Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Phones and Outdoor Advertising: Measurable Advertising

Television and newspapers sit at the top of many agency marketing plans, while outdoor advertising stays at the bottom. The reason for this is that it’s difﬁcult to account for who views a billboard, so there is no way of consistently determining the effectiveness of outdoor advertising. As a result, agencies do not consider the medium and allocate their money elsewhere. To change this situation, one needs to create new credible audience measurements for the outdoor marketing industry. Here we propose a new way of performing audience measurements that combines mobile phone location estimations with information freely available on the Internet. We show that it is possible to estimate the number of people who drive or walk by a given area in Greater Boston from location estimations of a large fraction of mobile phone users in the region. We also infer the preferences for social events of the users by combing their location estimations with Internet listings of social events. This makes it possible to proﬁle areas based on their residents’ interests and dynamically change displayed advertising based on those assessments.
Design and Implementation of a Communication-Efficient Data-Parallel Program Compiling System

KUEI-PING SHIH, CHING-YING LAI*, JANG-PING SHEU* AND YU-CHEE TSENG+ Department of Computer Science and Information Engineering Tamkang University Tamshui, Taipei, Taiwan 251, R.O. C. E-mail: kpshih@mail.tku. edu.tw Department of Computer Science and Information Engineering National Central University Chungli, Taoyuan, Taiwan 320, R.O. C. E-mail: sheujp@csie.ncu. edu.tw Department of Computer Science and Information Engineering National Chiao Tung University Hsinchu, Taiwan 300, R.O. C. E-mail: yctseng@csie.nctu. edu.tw
THE NAMING OF THINGS AND THE CONFUSION OF TONGUES

If we accept language as an endogenous system, then we can start discussing representations for language processing which have a basis in endogenous systems. This paper is a start in the direction of showing how such a model might be constructed – drawing from across disciplines such as psychology, psycholinguistics and neuroscience. What we propose, and show the start of, is a model of lexical items which rather than being a list of words and features, is a system of evidence points. We outline the underlying technologies supporting this and describe what each will bring to the model. We believe this model will better support data analysis where language values are involved and show how it could work in cross-language information analysis. The first area of application involves named entities – people, places, associations – which are frequently necessary intelligence analysis data points, but which have confounded systems designed to automatically incorporate them.
Indexing the Distance : An EÆcient Method to KNN Processing

In this paper, we present an eÆcient method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional space. iDistance partitions the data and selects a reference point for each partition. The data in each cluster are transformed into a single dimensional space based on their similarity with respect to a reference point. This allows the points to be indexed using a B-tree structure and KNN search be performed using onedimensional range search. The choice of partition and reference point provides the iDistance technique with degrees of freedom most other techniques do not have. We describe how appropriate choices here can e ectively adapt the index structure to the data distribution. We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its e ectiveness.
Moving Horizon Estimation based on finite impulse response models

In this paper we develop a moving horizon constrained regularized l2 estimator based on finite impulse response models. By numerical examples we illustrate the performance of the estimator for a system with measurement noise. Also the closed-loop performance of a model predictive controller consisting of the estimator and a predictive regulator is investigated. The moving horizon estimator is no panacea to MPC and the closed-loop performance is strongly correlated to the level of measurement noise and the desired speed of response.
Causal Discovery via MML

Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate re ections of the original models and compare favorably with those of TETRAD II (Spirtes et al. 1994) even when it is supplied with prior temporal information and MML is not.
Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues

Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much they think they know. Across seven critical issues that enjoy substantial scientific consensus, as well as attitudes toward COVID-19 vaccines and mitigation measures like mask wearing and social distancing, results indicate that those with the highest levels of opposition have the lowest levels of objective knowledge but the highest levels of subjective knowledge. Implications for scientists, policymakers, and science communicators are discussed.
Compressive detection and localization of multiple heterogeneous events with sensor networks

This paper considers the crucial problem of event detection and localization with sensor networks, which not only needs to detect occurrences but also to determine the locations of detected events and event source signals. It is highly challenging when taking several unique characteristics of real-world events into consideration, such as simultaneous emergence of multiple events, overlapping events, event heterogeneity and stringent requirement on energy efficiency. Most of existing studies either assume the oversimplified binary detection model or need to collect all sensor readings, incurring high transmission overhead. Inspired by spatially sparse event occurrences within the monitoring area, we propose a compressive sensing based approach called CED, targeting at multiple heterogeneous events that may overlap with each other. With a fully distributed measurement construction process, our approach enables the collection of a sufficient number of measurements for compressive sensing based data recovery. The distinguishing feature of our approach is that it requires no knowledge of, and is adaptive to, the number of occurred events which is changing over time. We have validated the signal attenuation event model through testbed experiments with TelosB motes. Extensive simulation results demonstrate that our approach can achieve high detection rate and localization accuracy while incurring modest transmission overhead.
HOW DOES THE TRANSFORMATION OF AN AVATAR FACE GIVING A FAVORABLE IMPRESSION AFFECT HUMAN RECOGNITION OF THE FACE?

: We investigated how different appearances in the favorable impressions of 3D avatar faces affect face-recognition performances by humans. We conducted an encoding and testing experiment using synthesized facial images and artificially manipulated the strength of the perceived impressions in three different dimensions. We also subjectively assessed the favorability of the synthesized faces that were used as visual stimuli in face- recognition tests and found that facial transformation, which decreasedthe favorability impressions, generally deteriorates human face-recognition performance.
A NEW ATTITUDE-BEHAVIOUR (AB) THEORY FOR ACCEPTABLE LEADERS IN WINNING ORGANIZATIONS

The organizational fate depends on a committed effective decision maker who can predict the future business situations based on various affecting parameters on the organizational business. The right decisions taken at right time can elevate an organization as winning organization and the decision maker can transform himself as an acceptable leader. Such decisions can also decide the fate of the organization along with the livelihood of its employees. A winning leader is an asset of an organization and the employees are directly and indirectly get the benefits of such leader. If the leader fails in predicting the future of a profit oriented organization or not for profit organizations, the employees are directly going to be the victims of such wrong decisions. Leaders should able to make correct judgement while taking decisions based on effective predictions by using predictive analysis framework. The attitude of the leader plays an important role while making decisions on solving organizational/individual pro blems. In Simon’s model of decision making, the attitude of the leader decides his ability of identifying a problem, finding alternative solutions, analysing these alternatives to find optimum alternative, and finally to implement the best alternative to practically solve the problem. Attitude is the mental status of a human being which represents the emotions based on his/her feelings at a given time and controls his/her instantaneous behaviour. In this paper, we have developed a theory on winning leaders actions based on their behaviour in organizations. It is argued that the behaviour of a leader depends on his/her attitude which may be positive or negative depends on the four factors identified as feelings, emotions, belief, and environment. Thus a supportive, effective, good environment creates positive attitude and hence winning leaders. The bad environment supports negative attitude due to wrong belief, negative emotions, and frustratiive feelings which are again depends on the present and past environment of the decision maker.
Classification of outdoor 3D lidar data based on unsupervised Gaussian mixture models

3D point clouds acquired with lidars are an important source of data for the classification of outdoor environments by autonomous terrestrial robots. We propose here a two-layer classification system. The first layer consists of a Gaussian mixture model, issued from unsupervised training, which defines a large set of data-oriented classes. The second layer consists of a supervised, manual grouping of the unsupervised classes into a smaller set of task-oriented classes. Because it uses unsupervised learning at its core, the system does not require any manual labelling of datasets. We evaluate the system on two datasets of different nature, and the results show its capacity to adapt to different data while providing classes which are exploitable in a target task.
PIMBALL : Binary Neural Networks in Spintronic Memory

SALONIK RESCH, University of Minnesota, Twin Cities S. KAREN KHATAMIFARD, University of Minnesota, Twin Cities ZAMSHED IQBAL CHOWDHURY, University of Minnesota, Twin Cities MASOUD ZABIHI, University of Minnesota, Twin Cities ZHENGYANG ZHAO, University of Minnesota, Twin Cities JIAN-PING WANG, University of Minnesota, Twin Cities SACHIN S. SAPATNEKAR, University of Minnesota, Twin Cities ULYA R. KARPUZCU, University of Minnesota, Twin Cities
Syntactic Structure and Artificial Grammar Learning : The Learnability of Embedded Hierarchical Structures

Embedded hierarchical structures, such as “the rat the cat ate was brown”, constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca’s area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca’s area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated.
Extreme programming evaluation framework for object-oriented languages version 1.4

The Extreme Programming (XP) software development methodology appears appropriate for some teams or projects. Anecdotal stories from developers who have adopted XP are often favorable. However, there is little empirical evidence to support or refute claims regarding XP. The Extreme Programming Evaluation Framework (XP-EF) provides a benchmark measurement framework for researchers and practitioners to assess concretely the extent to which an organization has adopted XP practices and the result of this adoption. Through a series of case studies that utilize the structure of the XP-EF, the community can build an experience factory of empirical findings on XP; the results of these case studies can be integrated and combined. This paper describes the elements of the XP-EF and provides examples of using it to measure a software project.
An Adaptive Cache Coherence Protocol Optimized For Migratory Sharing

Parallel programs that use critical sections and are executed on a shared-memory multiprocessor with a write-invalidate protocol result in invalidation actions that could be eliminated. For this type of sharing, called migratory sharing, each processor typically causes a cache miss followed by an invalidation request which could be merged with the preceding cache-miss request. In this paper we propose an adaptive protocol that invokes this optimization dynamically for migratory blocks. For other blocks, the protocol works as an ordinary write-invalidate protocol. We show that the protocol is a simple extension to a write-invalidate protocol. Based on a program-driven simulation model of an architecture similar to the Stanford DASH, and a set of four benchmarks, we evaluate the potential performance improvements of the protocol. We find that it effectively eliminates most single invalidations which improves the performance by reducing the shared access penalty and the network traffic.
Characterization of doxorubicin nanoparticles prepared by ionic gelation

Purpose: To prepare and characterise doxorubicin nanopatrticles and study their drug delivery in breast cancer.Methods: Doxorubicin nanoparticles were prepared by ionic gelation method using sodium alginate as polymer. The formulations were optimized by cross-linking CaCl2 with sodium alginate at different concentrations. Zeta sizer Nano ZS (UK) was used to determine the mean particle size distribution of the nanoparticle preparations. The shape and external morphologies of the nanoparticles were evaluated by scanning electron microscopy (SEM). Drug release was determined and kinetic release analysis was applied to determine the mechanism of drug release.Results: Entrapment efficiency and mean particle size values were correlated. Scanning electron micrographs showed that the nanoparticles were spherical with little irregularity but without cracks. Doxorubicin release from the sodium alginate nanoparticles followed Korsmeyer-Peppas model which suggest that drug release from the nanoparticles was by diffusion and dissociation from the natural polymer matrix.Conclusion: The doxorubicin-loaded nanoparticles showed concentration-dependent increases in entrapment efficiency. The nanoparticles displayed anticancer properties in breast cancer cell line, thus indicating its potential fo chemotherapeutic application.Keywords: Doxorubicin, Ionic gelation, Nanoparticles, Sodium alginate, Drug release mechanism, Anticancer
Sparse subspace clustering via smoothed (cid:96) p minimization

In this letter, we formulate sparse subspace clustering as a smoothed ‘ p (0 < p < 1) minimization problem (SSC-SLp) and present a uniﬁed formulation for di ﬀ erent practical clustering problems by introducing a new pseudo norm. Generally, the use of ‘ p (0 < p < 1) norm approximating the ‘ 0 one can lead to a more e ﬀ ective approximation than the ‘ 1 norm, while the ‘ p -regularization also causes the objective function to be non-convex and non-smooth. Besides, better adapting to the property of data representing real problems, the objective function is usually constrained by multiple factors (such as spatial distribution of data and errors). In view of this, we propose a computationally e ﬃ cient method for solving the multi-constrained non-smooth ‘ p minimization problem, which smooths the ‘ p norm and minimizes the objective function by alternately updating a block (or a variable) and its weight. In addition, the convergence of the proposed algorithm is theoretically proven. Extensive experimental results on real datasets demonstrate the e ﬀ ectiveness of the proposed method.
Question answering using maximum entropy components

We present a statistical question answering system developed for TREC-9 in detail. The system is an application of maximum entropy classification for question/answer type prediction and named entity marking. We describe our system for information retrieval which did document retrieval from a local encyclopedia, and then expanded the query words and finally did passage retrieval from the TREC collection. We will also discuss the answer selection algorithm which determines the best sentence given both the question and the occurrence of a phrase belonging to the answer class desired by the question. A new method of analyzing system performance via a transition matrix is shown.
CloudCluster: Unearthing the Functional Structure of a Cloud Service

In their quest to provide customers with good tools to manage cloud services, cloud providers are hampered by having very little visibility into cloud service functionality; a provider often only knows where VMs of a service are placed, how the virtual networks are conﬁgured, how VMs are provisioned, and how VMs communicate with each other. In this paper, we show that, using the VM-to-VM trafﬁc matrix, we can unearth the functional structure of a cloud service and use it to aid cloud service management. Leveraging the observation that cloud services use well-known design patterns for scaling ( e.g., replication, communication locality), we show that clustering the VM-to-VM trafﬁc matrix yields the functional structure of the cloud service. Our clustering algorithm, CloudCluster, must overcome challenges imposed by scale (cloud services contain tens of thousands of VMs) and must be robust to orders-of-magnitude variability in trafﬁc volume and measurement noise. To do this, CloudCluster uses a novel combination of feature scaling, dimensionality reduction, and hierarchical clustering to achieve clustering with over 92% homogeneity and completeness. We show that CloudCluster can be used to explore opportunities to reduce cost for customers, identify anomalous trafﬁc and potential misconﬁgurations.
Synthesizing weather forecasts from formated data

This paper describes a system (RAREAS) which synthesizes marine weather forecasts directly from formatted weather data. Such synthesis appears feasible in certain natural sublanguages with stereotyped text structure. RAREAS draws on several kinds of linguistic and non-linguistic knowledge and mirrors a forecaster's apparent tendency to ascribe less precise temporal adverbs to more remote meterological events. The approach can easily be adapted to synthesize bilingual or multi-lingual texts.
Multi-Task Mutual Learning for Joint Sentiment Classiﬁcation and Topic Detection

—Recently, advances in neural network approaches have achieved many successes in both sentiment classiﬁcation and probabilistic topic modeling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classiﬁcation accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classiﬁers could carry word-level polarity information and can be used to guide the discovery of topics in topic modeling. This paper proposes a multi-task learning framework which jointly learns a sentiment classiﬁer and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in sentiment classiﬁers through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classiﬁcation and topic modeling. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
Decision Tree Classification Model In Water Supply Network

With the service life of water supply network (WSN) growth, the growing phenomenon of aging pipe network have become exceedingly serious. Urban water supply network is a hidden underground asset, therefore, it is difficult for monitoring staff to make a direct classification towards the faults of pipe network by means of the modern detecting technology. In this paper, based on the basic property data (e.g. diameter, material, pressure, distance to pump, distance to tank, load, etc.) of water supply network (WSN), decision tree algorithm (C4.5) can be carried out to classify the specific situation of water supply pipeline. Part of the historical data is to establish a decision tree classification model, then the remaining historical data is to validate this established model. Adopting statistical method is to assess the decision tree model including Basic Statistical Method (BSM), Receiver Operating Characteristic (ROC)). These methods can be successfully used to assess the accuracy of this established classification model of water pipe network. The purpose of classification model is to classify the specific condition of water pipe network. It is important to maintain the pipeline according to the classification results including asset unserviceable(AU)、near perfect condition(NPC) and serious deterioration(SD). Finally, this research focuses on pipe classification which plays a significant role in maintaining water supply networks in the future.
Filling the gap between web 2 . 0 technologies and natural language processing pipelines with semantic web the semtoolbox approach

As web 2.0 websites are now widespread and natural language proposes mature processing methods, using the later to enrich the former opens new possibilities to imp rove interactions. This paper will show a way of combini ng natural language processing tools with semantic web to enha nce tagging and mashup functionalities of web 2.0 websi tes.
Research on Text Proofreading Method for Judgment Document

Automatic proofreading of judgment document can effectively overcome human factors and ensure the quality of proofreading. The outstanding work in this paper includes following two aspects: The first aspect is to proofread the mistakes of legal file name and legal provision reference by establishing the proofreading knowledge base of judgment document. The second aspect is to maximizing identify legal terminology and common name entities, then proofread the collocation mistakes between words, phrases and legalese by using Markov Model. The experiment results show that this method can basically meet the needs of the judgment documents proofreading.