Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing private user models and shared personas

This paper explores the question: How should we modify and extend existing UM Languages and representations to address specific ubiquitous computing issues? The answer we propose builds upon previous work on single user model server and exploits the user model representation to enable users to permit parts of the environment to access selective personas, which allow the user to provide one persona to one application and a different persona to another. We discuss the way that sensors can contribute to the user model in an ubiquitous computing environment. We also describe the ways that we address the issues of reusability and group models in ubiquitous computing.
A Novel Pre-FFT Beamforming for an OFDM System with an Antenna Array in WLAN

In this paper, a new pre-fast Fourier transformation (FFT) beamforming approach is proposed for an orthogonal frequency division multiplexing (OFDM) system with an antenna array in wireless local area network (WLAN). Compared with the OFDM system based on traditional beamforming or principle eigen-vector beamforming, it can offer almost the same performance with significant reduction in system structure as well as computational complexity. In addition, it outperforms the system with a switched-beam beamformer. Simulations are conducted to show the effectiveness of the proposed approach
The effects of different planting methods on sweet potato

The aim of our research program is to develop the production technology and to examine the possibilities of the utilization of sweet potato (Ipomea batatas (L.) Lam. ). In 2016 and 2017, production technology experiments were set up with four replications in a randomized block design on an alluvial soil in Deszk, Hungary. In our field experiments, we obtained results of planting material production, planting methods and the optimized fertilization of sweet potato. Experimental plots were set up either with or without ridges. In heavy soils – where usually ridge planting is preferred –, in 2016, the production technology without ridges proved to be more effective. In 2017, however, we got opposite results: based on the result of the harvest, the production technology with ridges proved to be more effective. The transplants originating from cuttings from tubers (primary transplants) or from shoots (secondary transplants) did not show significant differences, however, in both years, yield levels were higher on ridges with secondary transplants. Sweet potato yield may vary widely among producers due to improper ridging and planting orientation. The aim of this study was to establish the proper ridging and planting orientation, so as to enhance constant reliable yields among sweet potato producing farmers.
Category Development Using Complete Semantic Networks Category Development Using Complete Semantic Networks 3 Analysis of Text Sample Using Lexical Cohesion 4 Content Analysis Category System

1 Abstract In developing lexicons to support a scoring system for open-ended test questions and a content analysis system, we found it necessary to extend the conventional notion of a semantic network as consisting of concept nodes and (perhaps several kinds of) labeled links by adding nodes for pseudoentries capturing semantic features (as in typed feature structures) and by viewing links as nodes with selectional restrictions on their arguments. For the test scoring system, we incorporated a module for maintaining lexical cohesion, based on the extended semantic network, into a general discourse analysis algorithm. The module accomplishes this objective by requiring discourse entities to be instantiations of lexical relations licensed by the network. For the content analysis dictionary, which assigns categories to entries, we found that category definition required use of lexical semantic information beyond the ISA (and other standard relational) backbone of a semantic network. We conclude that these more "complete" semantic networks are necessary and desirable for various NLP tasks. We found that several principles for category development and extension readily emerged, including principles for characterizing the "category" of a text. These principles have some correspondence with techniques for identifying the topic of a text. We describe some summary procedures for category development and naming. 2 Introduction Two specific tasks involving lexicon development and analysis have led to the need for the broadened conceptualization of the knowledge contained in semantic networks of lexicons. The first task was to build a knowledge representation of texts (5-6 sentences of 30-100 words) for the Formulating-Hypotheses test item developed at the Educational Testing Service (ETS); this task required the use of lexical cohesion principles to build the discourse structure of the text. The second task involved the analysis of a content-analysis dictionary of 11,000 words. We describe these tasks, both of which involved use of WordNet (Miller, et al., 1990) and consideration of other semantic networks. We identified shortcomings of these networks in completing the tasks. We present extensions of the nodes and relations in semantic networks, principally by converting labels on links into lexicon entries and by adding pseudoentries corresponding to pieces of lexical semantic information, to enable completion of the tasks. In both tasks, we found that sublexicons can be characterized in a way resembling categorization schemes used in information retrieval (Hearst & Schütze, 1996). We compare these extended networks with previous conceptions of semantic networks and their relation to …
Learning resources in the context of play. Promoting effective learning in early childhood

SUMMARY This articles argues that there is no absolute standard for defining the quality of early childhood education and care. Current approaches to effective teaching/learning are of a very limited value for the definition of a standard for good education. In an attempt at finding an alternative definition of effective teaching in early childhood education, the article explores from a Vygotskian perspective the notion of play as a format for young children's activities. From this perspective, every activity — including play activity- is seen as a distributed form of cognition, that comprises different cultural resources that can be employed by the actors for the accomplishments of the activity. Effective learning in early childhood now can be conceived as a characteristic of a shared playful activity for children in which they are stimulated to use as many of the available resources as possible. Initial evaluations of a play-based curriculum based on these ideas, are presented and discussed.
Energy aware routing in IEEE 802.11s wireless mesh networks

Wireless Mesh Networking is a continuous growing technology that can be used used for several application scenarios, such as military tactical operations, etc. in next generation wireless networks. The IEEE 802.11s Standard defines the procedures that wireless nodes follow in order to interconnect and create a Wireless Local Area Network (WLAN) mesh network. It, also, defines the routing protocol and the metric that are used by a IEEE 802.11s mesh network to route data. However, although the energy consumption of mesh nodes is a crucial parameter for the network's lifetime in specific purpose operations (e.g. military and health) the default metric proposed by the standard doesn't take into account the energy of the nodes. In this paper, a new energy - aware routing metric for the IEEE 802.11s mesh networks has been implemented. Simulation results showed that the proposed metric prolongs the lifetime of a WMN in comparison with the the default metric used by IEEE 802.11s Standard while causing a little higher total delay in the network.
Jumping patterns analysis for 1-DOF two-legged robot

In this paper, a jumping motion is considered with the goal of achieving high-speed dynamic motion for legged robots. We propose a two-step jumping pattern which makes use of the characteristic of a lightweight and fast actuator. In addition, jumping strategies based on the dynamics of the robots are presented, and the effect of structural parameter on jumping height is analyzed. Experimental results are shown in which a 1-DOF two-legged robot jumps with three patterns.
Activation of sorbitol pathway in metabolic syndrome and increased susceptibility to cataract in Wistar-Obese rats

Purpose Obesity is a major public health problem worldwide, and of late, epidemiological studies indicate a preponderance of cataracts under obesity conditions. Although cataract is a multifactorial disorder and various biochemical mechanisms have been proposed, the influence of obesity on cataractogenesis has yet to be investigated. In such a scenario, a suitable animal model that develops cataract following the onset of obesity will be a welcome tool for biomedical research. Therefore, we investigated the molecular and biochemical basis for predisposition to cataract in the obese mutant rat models established in our institute because 15%–20% of these rats develop cataracts spontaneously as they reach 12–15 months of age. Methods We analyzed the major biochemical pathways in the normal lenses of different age groups of our obese mutant rat strains, Wistar/Obese (WNIN/Ob) and WNIN/GR-Ob, the former with euglycemia and the latter with an additional impaired glucose tolerance trait. In addition, sorbitol levels were estimated in the cataractous lenses of the obese rats. Results Except for the polyol pathway, all the principal pathways of the lens remained unaltered. Therefore, sorbitol levels were found to be high in the normal eye lenses of obese rats (WNIN/Ob and WNIN/GR-Ob) compared to their lean controls from three months of age onwards. Between WNIN/Ob and WNIN/GR-Ob, the levels of sorbitol were higher in the latter, suggesting a synergistic effect of impaired glucose tolerance along with obesity in the activation of the sorbitol pathway. Either way, an elevated sorbitol pathway seemed to be the predisposing factor responsible for cataract formation in these mutant rats. Conclusions Activation of the sorbitol pathway indeed enhances the risk of cataract development in conditions such as metabolic syndrome. These rat models thus may be valuable tools for investigating obesity-associated cataract and for developing intervention strategies, based on these findings.
Optimal timing for market entry

This paper considers the optimal market entry timing of a firm facing price uncertainty and investment irreversibility. When the entry decision is made, the firm has to pay the necessary investment costs and from then onwards will receive the expected future cash-flows. The total expected income of the investment is given by the sum over time of the expected discounted future cash-flows. For the investment to be worthwhile this value must be significantly above, and not just above, the investment cost. Therefore, we address investment decisions where one must decide when it is the best time to make a commitment, losing the option to wait for a better market opportunity. We developed a model that provides us with a rule that specifies under which conditions we should enter the market. In addition, the methodology developed also provides an estimate on how long we should wait before entering the market.
Passive NATted Hosts Detect Algorithm Based on Directed Acyclic Graph Support Vector Machine

Unauthorized network address translation (NAT) devices may be a significant security problem. They provide unrestricted access to any number of hosts connecting to them. Some attackers may use computers hidden behind NAT devices to conduct malicious activities such as denial of service. An algorithm is proposed in this work to detect hosts hidden behind NAT. Different from previous researches, the algorithm does not depend on any special field in any packet header. It is based on analyzing traffic features with directed acyclic graph support vector machine (DAGSVM). Firstly, traffic models of hosts are selected from training samples with DAGSVM. Then the models and classifier are used for predicting host number of unknown traces. What revealed by the experiment includes that the proposed algorithm is effective, even when there are more hosts in the test set than it is in the training set, and the accuracy will fall when there are more unknown hosts in the test traces.
A method for determining the number of signals based on modified K-means clustering

Determining the number of signals impinging on an array of sensors is one of the hot and important problems in array processing field. Research on estimation method which is available in colored noise field has important theoretical and practical meaning. A new method is proposed based on K-means clustering algorithm, we construct a distinguishing criteria in reason according to the difference between the signal and noise eigenvalues, and then the signal and noise eigenvalues are classified into two kinds using K-means clustering method. The method is valid in colored noise field. Computer simulation results and the available measured data confirmation prove the effective performance of the method.
Symmetry-reinforced Nogood Recording from Restarts

Nogood recording from restarts is a form of lightweight learn- ing that combines nogood recording with a restart strategy. At the end of each run, nogoods are extracted from the current (rightmost) branch of the search tree. These nogoods can be used to prevent parts of the search space from being explored more than once. In this paper, we propose to reinforce nogood recording (from restarts) by exploiting symmetries: every time the solver has to be restarted, not only the nogoods that are extracted from the current branch are recorded, but also some additional nogoods that can be computed by means of the previously identi ed problem symmetries. This mechanism of computing symmetric nogoods can be iterated until a xed-point is reached, and controlled (if necessary) by limiting the number and/or the size of recorded nogoods.
The Recognition Problem for the Set of Perfect Squares

Lower bounds on the capacity and on the product of capacity and computation time are obtained for machines which recognize the set of squares. The bound on capacity is approached to within a factor of four by a specific machine which carries out a test based on the fact that every non-square is a quadratic non-residue of some rational prime. A machine which carries out a test based on the standard root-extraction algorithm is substantially less efficient in this respect. For neither machine is the bound on the capacity-time product closely approached.
ALOHA performs optimal power control in Poisson networks

This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We focus on the case where each transmitter knows its distance to its desired receiver but does not know the topology of the rest of the network. We study three sets of strategies: the single-node optimal power control (SNOPC) strategy, the Nash equilibrium power control (NEPC) strategy, and the globally optimal power control (GOPC) strategy. SNOPC strategies maximize the expected throughput of the power controllable link given that all the other transmitters do not use power control. Under NEPC strategies, no individual node of the network can achieve a higher expected throughput by unilaterally deviating from these strategies. The GOPC strategy maximizes the throughput of a typical node in the network. This paper shows that under mean and peak power constraints at each transmitter, all of the three strategies are ALOHA-type random on-off power control policies in bipolar networks. For links of iid random distances, we show both SNOPC and NEPC strategies are ALOHA-type random on-off policies. These results suggest that ALOHA can be viewed not only as a MAC scheme but also as an efficient and stable power control scheme.
Biomechanical modeling and sensitivity analysis of bipedal running ability. II. Extinct taxa

Using an inverse dynamics biomechanical analysis that was previously validated for extant bipeds, I calculated the minimum amount of actively contracting hindlimb extensor muscle that would have been needed for rapid bipedal running in several extinct dinosaur taxa. I analyzed models of nine theropod dinosaurs (including birds) covering over five orders of magnitude in size. My results uphold previous findings that large theropods such as Tyrannosaurus could not run very quickly, whereas smaller theropods (including some extinct birds) were adept runners. Furthermore, my results strengthen the contention that many nonavian theropods, especially larger individuals, used fairly upright limb orientations, which would have reduced required muscular force, and hence muscle mass. Additional sensitivity analysis of muscle fascicle lengths, moment arms, and limb orientation supports these conclusions and points out directions for future research on the musculoskeletal limits on running ability. Although ankle extensor muscle support is shown to have been important for all taxa, the ability of hip extensor muscles to support the body appears to be a crucial limit for running capacity in larger taxa. I discuss what speeds were possible for different theropod dinosaurs, and how running ability evolved in an inverse relationship to body size in archosaurs. J. Morphol. 262:441–461, 2004. © 2004 Wiley‐Liss, Inc.
Do Ads Influence Editors? Advertising and Bias in the Financial Media

We use mutual fund recommendations to test whether editorial content is independent from advertisers’ influence in the financial media. We find that major personal finance magazines (Money, Kiplinger’s Personal Finance, and SmartMoney) are more likely to recommend funds from families that have advertised within their pages in the past, controlling for fund characteristics like expenses, past returns and the overall levels of advertising. We find little evidence of a similar relationship for mentions in the New York Times or Wall Street Journal. Positive media mentions in both newspapers and magazines are associated with significant future inflows into the fund while advertising expenditures are not. Therefore, if we interpret our coefficients causally, a large share of the benefit of advertising in our sample of personal finance magazines comes via the apparent content bias. The welfare implications of this apparent bias are unclear, however, since our tests suggest that bias does not directly lead publications to recommend funds with significantly lower future returns than they might have recommended in the absence of any bias. In selecting funds to recommend, magazines overweight past returns relative to expenses, and as a group their recommendations do not outperform even an equal- weighted average of their peers. Nevertheless, this approach leaves magazines with large numbers of funds with high past returns from which to select, and so bias towards advertisers can be accommodated without significantly reducing readers’ future returns. Interestingly, the recommendations of Consumer Reports, which does not accept advertising, have future returns comparable to or below those of the publications which accept do advertising.
Tumor-targeted Gene Therapy for Nasopharyngeal Carcinoma 1

The unique feature of human nasopharyngeal carcinoma (NPC) is its almost universal association with the EBV, which is expressed in a latent form exclusively in cancer cells, and not in the surrounding tissues. We have exploited this differential by constructing a novel replication-deficient adenovirus vector (ad5.oriP) in which transgene expression is under the transcriptional regulation of the family of repeats domain of the origin of replication (oriP) of EBV. When EBNA1, one of the latent gene products of EBV, binds to the family of repeats sequence, this activates transcription of downstream genes. Vector constructs were made using the -galactosidase and luciferase reporter genes (ad5oriP. gal and ad5oriP.luc) or the p53 tumor suppressor gene (ad5oriP.p53). 5-Bromo4-chloro-3-indolyl-D-galactopyranoside staining demonstrated extensive expression only in EBV-positive NPC cells, specifically in response to the presence of EBNA1. The relative difference in expression between EBVpositive and -negative cell lines is approximately 1000-fold. This selective expression was corroborated in EBV-positive and -negative tumor models, along with an absence of transgene expression in the host liver. Significant cytotoxicity was achieved using the adv.oriP. p53 therapeutic gene only in EBV-positive NPC cells, which was enhanced with the addition of ionizing radiation. Cytotoxicity was mediated primarily by induction of apoptosis. These results demonstrate that the oriP sequence can achieve high levels of gene expression targeted specifically to EBV-positive NPC cells in the context of the adv vector. This has now provided the tumor-specific expression system from which additional interventions can be evaluated in future treatment strategies for patients with nasopharyngeal cancers.
Neural mechanisms tracking popularity in real-world social networks

Significance In virtually all human groups, differences in popularity induce social status and shape interactions. How do we recognize that certain individuals are popular—highly liked by the group—even when this collective preference differs from our own? Our results suggest that group members’ popularity is tracked by activity in neural valuation systems, which in turn engage social cognition systems that facilitate understanding others’ mental states. Popular participants’ valuation systems demonstrated enhanced sensitivity to differences among other group members’ popularity. These neural data offer insights into how status guides social behavior and reinforces social network structures, and why the affective valuation and social cognition systems are critical for navigating these networks and achieving high status within them. Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others’ popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets’ sociometric popularity, even when controlling for potential confounds. The target popularity–social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity–valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members’ popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.