Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Characteristics and Activity Space Pattern Analysis of Dhaka City, Bangladesh

Although a handful of studies have begun to integrate activity space within travel behavior analysis in the European and United States (U.S.) contexts, few studies have measured the size, structure, and implications of human activity spaces in the context of developing countries. To identify the effects of land-use characteristics, socio-demographics, individual trip characteristics, and personal attitudes on the travel-activity based spatial behavior of various population groups in Dhaka city, Bangladesh, a household-based travel diary pilot survey (for two weekdays) was conducted for 50 randomly selected households in the winter of 2017. The study focused on two separate subareas: one taken from Dhaka North City Corporation, and another taken from Dhaka South City Corporation. Two methods—shortest-path network and road network buffer—were used for calculating activity space in a geographic information system (GIS). The daily activity areas for individual respondents ranged from 0.37 to 6.18 square miles. Land-use mix was found to be a significant predictor of activity space size for the residents. Larger activity space was recorded for the residents of one subarea over another due to less land-use diversity. The pilot data showed some specific socio-economic and travel differences across the two study subareas (car ownership, income, modal share, distance traveled, trip duration).
Current Self Future Self Current Self Future Self Current Self Future Self Current Self Future Self Current Self Future Self Current Self Future Self Current Self Future Self

Some people find it more difficult to delay rewards than others. In three experiments, we tested a “future selfcontinuity” hypothesis that individual differences in the perception of one’s present self as continuous with a future self would be associated with measures of saving in the laboratory and everyday life. Higher future self-continuity (assessed by a novel index) predicted reduced discounting of future rewards in a laboratory task, more matches in adjectival descriptions of present and future selves, and greater lifetime accumulation of financial assets (even after controlling for age and education). In addition to demonstrating the reliability and validity of the future self-continuity index, these findings are consistent with the notion that increased future self-continuity might promote saving for the future.
Distributed File Systems

• Naming: identify files systemwide • how are concurrent reads and writes ex-ecuted? Naming Aims: • Location Transparency: name does not give any hint on location • Location independence: files can be moved without name being changed standard approach: Remote mounting Make remote file system available under local name Achieves only location transparency Latter difficult to achieve (requires name server)
An efficient method to detect communities in social networks

Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.
The role of terroir in tourism led amenity migration.

Definitions of terroir elude consistent agreement. As defined geographical space the common denominators of its conceptualization include natural and cultural elements of life, work, and lifestyle that have become idealized, even fetishized worldwide. It seems the ideal terroir for wine production is also an idealized lifestyle location. A very high quality of life is associated with the landscape of the wine terroir, hence, visiting a winery, running a winery and living in the vicinity of a winery has become valued among amenity seeking tourists often followed by amenity migrants. This broad scale investigation uses the theoretical framework of evolutionary economic geography to examine the process and effects of tourism led amenity migration in Tuscany, Italy and the Okanagan Valley of British Columbia, Canada. Tuscany and the Okanagan are examples of old and new worlds of wine production where wine tourism and amenity migration have taken on common qualities with often differing results. Evolutionary histories of the wine and tourism industries in Tuscany and the Okanagan are laid out alongside the process of lifestyle or amenity migration that have emerged. Key motivators that facilitate tourism led, wine based migrations are theorized to illustrate temporal and spatial patterns of tourism and migration. The amenities of the wine terroir integrate natural, cultural and lifestyle characteristics associated with the rural countryside in general. The process of change from wine led tourism to migration appears imbedded in a class attachment to the romanticized social construction of wine production. However, the effects of wine based amenity migrations are deeply localized and appear driven by local innovations and innovators, the transition and specialization of local institutions and the presence or absence of windows of opportunity created by cultural and economic transformations.
Mining Specific and General Features in Both Positive and Negative Relevance Feedback: QUT E-Discovery Lab at the TREC 2010 Relevance Feedback Track

User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovered knowledge are two critical problems. However, understanding what makes an individual document good or bad for feedback can lead to the solution of the previous problem. In TREC 2010, we participated in the Relevance Feedback Track and experimented two models for extracting pseudo-relevance feedback to improve the ranking of retrieved documents. The first one, the main run, was a pattern-based model, whereas the second one, the optional run, was a term-based model. The two models consisted of two stages: one using relevance feedback provided by TREC’10 to expand queries to extract pseudo-relevance feedback; one using pseudo-relevance feedback to find useful patterns and terms according to their relevance and irrelevance judgements to rank documents. In this paper, the detailed description of those models is presented.
Adaptive Indoor Scene Classification with Multi-SVM Classification to solve Multi-Class Problem

In recent years, indoor scene recognition has attracted much attention and its research has rapidly expanded by not only engineers but also neuroscientists, since it has many potential applications in computer vision communication and automatic access control system. Especially, indoor scene recognition is an important part of computer vision and feature recognition as the first step of automatic uninterrupted robotic movement or computer vision applications like automatic interior designing algorithms. However, the indoor scene detection is not straightforward because it has lots of variations of image appearance, such as light effect, occlusion, image orientation, illuminating condition and object variety. Many novel methods have been proposed to resolve each variation listed above. For example, the template-matching methods are used for indoor scene localization and detection by computing the correlation of an input image to a standard and training scene appearance or pattern. The feature invariant approaches are used for feature detection of bed, chair, cabinet, table, door, electrical or electronic items, etc. The appearance-based methods are used for indoor feature detection with support vector machine and information theoretical approach. Nevertheless, implementing the methods altogether is still a great challenge. Fortunately, the images used in this project have some degree of uniformity thus the detection algorithm can be simpler: first, the all the faces are vertical and have frontal view; second, they are under almost the same illuminate condition. This project presents an indoor scene detection technique mainly based on the appearance based feature segmentation, SVM training and SVM classification methods to recognize the indoor scenes.
Understanding Continuous Use of Business Intelligence Systems: A Mixed Methods Investigation

Business intelligence (BI) systems play an important role in organizations' decision-making processes. The existing literature has long focused on the continuous use of information systems (IS). However, the specificities of BI systems such as voluntary use, long-term return of investments, heterogeneity of their use cases, and innovative rather than routine use in such systems motivate our investigating continuous use in the specific context of BI systems. To theorize continuous use of BI systems, we investigate the influencing factors and their interactions. By means of an exploratory and confirmatory mixed-methods research design that comprises a literature review, a single-case study, and a survey, we integrate the identified factors and hypothesize their influence on the continuous use of BI systems in a research model. We test the research model following a partial least squares (PLS) approach to structural equation modeling (SEM). The paper makes two primary contributions: 1) it confirms certain well-established constructs and relations in the specific context of BI systems, which are generally theorized for the continuous use of IS, and 2) it introduces either new constructs or new relations through the given investigation in the context of BI systems. Future studies can test these new constructs and relations as potential input for theorizing general IS continuous use.
Crowdtasking: Field Study on a Crowdsourcing Alternative.

In this paper we elaborate on the concept of crowdtasking , which has previously been introduced as an alternative to crowdsourcing . The paper describes the setup and boundaries of a first controlled live field-test of a prototypical implementation of a possible crowdtasking workflow. The implemented workflow allows crisis managers rapid intelligence gathering due to direct and tailored task distribution. Practitioners of Crisis and Disaster Management and volunteer managers who were present during the field test made favourable comments on the approach and its implementation. The analysis of the records and the conducted interviews give new insights and ideas for further development
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to smal l shifts and distortions. The resulting feature extractor co nsists of multiple convolution filters, followed by a pointwise sigmoid non-linearity, and a feature-pooling layer that computes the max of each filter output within adjacent windows. A second level of larger and more invariant features is obtained by training the same algorithm on patches of features from the first level. Training a supervised classifier on these features yields 0.64% error on MNIST, and 54% average recognition rate on Caltech 101 with 30 training samples per category. While the resulting architecture is similar to convolutional networks, the layer-wise unsupervised training procedure alleviates th e over-parameterization problems that plague purely supervised learning procedures, and yields good performance with very few labeled training samples.
A Simple Model of Communication APIs - Application to Dynamic Partial-order Reduction

We are interested in the verification, using model checking, of distributed programs that communicate asynchronously over standard communication APIs such as MPI. This is feasible only if the set of executions that the model checker explores is aggressively reduced to a subset of representative executions, using techniques such as dynamic partial-order reduction. We propose a small set of core primitives in terms of which such APIs can be defined and formally specify these primitives in TLA+. From this specification we derive theorems about the (in)dependence of invocations of the primitives, and use them in a DPOR-based verifier that runs within SimGrid, a simulation framework for distributed programming. Our preliminary experimental results indicate that we obtain good reductions, even though complex network operations are implemented in terms of the core commu nication primitives.
HERMITE-HADAMARD TYPE INTEGRAL INEQUALITIES FOR DIFFERENTIABLE m-PREINVEX AND (α,m)-PREINVEX FUNCTIONS

In this paper, the notion of m-preinvex and (α,m)-preinvex functions is introduced and then several inequalities of Hermite-Hadamard type for differentiable m-preinvex and (α,m)-preinvex functions are established. The obtained inequalities for m-convex and (α,m)-convex functions, are then extended to functions of several variables.
Agile Methods : Looking for an Agreement

One of the effects of the nth software crisis is the great expansion of agile methodologies. Many agile methodologies have appeared since the publication of the Agile Manifesto. Agile methodologies are considered light in comparison to the traditional heavy ones. This paper describes (a) whether or not it is worth to use some UML diagrams as artefacts in Agile methods and, (b) what would be the set of UML diagrams selected as useful artefacts obtained in an agile process and why. The paper makes use of a particular agile method called Xcrum to show how and when the proposed UML diagrams would be employed.
TEXTURE REMOVAL IN COLOR IMAGES BY ANISOTROPIC DIFFUSION Preparation of Camera-Ready Contributions to SciTePress Proceedings

In this paper, we present a new method for removing texture in color images using a smoothing rotating filter. From this filter, a bank of smoothed images provides pixel signals for each channel image able to classify a pixel belonging to a texture region. We apply this classification in each channel image in order to compute two directions for the anisotropic diffusion. Then, we introduce a new method for vector anisotropic diffusion which controls accurately the diffusion near edge and corner points and diffuses isotropically inside textured regions. Several results applied on real images and a comparison with vector anisotropic diffusion methods show that our model is able to remove the texture and control the diffusion.
Service Innovation Research in the Context of Business Ecosystems-A multidisciplinary Mapping Study

Advances in technology and digitalisation have changed the ways services are created and offered to users. Successful new innovative services and service value co-creation are done in collaboration with organisations and actors in contexts such as business ecosystems. The research relating to service design and open innovation in business ecosystems is cross-disciplinary, and relevant knowledge is scattered throughout different fields. In this research, we are focusing on the research that exists in the areas of service design and open innovation in the business ecosystems context. We aim to collect, analyse and synthesise this existing knowledge in order to increase the understanding of the companies’ service design and open innovation activities in business ecosystems. A mapping study method is utilised to identify the existing related research in the area and to create an overview. The final analysis included 38 papers. Our analysis revealed that the relevant research is focused mainly on two research areas: business research and information technology-related research. Most of the papers combine the open innovation and business ecosystem aspects, but service design is mostly present as a general view on services as means for value creation. We identified antecedents for service value cocreation, such as practices for ecosystem actor involvement. We also identified challenges, such as managing the business ecosystem in terms of finding the right actors. The results indicate that more focused research on the practical understanding of service design and open innovation practices, methods and tools as well as sound theory development are still needed as the research field matures. The results help inform future research in this cross-disciplinary phenomenon.
the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation

We have witnessed a rapid growth of programmable switch applications, ranging from monitoring to security and offloading. Meanwhile, to safeguard the diverse network behaviors, researchers have developed formal verification techniques for high assurance. As a recent advance, network devices have become runtime programmable, supporting live program changes via partial reconfiguration. However, computing a runtime update plan that provides safety guarantees is a challenging task. FlexPlan is a tool that identifies step-by-step runtime update plans using program synthesis, guaranteeing that each transition state is correct with regard to a user specification and feasible within switch memory constraints. It develops novel, domain-specific techniques for this task, which scale to large, real-world programs with sizable changes.
Automatic texture segmentation based on k-means clustering and efficient calculation of co-occurrence features

This work presents a method for automatic texture segmentation based on the k-means clustering technique and co-occurrence texture features. A set of eight features were extracted from the grey-level co-occurrence information. Two of them are proposed here to improve segmentation results for magnetic resonance (MR) images. All the features are used to segment image regions based on textural homogeneity of its areas. As the process of calculating the grey-level co-occurrence matrix (GLCM) demands increased computational resources, we propose a new methodology based on an indexed list (IL) for fast element access. This novel approach highly optimizes the algorithm called grey level co-occurrence indexed list (GLCIL). Moreover, we compare the efficiency of the proposed method against two others, namely the traditional GLCM and the grey level co-occurrence linked list (GLCLL), which was suggested as a faster alternative to GLCM. The technique proposed here is the most efficient in terms of computational time, when compared to the other two. Additionally, examples on real time segmentation are presented to illustrate the appropriateness and robustness of this new method.
Comparison of Machine Learning Techniques for the Detection of the Electricity Theft

There are many applications available for detecting the electricity theft. However, only few studies compare the machine learning techniques in discovering electricity-stealing behavior. This study, therefore, compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), The K-Nearest Neighbor Algorithm, (K-NN), Support Vector Machines (SVM), and Neural Networks (NNet) for predicting the electricity thefts in a concrete model.
Query Learning with Large Margin Classi(cid:12)ers

The active selection of instances can sig-ni(cid:12)cantly improve the generalisation performance of a learning machine. Large margin classi(cid:12)ers such as Support Vector Machines classify data using the most informative instances (the support vectors). This makes them natural candidates for instance selection strategies. In this paper we propose an algorithm for the training of Support Vector Machines using instance selection. We give a theoretical justi(cid:12)cation for the strategy and experimental results on real and arti(cid:12)-cial data demonstrating its e(cid:11)ectiveness. The technique is most e(cid:14)cient when the dataset can be learnt using few support vectors.
Increasing Pace of Innovation: The Innovation Cube Model

– Innovation is the essence of humanity’s progress. Well-known internal corporate R&D and innovation centers like AT&T Bell Labs and Xerox PARC have been quite prolific in generating new innovations in post-World War II era and even received a number of Nobel prizes. Interestingly, it is often not possible to always count on internal innovations: big companies need to source new technologies and ideas from outside rather than trying to develop it all internally. GE’s inability to stay ahead of rapid changes got highlighted in 2018. Internet exponentially enhanced the pace of global innovations. With lower entry barriers and relatively easier availability of financing, innovators are increasing in numbers. In fact, new out-of-the-box innovations are emanating from smaller entrepreneurial startup organizations. ‘Innovation Cube’ is offered as a visualization tool to help refine the annual and longer-term strategic vision for a company in conjunction with recommendations regarding ways to source, develop and leverage radical innovation versus Incremental/ Evolutionary Innovations.
DepLink: an R Shiny app to systematically link genetic and pharmacologic dependencies of cancer

Abstract Motivation Large-scale genetic and pharmacologic dependency maps are generated to reveal genetic vulnerabilities and drug sensitivities of cancer. However, user-friendly software is needed to systematically link such maps. Results Here, we present DepLink, a web server to identify genetic and pharmacologic perturbations that induce similar effects on cell viability or molecular changes. DepLink integrates heterogeneous datasets of genome-wide CRISPR loss-of-function screens, high-throughput pharmacologic screens and gene expression signatures of perturbations. The datasets are systematically connected by four complementary modules tailored for different query scenarios. It allows users to search for potential inhibitors that target a gene (Module 1) or multiple genes (Module 2), mechanisms of action of a known drug (Module 3) and drugs with similar biochemical features to an investigational compound (Module 4). We performed a validation analysis to confirm the capability of our tool to link the effects of drug treatments to knockouts of the drug’s annotated target genes. By querying with a demonstrating example of CDK6, the tool identified well-studied inhibitor drugs, novel synergistic gene and drug partners and insights into an investigational drug. In summary, DepLink enables easy navigation, visualization and linkage of rapidly evolving cancer dependency maps. Availability and implementation The DepLink web server, demonstrating examples and detailed user manual are available at https://shiny.crc. pitt.edu/deplink/. Supplementary information Supplementary data are available at Bioinformatics Advances online.