capture key aspects of how individuals value animals. Our research used confirmatory factor analysis and ordinary least squares regression techniques on data collected from an Amazon Mechanical Turk online survey. It expands on the values commonly used in the environmental decision-making literature to consider whether there is a distinct value dimension focused on animals. Our suggestion that a “concern for animals” value orientation constitutes a motivation that is separate from the other values commonly used in research on environmental decision-making finds support in our data. Traditional scaling methods indicate that the items we created have high reliability when used to form an unweighted additive scale. The results of confirmatory factor analysis are consistent with the argument that this “concern for animals” value orientation is distinct from biospheric altruism, humanistic altruism, self-interest, traditionalism, openness to change, and hedonism. Finally, our regressions using values and demographic, social, and political characteristics as controls show that an animal-focused value orientation and biospheric altruism have different relationships with identification with the animal rights movement than with identification with the environmental movement.
Rating Prediction for Amazon Movies A Comparison of Text-Mining and Recommender System Algorithms

With the proliferation of ecommerce, social, and media consumption websites in recent years, the task of providing accurate recommendations has become essential to maintaining user engagement and improving user satisfaction. There are a wide variety of recommender system algorithms intended to solve these tasks, including content-based, collaborative filtering, and latent factor algorithms. While these algorithms perform well given adequate data and appropriate rating density, their performance often suffers on smaller and sparser datasets. This is known as the cold-start problem for recommender systems, and there have been many recommendation algorithms proposed to address this issue. One potential approach is to utilize the text of the review along with the observed ratings. This takes advantage of textmining techniques to extract user sentiment from relatively few observations, resulting in improved rating predictions for new users and items. In this project, we analyze a dataset consisting of 50,000 movie reviews on Amazon. The dataset includes the observed rating on a 1 5 scale, along with the text of the user’s review. As the dataset is quite sparse, these reviews prove to be valuable in predicting unobserved ratings. We train two supervised learning algorithms on the data, linear regression and logistic regression. These algorithms used a bag-ofwords model of the observed reviews to predict movie ratings. We also train a latent factor recommender system on the data, to predict ratings based on the observed user and item rating behavior. After analyzing the results of these algorithms, we se that the simple linear regression model outperforms all other models, including the latent factor algorithms which are optimized for this particular task. This matches our expectations, as we would expect textual reviews to provide richer and more predictive features than the observed ratings, especially given the size and sparsity of our dataset. In Section 2, we present relevant information and statistics about the Amazon movies dataset. We describe the predictive tasks and evaluation metrics we used in Section 3, and the corresponding models in Section 4. Section 5 discusses the results of our algorithms on the Amazon movies data. Finally, we present related literature in Section 6, and conclude in Section 7.
Semantic annotation of Bangla news stream to record history

Every day thousands of news articles are published in Bangla from several different sources on the web and this number is even increasing rapidly. On the contrary, the readers are often selective to read their desired news only. In this connection, classical Information Extraction (IE) techniques are used to query with keywords from unstructured or semi-structured news contents to fulfill partial requirements. However, they cannot interpret sequences of events, relation among entities, inference some unveiled facts to facilitate further human analysis. To achieve this goal, semantic technology adds formal structure and semantics to the news stream. In this paper, we propose a system to analyze Bangla news content to annotate especially things, people and places with semantic technology automatically by extracting what happened, when, where and who being involved in the news with the help of classical Natural Language Processing (NLP) techniques. Furthermore, we relate news of today with the previous news to accumulate information over time. We present our proposed system of annotating Bangla news semantically and experiment with SPARQL to inference integrated news from different sources over time and shows its effectiveness in querying specific information.
Tree-guided transformation-based homograph disambiguation in Mandarin TTS system

Homograph disambiguation is the core issue of the grapheme- to-phoneme conversion in Mandarin Text-to-Speech system. In this paper, a hybrid algorithm called tree-guided transformation-based learning (TTBL), which combines decision tree with transformation-based learning (TBL), is proposed to resolve homograph ambiguity. It can automatically generate templates, thereby avoiding manually summarizing templates, which is time-consuming and laborious in conventional TBL. In addition, the paper evaluates various keyword selection approaches in different domains. Results of comparative experiments show that, for the task of homograph disambiguation, templates automatically generated by decision tree achieve comparable performance to manually summarized templates, and the TTBL significantly outperforms decision tree.
Decision-theoretic narrative planning for guided exploratory learning environments

Interactive narrative environments have been the focus of increasing attention in recent years. A key challenge posed by these environments is narrative planning, in which a director agent orchestrates all of the events in an interactive virtual world. To create effective interactions, the director agent must cope with the task's inherent uncertainty, including uncertainty about the user's intentions. Moreover, director agents must be efficient so they can operate in real time. To address these issues, we present U-DIRECTOR, a decision-theoretic narrative planning architecture that dynamically models narrative objectives (e.g., plot progress, narrative flow), storyworld state (e.g., physical state, plot focus), and user state (e.g., goals, beliefs) with a dynamic decision network (DDN) that continually selects storyworld actions to maximize narrative utility on an ongoing basis. DDNs extend decision networks by introducing the ability to model attributes whose values change over time; decision networks extend Bayesian networks by supporting utility-based rational decision making. The U-DIRECTOR architecture also employs an n-gram goal recognition model that exploits knowledge of narrative structure to recognize users' goals and an HTN planner that operates in two coordinated planning spaces to integrate narrative and tutorial planning. U-DIRECTOR has been implemented in a narrative planner for an interactive narrative learning environment in the domain of microbiology in which a user plays the role of a medical detective solving a science mystery. Formal evaluations suggest that the U-DIRECTOR architecture satisfies the real-time constraints of interactive narrative environments and creates engaging experiences.
Activity in Human Visual and Parietal Cortex Reveals Object-Based Attention in Working Memory

Visual attention enables observers to select behaviorally relevant information based on spatial locations, features, or objects. Attentional selection is not limited to physically present visual information, but can also operate on internal representations maintained in working memory (WM) in service of higher-order cognition. However, only little is known about whether attention to WM contents follows the same principles as attention to sensory stimuli. To address this question, we investigated in humans whether the typically observed effects of object-based attention in perception are also evident for object-based attentional selection of internal object representations in WM. In full accordance with effects in visual perception, the key behavioral and neuronal characteristics of object-based attention were observed in WM. Specifically, we found that reaction times were shorter when shifting attention to memory positions located on the currently attended object compared with equidistant positions on a different object. Furthermore, functional magnetic resonance imaging and multivariate pattern analysis of visuotopic activity in visual (areas V1–V4) and parietal cortex revealed that directing attention to one position of an object held in WM also enhanced brain activation for other positions on the same object, suggesting that attentional selection in WM activates the entire object. This study demonstrated that all characteristic features of object-based attention are present in WM and thus follows the same principles as in perception.
On the grammaticality of recomplementation in Spanish

In English, an overt secondary complementizer has been described as a grammatical violation, a claim that cannot be corroborated for Spanish. The present study fills this gap in the literature by asking whether the overt variety is associated with a decrement in acceptability judgment when compared to the null variety. Results from a speeded aural acceptability judgment task suggest that there is indeed a significant decrement in acceptability rating. This finding is robust across both statement and question constructions. A processing-based account is offered in order to unify results from both Spanish and English. Importantly, given that the native Spanish speakers do not fully reject the overt variety, the proposed account is meant to complement rather than to replace existing grammar-based accounts.