Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting tourism demand in Amsterdam with Google Trends: A research into the forecasting potential of Google Trends for tourism demand in Amsterdam

The tourism industry is still growing worldwide and is now responsible for 9 % of the Dutch domestic product, the tourism industry is contributing to the economic growth. Since tourism demand modelling and forecasting has attracted much attention from researchers and progress had been made in this area. This study focusses on the forecasting value of Google Trends for tourism demand by overnight stays in hotels in Amsterdam. The literature indicates that Google Trends has some value for forecasting tourism from which the extent will be measured in this study. The customer journey theory was used to subtract search query terms in a deductive way and the other way around tourist statistics were linked to Google Trends with the use of Google Correlate. The researcher found that data provided by Google Trends can be useful for forecasting night passes in hotels in Amsterdam if the fitting keywords are used. The extent to which the explorative research indicates usefulness is moderate with an average adjusted r-square of 37.4 %. The conclusion for the data driven phase of this research is that there are many correlating search queries, but only very view with possible predictive value or even Google Trends output. Therefore, it was concluded that the data generated from Google Trends in the inductive research has very low to zero usefulness for forecasting night passes in hotels in Amsterdam.
BCH Codes for Coherent Star DQAM Systems with Laser Phase Noise

Abstract Coherent optical systems have relatively high laser phase noise, which affects the performance of forward error correction (FEC) codes. In this paper, we propose a method for selecting Bose–Chaudhuri–Hocquenghem (BCH) codes for coherent systems with star-shaped constellations and M-ary differential quadrature amplitude modulation (DQAM). Our method supports constellations of any order M which is a power of 2, and includes differential M-ary phase shift keying as a special case. Our approach is straightforward, requiring only short pre-FEC simulations to parameterize a statistical model, based on which we select codes analytically. It is applicable to pre-FEC bit error rates (BERs) of around 10−3. We evaluate the accuracy of our approach using numerical simulations. For a target post-FEC BER of 10−5, codes selected with our method yield BERs within 2× target. Lastly, we extend our method to systems with interleaving, which enables us to use codes with lower overhead.
Amplification of risks to water supply at 1.5 °C and 2 °C in drying climates: a case study for Melbourne, Australia

Human-induced climate change poses a major threat to the reliable water supply in many highly populated regions. Here we combine hydrological and climate model simulations to evaluate risks to the water supply under projected shifts in the climate at the Paris Agreement warming levels. Modelling the primary surface water sources for Melbourne, Australia, we project that the risk of severe water supply shortage to the climate-dependent portion of the system increases substantially as global warming increases from 1.5 °C to 2.0 °C. Risks are further exacerbated by increases in water demand but substantially ameliorated by supply augmentation from desalination. We demonstrate that reductions in precipitation, rising temperature and growth in water demand combine to substantially amplify the risk of severe water supply shortage under near-term global warming in the absence of a climate-independent supply. This risk amplification is not as apparent in assessments based on meteorological drought alone. With the diminishing opportunity of meeting the 1.5 °C Paris target, our study highlights the need to accelerate greenhouse gas mitigation efforts to reduce risks to climate dependent water supply systems.
Learning complex basketball rules discriminative through instruction intentional.

: The aim of this paper is to present a case study to see if a group of players get improve their decision -making through intentional learning of complex rules, with little perceptual salience . They included 10 basketball players male aged between 18 and 24 years (M = 21.47 ) . A within pre -experimental design was used , in which the group considered is the experimental group. In the study the effect of individual responses from each player in the intervention were examined with a cross comparison analyzing the pre and post exposure changes to the intervention, and other longitudinal, to assess the changes oc-curring throughout the season, selecting 8 games for it randomly . Decision were measured during blocking actions that occurred during games. The intervention was based on the presentation of the keys using a reflective feedback The results show that after the process of training the players made better decisions for the actions of blocks. Furthermore a correlation between decision making and effectiveness found in the game . The results obtained in this study lead us to believe that the training of decision-making based on an explicit learning with a reflective intentionality may be suitable for highly complex actions such as those that occur in crashes in basketball.
A variability study of PCM and OxRAM technologies for use as synapses in neuromorphic systems

The human brain is made of a large number of interconnected neural networks which are composed of neurons and synapses. With a low power consumption of only few Watts, the human brain is able to perform computational tasks that are out of reach for today’s computers, which are based on the Von Neumann architecture. Neuromorphic hardware design, taking inspiration from the human brain, aims to implement the next generation, non-Von Neumann computing systems. In this thesis, emerging non-volatile memory devices, specifically Phase-Change Memory (PCM) and Oxide-based resistive memory (OxRAM) devices, are studied as artificial synapses in neuromorphic systems. The use of PCM devices as binary probabilistic synapses is studied for complex visual pattern extraction applications, evaluating the impact of the PCM programming conditions on the system-level power consumption.A programming strategy is proposed to mitigate the impact of PCM resistance drift. It is shown that, using scaled devices, it is possible to reduce the synaptic power consumption. The OxRAM resistance variability is evaluated experimentally through electrical characterization, gathering statistics on both single memory cells and at array level. A model that allows to reproduce OxRAM variability from low to high resistance state is developed. An OxRAM-based convolutional neural network architecture is then proposed on the basis of this experimental work. By implementing the computation of convolution directly in memory, the Von Neumann bottleneck is avoided. Robustness to OxRAM variability is demonstrated with complex visual pattern recognition tasks such as handwritten characters and traffic signs recognition.
Congestion-driven re-clustering CAD flow for low-cost FPGAs

FPGA device area is dominated by the on-chip interconnect. For this reason, the amount of interconnect provided must be limited. This limit is usually imposed by designing an FPGA device family with a fixed channel width. CAD tools must meet this hard channel-width constraint for a circuit to be successfully mapped to a device from this family. Previous work has shown that if a design cannot be mapped to a device due to insufficient interconnect availability, it is possible to identify regions of high interconnect demand, and spread out or depopulate the logic in this area into surrounding regions. This is done by re-packing logic in the affected regions into an increased number of CLBs. This increases the effective amount of interconnect available to these high-demand areas. This methodology has been shown to significantly reduce channel width, at the expense of CLB count and runtime. In this work, we extend this previous algorithm in two ways: we present novel region selection techniques to optimize the selection of which regions should be depopulated, and we introduce a local channel-width demand model which can used to more accurately determine the amount of white space insertion at each iteration. Together, these techniques lead to significant run-time improvements and reduce the area of the resulting FPGA implementations. We were able to improve runtime by a factor of up to 5.5 times while reducing area by up to 20% when compared to previous methods.
Evaluating Business Intelligence / Business Analytics Software for Use in the Information Systems Curriculum

Business Intelligence (BI) and Business Analytics (BA) Software has been included in many Information Systems (IS) curricula. This study surveyed current and past undergraduate and graduate students to evaluate various BI/BA tools. Specifically, this study compared several software tools from two of the major software providers in the BI/BA field. The participants in the study evaluated each software tool according to three key criteria: 1) functionality, 2) ease of use, and 3) learning effectiveness. The “learning effectiveness” criterion was used to determine which BI/BA tools provided the most effective learning of BI/BA concepts in the IS classroom. The three criteria were used to develop recommendations for including specific BI/BA software tools in the IS curriculum. Based on the findings of the study, the authors recommend that colleges and universities consider the use of the IBM-Cognos suite of tools as a viable means for teaching BI/BA concepts in their Information Systems curricula. The results of the study are relevant to any college or university that currently includes (or is considering the inclusion of) Business Intelligence / Business Analytics concepts in its Information Systems curriculum.
Interactive induction

The authors identify and discuss a generic induction approach called interactive induction, which offers an alternative method for eliciting subjective information from an expert during the course of knowledge acquisition. The approach extends pure induction by involving the expert in the provision of additional subjective knowledge and in the incremental evaluation and validation of knowledge induced. It is still able, in principle, to induce knowledge beyond that articulated or known by the expert. Moreover, unlike earlier induction methods, interaction does play a key role; induction is not viewed as an automatic process. Consideration is given to the various aspects of the knowledge engineering problem that are relevant to the interactive induction approach and software features that are needed to support it. Relevant theory is discussed along with lines for future research. <<ETX>>
VizLattes: a tool for relevance analysis from scientific co-authorship networks

Social network data typically carry attribute information associated with the individuals and to their relationships. Such interconnected information can be useful to identify groups of individuals sharing common attribute properties and also to investigate the behaviour of particular individuals in a global network scenario. Different approaches have been introduced to extract and identify information of interest in social networks, and community identification is one of them. Some methods focus on identifying groups or communities of individuals based on their relationships, while others try to identify groups of individuals based on the common information they share. Integrating both approaches is not straightforward, as different mathematical and computational must be implemented and integrated into a unified framework. In this paper we approach this problem and propose a new method to identify underlying communities in a network, while highlighting the information shared by their components. Our solution relies on a single unified mathematical method. As a proof-of-concept, we have applied the proposed method to scientific co-authorship networks extracted from the wellknown Lattes Platform made available by CNPq, the Brazilian national science funding agency. We use textual information on the co-authors and their papers as focus attributes. We show that the method supports both community detection and also the identification of thematic paths, underlying topics and relevant authors characterizing distinct academic communities. The results presented show that this approach can be quite useful for exploration and understanding of academic collaboration networks. Keywords-Thematic paths, Scientific co-authorship networks, Non-Negative Matrix Factorization.
3D Shape Reconstruction by Dynamic Sensing with A Range Sensor

Building 3D shape models of interesting targets and getting their localization are very important fundamental tasks in the fields like digital culture heritage, robotics, mixed reality, medical application and so on. Most commonly used sensors for 3D modeling are camera and laser range sensor. Compared with camera, range sensor has the following advantages: 1. It can easily get direct and highly accurate 3D data. 2. Range data is stable, whereas the 3D modeling techniques based on 2D cameras are much more sensitive to illumination, shadows, scale and pose. However, range sensor costs a relatively long time of data acquisition. This causes data distortion and cannot be ignored especially in the case of continuously gathering data on a moving platform. Existing common solutions to this problem are either taking ”stopscan-go” strategy to avoid distortion or correcting the sensor motion using secondary sensors like GPS, inertial sensor, camera or even another range sensor. Some other researches apply linearization, discretization and other specified constraints to the problem to achieve compromised results. This thesis focuses on efficient and accurate 3D shape reconstruction under a moving sensing system only with a single range sensor. Different from previous solutions, the proposed system works in an efficiently continuous manner. We don’t have to stop the platform to obtain an stationary scan. Sensor can move under a reasonable motion mode and simultaneously scan the target or environment. Data distortion caused by continuous movement will be rectified. This continuous manner is much more efficient and attractive in practical applications. According to the different prior conditions of targets, we divide the problem to two categories. The first one is based on the assumption that the target shape model is known. The second one is focusing on the unknown targets. For the known targets, since there is no need to build 3D model for the object, our interesting mainly focus on how to accurate localize them. We
The development of trust over time in an emerging market context: the case of the Tunisian automotive sector

Purpose The purpose of this paper is to empirically investigate how context influences the quality of business relationships. This theoretical question is studied from the point of view of trust, one of the important components of business relationship quality. The authors study how trust is related to the dynamics and management of the business relationship in the context of an emerging market. Design/methodology/approach This paper is based on qualitative interviews with 15 spare-parts resellers in the Tunisian automotive industry. The authors take a monadic view, interviewing resellers about their relationships with their wholesalers-importers. The decision to undertake the research in Tunisia is based on three factors. First, Tunisia is an emerging country and there is very little published research based in the Maghreb countries. Second, the Tunisian automotive parts market structure is relatively simple and, hence, easily understood, with most spare-parts being imported because of the low level of local production. Third, the actors in the study are all Tunisian companies, so research allows us to explore relationships between local companies in an emerging country. Findings The authors find that different kinds of trust play different roles over the dynamics of the relationship. Perceived trust is more important at the emergent stage of a relationship, and as the two parties learn from each other, experienced trust becomes more important in the established relationships. The initial perceived trust creates the possibility of building trust, and when mutual trust exists between the parties, it motivates them to maintain the relationship, but there is always the threat of the degradation of the quality of the relationship because of the violation or destruction of the trust. Research limitations/implications This paper shows that more care should be taken when using trust as the variable under scrutiny. Different aspects of trust manifest themselves at various stages of the relationship building cycle. Practical implications The results emphasize that when initiating a business relationship, managers first need to create perceived trust. Thereafter, once trust is built up, it is the trust that may “manage” or act to control the on-going relationship as long as the partners’ behavior or network changes do not violate the trust. Originality/value The results of this paper show that there is a mutual but not necessarily symmetrical or balanced influence of trust on the behavior of the partners involved. The influence of the different parties is dependent on the power architecture, the history of the relationship and the network position of the actors.
Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2) are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.
EXTRACTION OF RIDGES TO DETECT ROAD NETWORK FROM TOPOGRAPHIC IMAGE

Current methods of road network extraction a feature extraction phase. In this article, we suggest a method for extraction of road network from topographic image using ridges. Our purpose is to study a simple, but robust method which allows to extract the ridges in an image at several scales. These ridges are used to build object models. The experiments show that our ridge extraction method based on differential geometry performs well on road network extraction from topographic image.With just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection. It is shown how the methodology can be implemented in terms of straightforward visual front-end operations, and the validity of the approach is supported by theoretical analysis as well as experiments on real-world and synthetic data.
Automatic Identification of Potential Respondents to Subjective and Objective Questions

In recent years social networking sites are ver popular for user needs. Mostly people use an important social networking information source such as Twitter to find answers to their questions. Twitter contains microblogging services for informal information interactions. In this paper, we classify the subjective and objective questions using Naïve Bayes algorithm and also find the respondent users. For classification we build the feature extraction techniques such as lexical, syntactical and contextual in terms of the way they are asked and answered. For applying the classification on a larger dataset using other social media and analysis of performance.
ECN Implementations in the NS Simulator

This document illustrates the validation test for ECN (Explicit Congestion Notification) in the NS simulator [NS95]. Figure 1 shows a single simulation with two-way traffic, showing the use of the CE (Congestion Experienced) bit in the IP packet header, and the CWR (Congestion Window Reduction) and ECN-Echo bits in the TCP header. We refer to a packet with the CE bit marked as a CE packet. Similarly, a CWR packet denotes a packet carrying a CWR notification, and an ECN-Echo ACK packet denotes an ACK packet with the ECN-Echo bit set. Figures 2 and 3 show that multiple CE packets in a window of data are considered as a single instance of congestion. Figures 4, 5, 6, and 7 show that interspersed dropped packets and CE packets are treated as a single instance of congestion. While the behavior in these simulations is specific to Tahoe TCP, similar simulations in the test suite show that the same property holds for the Reno, NewReno, and Sack TCP implementations. Figures 8 and 9 are a separate simulation scenario showing that interspersed dropped packets and CE packets are treated as a single instance of congestion. Figures 10, 11, and 12 show that interspersed dropped packets and CE packets are treated as a single instance of congestion even when the packet drops are sufficiently severe to result in a retransmit timeout. Figure 13 shows that retransmitted packets with the CE bit set are interpreted as a new instance of congestion.
Zakat accounting information system in private higher education

Zakat is one of the five pillars of Islam and Muslims must pay it if they meet a certain prerequisite. The obligation to pay zakat among Muslims is stated clearly in the Quran and hadith. This explains that all Muslims are required to pay zakat when meeting certain criteria and they need to declare the zakat compensation as well. However, integrated accounting information in the Zakat Management Organization at the Private Higher Educational Institutions in Indonesia does not exist yet, especially the use of finance data analysis with accounting principles. It may be one of the most important parts of building the trust of the public, especially in increasing the collection and distribution of zakat funds. Accounting information related to zakat is still relatively lacking and ineffective, thus limiting the ability to make a good decision. Based on this issue, this paper has two main research objectives. The first one is to form an Accounting Information System of Zakat (AISoZ) conceptual framework, which is compatible with Islamic laws. The second one is to identify the general concepts of shariah accounting. This study uses the content analysis method through documentation study. The result of this study will provide the basics for developing the conceptual framework of AISoZ at higher education levels so that all parties can benefit. The conceptual framework of this study can build muzakki and mustahiqq. The Qur'an and Hadith have long applied these elements of information systems and they are suitable for use in all situations. Therefore, people need to cooperate to provide a good zakat service system and to maintain better zakat networks.