Miracle-dz/UniFAR-FTUs huggingface dataset:
A performance comparison of control policies for slotted Aloha frequency-hopped multiple access systems

A performance analysis of slotted Aloha with frequency-hopped code-division multiple access is presented, with emphasis on the effects of the retransmission control policy. Static and dynamic control policies are considered. Performance using static control is shown to be highly sensitive to the relationship between the packet generation process and the chosen retransmission probability. Dynamic control, on the other hand, is very robust in that it provides high throughput over wide ranges of arrival rates. Delays are generally quite small when system parameters are properly set. A simple rule for computing the retransmission probability as a function of the backlog is provided and gives a result very close to the analytically derived optimal value. Numerical performance evaluation is given for the case of a network of 250 users and a spread factor (number of hopping positions) of 100, where the other system parameters are varied. <<ETX>>
Research of an Improved Hybrid Genetic Algorithm

The extensive ability of SGA surpasses its refinement ability. It is difficult to control the precision when we solve the GA-hard problems. This paper proposes a new hybrid genetic algorithm,e-HGAA. After finding the most optimal individual in each generation,it takes this optimal individual as the initial point to carry out local search in a fixed-radius region . The search results replace the worst individual or other individuals; and the algorithm enters the next generation operation again. This algorithm not only increases the solution precision,but also enhances the stability.
Neural mechanisms of cognitive control of motivated behavior

This thesis used functional magnetic resonance imaging and positron emission tomography of the brain to study neural correlates of cognitive control of human motivated behavior. Results showed that cognitive control (i) activates overlapping brain areas for the regulation of aversive emotions and rewards, (ii) relies on both local brain activation and global whole-brain interaction, and (iii) is linked with dopamine transmission in the striatum, which is aberrant in schizophrenia.
Partial Order Reduction in Parallel Model Checking

Hlavnim zaměřenim teto prace je technika redukce castecným uspořadanim pro paralelni LTL model checking. Přinos techniky spociva v jeji paralelni implementaci, poskytujici skutecně dynamickou heuristiku se sekvencni složitosti v O(n). Naproti tomu, tradicni techniku založenou na DFS nelze použivat s paralelnimi algoritmy a existujici heuristiky vhodne pro paralelni prostředi vyžaduji super-linearni cas nebo jsou přilis striktni, ve srovnani s sekvencnimi implementacemi. Tuto techniku uvažujeme v sirokem kontextu technologie a algoritmů pro paralelni, vysokovýkonný enumerativni LTL model checking, a to jak z hlediska teoretickeho tak i technickeho. K dispozici je zdrojový kod popsaneho systemu, zahrnujiciho kompletni model checker vhodný pro obecne použiti. Techniku redukce castecným uspořadanim doplňuje vylepsený on-the-fly algoritmus pro paralelni detekci cyklů, dale posouvajici hranice dostupne technologie. Tento algoritmus lze navic výborně kombinovat s navrhovanou technikou redukce.
A Hierarchical Method for Form Classification of Financial Document Images

Recently,financial document analysis and recognition is a hot research topic,and form classification is one of its fundamental parts. In this article,we introduce a hierarchical method for classifying financial documents using a binary tree decision.First,form classification is based on elastic matching of form structure shape.Then,OCR of document titles is performed.Thirdly,document color is re-confirmed.As a result,the sequent range of document types becomes more and more tighter.At last, the final decision of document types is performed by linear combination of the first two classifiers.Applications of financial document recognition systems based on this form classification method have been successfully and widely used.
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search

Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interest with respect to multiple aspects, how to extract and fuse users' long-term interest with short-term interest, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interest Extractor (SIE), Long-term Interest Extractor (LIE), Interest Fusion Module (IFM) and Interest Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's short-term interest by integrating three fundamental interest encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interest by devising an attention mechanism with respect to the short-term interest from SIE module. In IFM, the achieved long and short-term interests are further fused in an adaptive manner, followed by concatenating it with original raw context features for the final prediction result. Last but not least, considering the entangling characteristic of long and short-term interests, IDM further devises a self-supervised framework to disentangle long- and short-term interests. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of HIFN over state-of-the-art methods.
See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction

Many recommender systems optimize click through rates (CTRs) as one of their core goals, and it further breaks down to predicting each item's click probability for a user (user-item click probability) and recommending the top ones to this particular user. User-item click probability is then estimated as a single term, and the basic assumption is that the user has different preferences over items. This is presumably true, but from real-world data, we observe that some people are naturally more active in clicking on items while some are not. This intrinsic tendency contributes to their user-item click probabilities. Besides this, when a user sees a particular item she likes, the click probability for this item increases due to this user-item preference. Therefore, instead of estimating the user-item click probability directly, we break it down into two finer attributes: user's intrinsic tendency of clicking and user-item preference. Inspired by studies that emphasize item features for overall enhancements and research progress in multi-task learning, we for the first time design a Multi Classifier Click Rate prediction model (MultiCR) to better exploit item-level information by building a separate classifier for each item. Furthermore, in addition to utilizing static user features, we learn implicit connections between user's item preferences and the often-overlooked indirect user behaviors (e.g., click histories from other services within the app). In a common new-campaign/new-service scenario, MultiCR outperforms various baselines in large-scale offline and online experiments and demonstrates good resilience when the amount of training data decreases.
DuARUS: Automatic Geo-object Change Detection with Street-view Imagery for Updating Road Database at Baidu Maps

As the core foundation of web mapping, each geographic object (geo-object), such as a traffic sign, plays a vital role in navigation and intelligent driving. Determining how to obtain the latest high-precision geo-object information is a classic topic in updating road databases. Benefiting from the cost-effective attribute and availability of the positioning equipment and camera, the vision-based update pattern is becoming increasingly popular in the industry. Generally speaking, the road database update mainly includes three phases: geo-object recognition, localization, and change detection. Previous change detection strategies are mainly performed by comparing the historical road information (i.e., geo-object type and position) with the new geographic data of geo-objects collected from the street-view imagery. However, limited by the localization precision of the positioning equipment and the discriminative power of the vanilla differential-based method, the accuracy, recall, and efficiency of previous systems for geo-object change detection are greatly impaired. In addition, the artificially prescribed production standards make the geo-object position in the map data deviate from its position in the real world, as well as some geo-objects do not need to be updated (e.g., temporary speed limit), which further yields many false-positive detections and significantly increases the labor costs of existing systems. To address these challenges, we propose a novel framework called DuARUS for automatic geo-object change detection with street-view imagery. In this paper, we mainly focus on automatic geo-object localization and change detection. Specifically, for geo-object localization, we propose a two-stage, integrated localization algorithm based on image matching and monocular depth estimation. Furthermore, to achieve automatic change detection, vision-based representation learning and scene understanding strategies are introduced to build a large-scale geo-object semantic map, which can provide sufficient multimodal information support for change detection. Based on such artful modeling, we recast the complicated, labor-based change detection problem as a vanilla binary classification task, which is a robust and efficient strategy that contributes to resolving this problem. By combining these operations, we construct an industrial-grade, fully automatic production system for road database updates. Extensive experiments conducted on large-scale, real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of the system. Moreover, this system has already been deployed in production at Baidu Maps since July 2020, handling 96% of automatic road database updates. DuARUS improves the annual update mileage from millions to tens of millions, and it achieves weekly updates.
ML-1M++: MovieLens-Compatible Additional Preferences for More Robust Offline Evaluation of Sequential Recommenders

Sequential recommendation is the task of predicting the next interacted item of a target user, given his/her past interaction sequence. Conventionally, sequential recommenders are evaluated offline with the last item in each sequence as the sole correct (relevant) label for the testing example of the corresponding user. However, little is known about how this sparsity of preference data affects the robustness of the offline evaluation's outcomes. To help researchers address this, we collect additional preference data via crowdsourcing. Specifically, we propose an assessment interface tailored to the sequential recommendation task and ask crowd workers to assess the (potential) relevance of each candidate item in MovieLens 1M, a commonly used dataset. Toward establishing a more robust evaluation methodology, we release the collected preference data, which we call ML-1M++, as well as the code of the assessment interface.
Higher Education Professionals Navigating Anti-Immigration Policy for Undocumented Students

Higher education professionals are critical to undocumented students’ educational success as their access to higher education is heavily dependent on being able to interpret and navigate policies. This study presents an analysis of policies that impact higher education access for undocumented students coupled with a qualitative case study that examines how anti-immigration policy affects the experiences of higher education professionals who worked directly with undocumented students in the state of Indiana. The authors help readers understand the experiences and challenges facing these “pathfinders” as they attempt to navigate the mazes of federal, state, and institutional policy to help their undocumented students.
Relationship between attachment styles and cluster B Personality disorders among adult males and females diagnosed with the disorder in Qeshm Island, Iran

The current study attempted to examine the relationship between the three attachment styles (secure, anxious/ambivalent, and avoidant) and the four Cluster B personality disorders (borderline, antisocial, narcissistic, and histrionic) among males and females diagnosed with the disorder in the island of Qeshm, Iran. A total of 80 adult male and female patients diagnosed with Cluster B personality disorder, aged between 18 and 65 years, were referred by 28otmail28ed28 centers, psychiatric clinics, and hospitals in Qeshm Island, Iran for participation in this research study. The participants were asked to complete the Adult Attachment Inventory (AAI) and the Millon Clinical Multiaxial Inventory (MCMI). Data analysis involved both descriptive and inferential statistics including reliability analysis, means, and standard deviations for the computed factors, GLM multivariate analysis of variance (MANOVA) to test for gender differences, and multiple regression analysis. The results revealed that none of the three attachment styles significantly predicted any of the four Cluster B personality disorders. Additionally, no overall gender difference was found in the seven variables combined. Implications, limitations, and avenues for future research are presented.
Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework.

Measuring Influence and Topic Dependent Interactions in Social Media Networks Based on a Counting Process Modeling Framework by Donggeng Xia Advisor: Professor George Michailidis Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as well as structured data, such as time stamps and interactions between users. Some key questions for such platforms are (i) to determine influential users, in the sense that they generate interactions between members of the platform and (ii) identifying important interactions between nodes in the corresponding user network. Regarding the first question, common measures used both in the academic literature and by companies that provide analytics services are primarily variants of the popular web-search PageRank algorithm applied to networks that capture connections between users. In this work, we develop a modeling framework using multivariate interacting counting processes to capture the detailed actions that users undertake on such platforms, namely posting original content, reposting and/or mentioning other users’ postings. Based on the proposed model, we also derive a novel influence measure. We discuss estimation of the model parameters through maximum likelihood and establish their asymptotic properties. The proposed model and the accompa-
Mindfulness in a school setting: considering youth who have experienced trauma

Background: Trauma is a common childhood experience, with two-thirds of children reporting at least one experience with trauma by the age of sixteen. Teachers often misidentify trauma symptoms as ADHD or behavioral problems within the classroom. At the same time, school-based Mindfulness interventions have increased in popularity in an attempt by school administrators to improve classroom behavior and student performance. Purpose: Mindfulness is a longstanding practice with roots in the Buddhist religion. The practice is a way of living, to focus on the present moment in a non-judgmental way. The practice has been adapted for clinical use with both children and adults, due to the value seen by clinicians, to cope with challenging cognitions and behaviors. The purpose of this literature review is to synthesize information about childhood trauma, Mindfulness interventions, and implementation of school-based Mindfulness programs. Methods: A literature review was conducted within the databases of ProQuest and MedLine. Results were restricted to peer-reviewed journal articles, full text, publications within the last ten years, school-age children (grade K-12 or age 6-18), typical school settings, and articles written in English. Boolean operations were applied to search for articles related to Mindfulness in the school setting. Results: Fourteen articles were selected from the literature search after meeting the search criteria, including qualitative studies, mixed methods studies, program evaluations, literature reviews, and randomized controlled trials. Results indicated improvements in self-regulation, attention, concentration, rumination, and classroom behavior. Therefore, findings revealed Mindfulness to be effective in reducing behaviors of interest to teachers. Only one study considered childhood trauma as a potential reason for student behavioral and emotional problems in the classroom. Children with a history of trauma can experience discomfort and become retraumatized through improper Mindfulness practice. Given the increasing use of school-based Mindfulness programs and the prevalence of childhood trauma, these results have public health significance, revealing a gap in research. There is a need for research that considers childhood trauma when implementing school-based Mindfulness programs.
A new embedded zeroree wavelet image coding algorithm

In this paper, a new embedded zerotree wavelet image coding algorithms is presented, in which the bit allocation is changed, the zerotree set is improved and the partition-sorting strategy is revised. The experimental results show that our proposed coding method performs better than that of SPIHT in the aspects of PSNR, encoding time and decoding time.
Information Rigidity in Growth Forecasts; Some Cross-Country Evidence

We document information rigidity in forecasts for real GDP growth in 46 countries over the past two decades. We investigate: (i) if rigidities are lower around turning points in the economy, such as in times of recessions and crises; (ii) if rigidities differ across countries, particularly between advanced countries and emerging markets; and (iii) how quickly forecasters incorporate news about growth in other countries into their growth forecasts, with a focus on how advanced countries‘ growth forecasts incorporate news about emerging market growth and vice versa.
Detection of Tricking Attack and Localization OfDeceivers In Multiple Wireless Networks

Wireless Tricking attack means it is the situation in which one person or program masquerading another for illegitimate advantage. Here person is not authorized but pretend to be authorized for gaining illegal access. Because of this attack network performance is reduced and openness of wireless transmission medium, adversaries can monitor any transmission. In existing system cryptographic mechanisms are used for detecting this tricking attack but it is not always desirable because of the overhead of requirements and maintance mechanisms. In this paper spatial correlation of received signal strength (RSS) inherited from wireless nodes is used to detect the tricking attacks. And also an integrated detection and localization system is developed that can localize the positions of multiple attackers. This paper is to address the problem in wireless networks that happens by tricking attack. In existing system only detection of tricking attack is occurred. But in our scheme localization and prevention of tricking attacks is occurred.
Lip synchronisation for the iCub talking head

Publisher Rights Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
El liderazgo distribuido desde la perspectiva del análisis de redes: una experiencia en escuelas vulnerables y efectivas de Chile

El presente estudio es una de las primeras aproximaciones a la distribución de liderazgo en organizaciones escolares de Chile. Esta perspectiva intenta superar las visiones tradicionales al comprender este factor como una función que se distribuye en la organización y por ello puede ser ejercida, no sólo en forma tradicional por el Director o Directora, sino por varios actores lo que permite ampliar sus posibilidades. Se trata de una muestra de 51 escuelas primarias vulnerables, de diversas regiones, que en periodos consecutivos lograron destacarse en la prueba SIMCE (Prueba estandarizada de aprendizajes de Chile), entre sus similares. La metodología utilizada es también novedosa en nuestro contexto, pues se utiliza el “Análisis de Redes Sociales” (ARS), que permite recoger las relaciones de influencia reales que ocurren en la organización. Se presentan los resultados en casos seleccionados, para recoger las relaciones de influencia en los temas de Gestión directiva y Gestión Pedagógica.
Consumer Perceptions of Fruit and Vegetable Origin, Growing Methods, and Willingness to Pay in Trinidad and Tobago Marketplaces: Implications for Extension Programming

Consumer behavior is a complex phenomenon encompassing internal, external, and situational factors. This study examined perceptions of market consumers about fruits and vegetables in Trinidad and Tobago in terms of produce origin, growing method, and willingness to pay. A stratified purposive sample of consumers at 14 unique market locations was surveyed to measure the three constructs and demographics. Descriptive statistics, correlational analysis, a ttest, and one-way analysis of variance were used to analyze the data. Findings revealed consumers have positive perceptions of locally grown produce and produce grown without chemicals. Findings also revealed a slight willingness to pay more for such characteristics. There were small to moderate correlations among the three constructs. Male and female perceptions of locally grown produce were significantly different, but no differences were found based on age. Extension educators working with producers who sell directly to consumers can utilize results from this study in working with clientele to tailor marketing and production strategies. Further research into social norms and perceived behavior control is recommended to better understand consumer behavior and help Extension better prepare stakeholders for success in the market places.
Research about Personalized Service Technology in Digital Library by Using Content-Based Filtering

In application of digital library the personalized service can offer retrieval result satisfying user's interests.This paper offers one content-based filtering technique to digital library personalized service.By building user's need model and content feature model in vector space and caculating their similarity,the texts which don't satisfy user's interests will be filtered.In addition,the paper detailedly represents the process of building the model and the algorithm of personalized content-based filtering.At last,some practical results in application on digital library are given.