Miracle-dz/UniFAR-FTUs huggingface dataset:
A new approach for instance-based schema matching

Schema matching is a crucial phase in data integration that aims to find correspondences between schema attributes by utilizing schema information. However, this information is not always available or useful to be used since it could be abbreviation. Consequently, instances could be an alternative choice for schema information. Various instance based schema matching approaches have been proposed to achieve the goal of discovering correspondences between schema attributes, by treating the instances as strings including the numeric instances. This prevents discovering common patterns or performing statistical computation among the numeric instances. As a consequence, this causes unidentified matches especially for attribute with numeric instances which further reduces the quality of match results. This thesis aims at proposing an efficient approach which is able to identify attribute matches between schemas by fully exploiting the instances. The approach utilizes the concept of pattern recognition to determine attribute matches for numeric and mix instances. This is acquired by automatically creating regular expression based on the instances. While, for alphabetic instances the approach calculates the semantic similarity score by utilizing Google similarity to capture the semantic relationships between instances. The proposed approach consists of five main phases, namely: (i) analysing instances, (ii) classifying schema attributes, (iii) extracting the optimal sample size, (iv) identifying instance similarity, and (v) identifying the match. Three analyses have been designed and conducted on two different data sets, namely: (i) Restaurant and (ii) Census, with respect to precision (P), recall (R), and F-measure (F). The first analysis aims at identifying the optimal sample size of tuples to be used during the phase of extracting the optimal sample size. The purpose of identifying the optimal sample size is to reduce the number of comparisons between the instances which lead to reduce the processing time of matching operation. This analysis showed that the optimal sample size is 50% from the actual table size of both data sets. The second analysis aims to investigate and to prove that combining both Google similarity and regular expression as in our proposed approach achieve higher accuracy compared to utilizing Google similarity or regular expression separately. The results showed that our proposed approach achieved precision (P), recall (R), and F-measure (F) in the range of 93% - 99% for both data sets. On the other hand, Google similarity and regular expression which are performed separately achieved precision (P), recall (R), and F-measure (F) in the range of 36% - 74%. While the third analysis intents to compare the performance of our proposed approach to the previous approaches. The results showed that our proposed approach outperformed the previous approaches although only a sample of instances is used instead of considering the whole instances during the process of instance based schema matching as used in the previous works.
A New Buffer and Energy Based Scheduling Scheme for Supporting QoS in MANETs

Today, the original IEEE 802.11 standard has several problems in providing Quality of Service in MANETs. A single FIFO queue is used in best effort manner and it does not support QoS. The upcoming IEEE 802.11e was drafted to overcome these drawbacks. In this paper we describe a new multiple queuing system with an adaptive scheduling taking into account the states of buffers and energy consumption in a mobile ad hoc network. The proposed scheduling scheme uses dynamic weights for each queue. We study the performance of this scheme and compare it with the original IEEE 802.11b and the upcoming IEEE 802.11e. We show through simulations that the proposed buffer and energy based scheduling scheme improves overall end-to-end throughput, and gives better results than the original 802.11b and the Enhanced Distributed Coordination Function (EDCF), in terms of delay and total received and lost packets, as well as support service differentiation over multi-hop ad hoc networks.
REVIEW ON POWER QUALITY IMPROVEMENT BY POWER FILTER’S

In recent year power system is turn towards smart grid and micro grid i.e renewable energy sources are integrated with utility grid system and hence various power electronics and non-linear lodes are used which leads to power quality problems in power system. the critical issue of power quality problem is harmonics present in power systems so for power quality improvement various power filters are used. this paper I m going to review recently published paper in ieee on power quality improvement by using various power filter and control strategies. As we know there are mainly three type of power filter are available to reduced THD, which are active power filter ,passive power filter and hybrid power filter and recently used inductively active power filter. all researcher trying to reduced THD below 2% and by using various type controller such as P,PI,PID controller, Hysteresis current control, fuzzy logic controller, adaptive controller, PSO for MPPT,ANN Techniques,UPQC, SMC, least squares harmonic extraction technique. Also various custom power devices such as DSTATCOM, DVR, etc
Teaching Couple Relationship Education: The Influence on Regional Extension Agents and Family Life Educators

Couple Relationship Education (CRE) programs are a prevention resource used to assist adult individuals, couples, and families reduce relationship distress and improve prosocial behaviors. Regional Extension agents (REAs) and other community family life educators (FLEs) who teach CRE are involved in a facilitation process that develops relationships and rapport with their participants similar to other helping relationships. To date, no published research has focused on how CRE may affect the REAs/FLEs who provide the programs through community adult education programs. Informed by relational-cultural theory and the ecological systems perspective, the current study focused on assessing change in ten outcomes measuring REAs/FLEs’ (N = 54) individual, couple, and co-parenting functioning and whether that change differed by gender. Results from repeated measure ANCOVAs indicate REAs/FLEs experience gains across several domains of functioning. There were no differences by gender. Implications for REAs/FLEs and suggestions for future research are described.
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.
BURDUR İLİ GELECEKTEKİ RÜZGÂR HIZI DEĞERLERİNİN YAPAY SİNİR AĞLARI METODU İLE TAHMİNİ

Bu calismada, Burdur ilinin gelecekteki ruzgâr hizi degerleri yapay sinir aglari (YSA) metodu kullanilarak tahmin edilmistir. Hesaplamalar icin gerekli olan ruzgâr hizi degerleri Turkiye Meteoroloji Enstitusunden alinmistir. Calisma sonucunda, gelecekteki ruzgâr hizi degerlerinin hesaplanabilmesi amaciyla formul turetilmistir. Bu calismanin Burdur ilinde yapilmasi planlanan ruzgâr enerjisi calismalarina yardim edebilecegi dusunulmektedir.
Generating Useful Network Features from Social Networks for Web 2.0 services

Recently, many Web 2.0 services such as social networking services, blogs, and collaborative tagging have become widely popular. The notable feature of the Web 2.0 systems is that users mutually interact through a social network among users with attributional data of each user. The data mining community has executed various studies related to relational data mining such as link mining and statistical relational learning. However, analyzing a social network with attributional data is sometimes not an easy task because numerous ways exist to define features through aggregation of dierent tables. In this study, we propose an algorithm to identify important network-based features systematically from a given social network to analyze user behavior eciently and to expand the services. For example, our algorithm can provide useful indices for recommendation of products, communities, and friends. The task can be considered as link-based classification and link prediction. We first define general operators that are applicable to a social network. Then the combinations of the operators provide dierent features, some of which correspond to traditional indices; others are considered new. We apply our method for link-based classification and link prediction tasks with two dierent datasets, i.e., an @cosme (an online viral marketing site) dataset and a Hatena Bookmark (collaborative tagging service) dataset, thereby demonstrating the eectiveness of our approach. Results show that our algorithm can achieve better performance on link-based classification and link prediction than existing algorithms by generating useful network-based features. Our algorithm is general and can provide useful network-based features for various Web 2.0 services.
Users' Beliefs Toward Technology: A Social Capital Perspective

IT acceptance is generally considered a mature research field. However, we are persuaded that current approaches are underestimating the importance of social factors and group influence on individual beliefs. This paper adopts a complementary approach to previous researches based on normative assumptions. A social capital perspective is embraced to allow us to focus on the nature and quality of relationship among group members, especially those related to IT adoption. A theoretical model is proposed in order to have a usable theory. Empirically testable propositions are drawn, together with theoretical implications and possible future researches.
Effects of User Tastes on Personalized Recommendation

Based on a weighted projection of the user-object bipartite network,the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm are studied,where a user tastes or interests are defined by the average degree of the objects he has collected.It is assumed that the initial recommendation power located on the objects should be determined by both of their degree and the users tastes.By introducing a tunable parameter,the user taste effects on the configuration of initial recommendation power distribution are investigated.The numerical results show that the presented algorithm could improve the accuracy,measured by the average ranking score,more importantly.When the data is sparse,the algorithm should give more recommendation power to the objects whose degrees are close to the users tastes,while when the data becomes dense,it should assign more power on the objects whose degrees are significantly different from user's tastes.
Link Analysis of VANET in Interchanges of Expressway

In recent years, the Vehicular Ad-hoc Networks(VANET) is growing rapidly. There are many applications of the VANET. Especially, the VANET is applied to in the urban-roads and expressways. In this paper, a city is considered as well as the expressway scenario while the analzing the communiation between the street-aware information with vehicles. The length of the vehicles is considered to analyze the communication among cars including those not only waiting at the traffic square but also passing through after obtaining the traffic signal clearly in the interchange. We describe the scenario of interchange of expressway and utilizes the simulation in order to provide useful information.The result of the experiment analysis shows that we simulate average end-to-end delay and packet delivery ratio in the interchange of expressway. We will combine different theories of vehicle in the future work.
Science of Cyber-Security

JASON was requested by the DoD to examine the theory and practice of cyber-security, and evaluate whether there are underlying fundamental principles that would make it possible to adopt a more scientific approach, identify what is needed in creating a science of cyber-security, and recommend specific ways in which scientific methods can be applied. Our study identified several sub-fields of computer science that are specifically relevant and also provides some recommendations on further developing the science of cyber-security.
Identifying opportunities for co-production of commodities and water quality improvements in agricultural landscapes in the US Cornbelt

One of the most striking features of the US Cornbelt is the degree to which it has been designed, constructed, and intricately managed by humans for the production of agricultural products – primarily row-crop corn and soybeans. The production of provisioning ecosystem services, such as row-crop corn and soybeans, often comes at a tradeoff to other ecosystem services, such as enhanced water quality for aquatic life, recreation, and human consumption. Best management practices (BMPs) are tools that landowners can implement and manage as a way to co-produce row-crop corn and soybeans and enhanced water quality in extensively managed agricultural landscapes. Yet, selecting how and where to locate BMPs to impact aggregate ecosystem service outcomes, such as enhanced water quality, is a complex decision-making process that often involves multiple stakeholders and objectives. This research evaluates opportunities to improve the efficiency of BMP placement and management within and among crop fields and within watersheds to co-produce provisioning and regulating ecosystem services (ES), specifically row-crop corn and soybeans and enhanced water quality, in the US Cornbelt. This dissertation includes three distinct studies across three, nested scales (sub-field, field, and watershed) to examine environmental, economic, and social opportunities and tradeoffs associated with the implementation and management of BMPs designed to reduce nitrate loss to surface waters in central Iowa. To assess nitrate-nitrogen (hereafter nitrate) retention in vegetative BMPs, the relationship between nitrate retention and plant species composition was examined in nine different monocultural and polycultural communities in a plot-based experiment. Results show a small, but significant difference between monocultural and polycultural
The Diffusion of ICT for Corruption Detection in Open Government Data

Corruption occurs in many places within the government. To tackle the issue, open data can be used as one of the tools in creating more insight into the government. The premise of this paper is to support the notion that data opening can bring up new ways of fighting corruption. The current paper aimed at investigating how open data can be employed to detect corruption. This open data is trivial due to challenges like information asymmetry among stakeholders, data might only be opened partly, different sources of data need to be combined, and data might not be easy to use, might be biased or even manipulated. The study was conducted using a literature review approach. The reviews implied that corruption can be detected using Open Government Data, Thus, by conducting the open data technique within the government, the public could monitor the activities of the governments. The practical contribution of this paper is expected to assist the government in detecting corruption by using a data-driven approach. Furthermore, the scientific contribution will originate from the development of a framework reference architecture to uncover corruption cases.
Online learning for imbalanced data: optimizing asymmetric measures

Online learning receives increasing attention due to its efficiency in handling large-scale streaming data. However, imbalanced data raises a big challenge for traditional online algorithms which aim at minimizing the misclassification error rate. This thesis works on online learning algorithms that are suitable for classification on imbalanced data. We first investigate fast converging algorithms directly optimizing asymmetric performance measures, which is challenging because most of the measures are non-decomposable so that standard online optimization methods are not applicable. As a preliminary step, we propose a fast converging adaptive stochastic approximation (ASA) algorithm for the risk minimization with Lipschitz continuous random functions, which adapts to to structure conditions of the problem (a.k.a. error bound conditions, (EBC)). The proposed algorithm achieves a convergence rate of Õ(1/n2−θ) 1 depending on θ ∈ (0, 2], the power constant in EBC, where n is the maximum number of iterations. Based on the framework of ASA, we develop two fast converging online stochastic algorithms respectively optimizing AUC and F-measure. Specifically, we propose the Accelerated Stochastic Online AUC Maximization (ASOLAM). The problem is challenging because the loss involves pairs of examples from different classes which is non-decomposable. Our ASOLAM algorithm shares the advantage of low time 1The Õ(·) notation hides logarithmic factors.
An Improved XSS Vulnerability Detection Method Based on Attack Vector

The XSS vulnerability is one of the more damaging vulnerabilities of web applications, it could use to steal user accounts, horse attacks. The traditional XSS vulnerability detection tool is easier to be intercepted by the web filter, there is a big gap between the accuracy of the web application and the actual situation. To solve this problem, this paper analyzed the filtering rules of web system filters and security software, and an improved method for dynamic detection of XSS vulnerabilities based on attack vectors is proposed. This method bypasses the web system's filters and security software by way of mutating, obfuscating attack vectors, etc. The experimental results show that the proposed method improves the detection efficiency, and it has good performance in saving time and improving the vulnerability accuracy rate.
Changing the subject : a further examination of the returns to postgraduate education

An investigation is made of the wage return to postgraduate degrees in the UK, crucially differentiating between subject of study for both undergraduate and postgraduate degree over the period 2014-2017. Positive rewards to postgraduate qualifications are found regardless of classification of undergraduate degree. Social Sciences undergraduates are most likely to change subject at postgraduate and see wage gains, implying that they gain transferable skills from their undergraduate degree to allow them to pursue a different career path should they wish. Business and Finance is a popular choice for postgraduates, which often provides significant wage gains for individuals, regardless of their undergraduate subject.
The Carbon Dioxide Removal Model Intercomparison Project (CDR-MIP): Rationale and experimental design

The recent IPCC reports state that continued anthropogenic greenhouse gas emissions are changing the climate threatening "severe, pervasive and irreversible" impacts. Slow progress in emissions reduction to mitigate climate change is resulting in increased attention on what is called Geoengineering , Climate Engineering , or Climate Intervention – deliberate interventions to counter climate change that seek to either modify the Earth's radiation budget or remove greenhouse gases such as CO 2 from the atmosphere. When focused on CO 2 , the latter of these categories is called Carbon Dioxide Removal (CDR). The majority of future emission scenarios that stay well below 2 °C, and nearly all emission scenarios that do not exceed 1.5 °C warming by the year 2100, require some form of CDR. At present, there is little consensus on the impacts and efficacy of the different types of proposed CDR. To address this need the Carbon Dioxide Removal Model Intercomparison Project (or CDR-MIP) was initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDR-MIP experiments that are designed to address questions concerning CDR-induced climate "reversibility", the response of the Earth system to direct atmospheric CO 2 removal (direct air capture and storage), and the CDR potential and impacts of afforestation/reforestation, as well as ocean alkalinization.
Reshaping the Financial Network: Value Redistribution and Externalities in Central Clearing

This paper argues that the post-crisis infrastructural reform mandating central clearing of standardized over-the-counter derivatives impacts the valuation of a derivative contract and leads to unintended value redistribution effects among market participants. In a theoretical model, we show how the exogenously imposed change in the market structure affects counterparty risk and funding costs of different types of market participants. Specifically, we find that netting is beneficial for relatively high-quality counterparties, but counterparties with low creditworthiness are better off from accumulating larger gross positions. Further, even though a CCP interposes itself between a buyer and a seller of a derivative contract, precisely in times of distress the network of expected exposures between CCP members becomes fully connected. Our results highlight that mutualization of risks and resources in a CCP leads to externalities between the members.
Tiered Information Disclosure: An Empirical Analysis of the Advance Peek into the Michigan Index of Consumer Sentiment

This paper studies market microstructure implications of informed high&#8208;frequency traders (HFTs) from two seconds of advance peek into the Michigan Index of Consumer Sentiment (ICS), provided by Thomson Reuters to its elite customers. Using individual stocks in the NASDAQ data set, we show how HFTs trade around ICS events. We find that liquidity demanders during two seconds of advance peek earn substantive profits, which are consistent with the notion that HFTs&#8217; informational advantages may increase adverse selection costs for other market participants. This evidence elucidates the debate on regulatory oversight and its role in circumventing the potentially adverse effects from an advance peek into ICS.