Miracle-dz/UniFAR-FTUs huggingface dataset:
Incomplete Information Choice on Incumbents, Cognitive Ability and Personality

In choices under incomplete information on incumbents, consumers with stronger preferences are more likely to reinforce their prior choices with motivated reasoning. However, in situations where incomplete information is restricted only to the prior choice, consumers with stronger preferences are more likely to abandon, not reinforce, their prior choices due to cognitive dissonance. Here, we consider how cognitive ability and personality traits mediate such interplay between motivated reasoning and cognitive dissonance. We set an experiment to show that consumers with a stronger System 2 are more likely to engage in motivated reasoning to reinforce the prior choice and thus suffer less cognitive dissonance. Cognitive dissonance can, however, overcome motivated reasoning for those cognitively poor consumers who are more emotional, less humble, less extraverted and less conscientious.
Inefficiencies of Business Internal Carsharing : Proposing Revenue Management Concepts to Achieve Financial and Environmental Benefits

This research investigates business internal carsharing [BICS], a concept where businesses replace the idea of having one leasing car per employee by having a pool of cars which all employees can flexibly share with each other through bookings. One incentive for the businesses is to make business related car ownership and travel more efficient. BICS already makes a step in the right direction, since cars are assumed to stand still less than in the traditional leasing situation. However, the underlying assumption for this research is that BICS at this point does not capitalize its full potential. Therefore, the case of a specific BICS concept is analyzed to reveal the current inefficiencies, which arise from a high disparity of the utilization between peak and low moments. The research further investigates opportunities to reduce the inefficiencies and how revenue management concepts could be of help for a possible implementation. Benefits of a possible implementation are shown, and the feasibility of such implementation is discussed.
V-RSIR: A WEB-BASED TOOL AND BENCHMARK DATASET FOR REMOTE SENSING IMAGE RETRIEVAL

Abstract. Benchmark datasets play an important role in evaluating remote sensing image retrieval methods. Current benchmark datasets are mostly collected through the Google Map API or other desktop tools. However, the Google Map API requires the users to have programming skills and other collection tools are not publicly available, which may hinder the development of new benchmark datasets. This paper develops an open access web-based tool V-RSIR to help users generating new benchmark datasets with volunteers for remote sensing image retrieval. Using this tool, a new benchmark dataset V-RSIR that contains 38 classes with at least 1500 images per class is created by 32 volunteers. A handcrafted low-level feature method and a deep learning high-level feature method are used to test the dataset. The evaluation results are consistent with our perception. This shows that the tool can help users effectively creating benchmark datasets for RSIR.
Towards informal computer human communication: detecting humor in restricted domain

The dissertation presents a computational humor detector designed to look at any one of 200 short texts (100 jokes and 100 non-jokes) and to determine whether the text is a joke or not. This is accomplished on the basis of meaning recognition by the computer with the help of an ontology crafted from a children's dictionary, and any additional background knowledge necessary for text understanding. The research is underlaid by an advanced formal semantic theory of humor, and it constitutes the first known attempt to validate a theory of humor computationally. The results of the computational experiments are quite encouraging. With the advancement of computational technologies, increasingly more emphasis continues to be placed on systems that can handle natural language, whether it involves human-computer communication, or comprehension of written narratives, information on the Web, or human conversations. Humor occurs frequently in verbal communication. Thus, without humor detection no natural language computer system can be considered successful. For full computational understanding of natural language documents and for enabling intelligent conversational agents to handle humor, humor recognition is necessary or at the very least highly desirable. This exploratory research had to be constrained and its goals narrowed down for the purpose of implementability. The joke detector is therefore restricted to the recognition of short jokes. The domain is further restricted to jokes that are based on ambiguous words, where the detection of several meanings results in humor; and to jokes that are based on similar- or identical-sounding words, where the detection of correct pairs also leads to humor. Because of the meaning-based nature of the research, the system can be extended to other types of humor in text, without changes to the algorithm. The central hypothesis is that humor recognition of natural language texts is possible when the knowledge needed to comprehend the texts is available in a machine-understandable form. To test the hypothesis, a description logic ontology was built to represent knowledge manifested in natural language texts. The results show that when the information, necessary for humans to understand humor, is available to a machine, it successfully detects humor in text.
A hybrid computer architecture for machine vision

A hybrid computer architecture for machine vision which combines the useful properties of different types of architectures is introduced. HYBRID, an experimental hybrid system consisting of specialized Datacube-compatible processors and a transputer network, has been developed in a Sun-3 environment. The VLSI implementation of an edge-preserving smoothing operator for the low-level vision system is described, and the performance of transputer-based systems for higher-level vision is evaluated. Methods for analyzing and optimizing the performance of a hybrid architecture are discussed. <<ETX>>
Effective Capacity-Based Resource Allocation in Mobile Edge Computing With Two-Stage Tandem Queues

In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.
Traffic network micro-simulation model and control algorithm based on approximate dynamic programming

This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.
Managing working capital efficiency in Turkish agribusinesses and the impact of globalization: insights from an emerging market

The efficiency of working capital is a major determinant of firm profitability. So, the grasp of working capital dynamics is extremely important for managers, but also for policy makers, since inefficient working capital management is an important source of industrial sickness. This study focuses on the profitability impacts of working capital policies of Turkish agribusinesses, and aims to investigate the potential effects of globalization on these interrelated relationships. The findings obtained from pooled panel analyses demonstrate that Turkish agribusinesses can enhance their profitability and value by adopting a conservative working capital policy through lengthening the cash conversion cycle up to an optimal level. Besides, globalization is found to deteriorate their efficiency and profitability where economic globalization seems to have the highest impact raising questions on the effectiveness of Turkish agribusinesses in coping with competition. Thus, both the managers and the policy makers should concern with the competitiveness impacts of globalization.
A New Algorithm for Modeling and Inferring User's Knowledge by Using Dynamic Bayesian Network

Dynamic Bayesian network (DBN) is more robust than normal Bayesian network (BN) for modeling users’ knowledge when it allows monitoring user’s process of gaining knowledge and evaluating her/his knowledge. However the size of DBN becomes numerous when the process continues for a long time; thus, performing probabilistic inference will be inefficient. Moreover the number of transition dependencies among points in time is too large to compute posterior marginal probabilities when doing inference in DBN. To overcome these difficulties, we propose the new algorithm that both the size of DBN and the number of Conditional Probability Tables (CPT) in DBN are kept intact (not changed) when the process continues for a long time. This method includes six steps: initializing DBN, specifying transition weights, re-constructing DBN, normalizing weights of dependencies, re-defining CPT(s) and probabilistic inference. Our algorithm also solves the problem of temporary slip and lucky guess: “learner does (doesn’t) know a particular subject but there is solid evidence convincing that she/he doesn’t (does) understand it; this evidence just reflects a temporary slip (or lucky guess)”.
Integrating general principles into mixed-integer programming to optimize schematic network maps

ABSTRACT Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with various sets of constraints. Most of these constraints govern geometric properties of individual features. A few constraints address relationships among features, but none explicitly deal with the main structure of an entire network. We believe that preservation of main structure is the most important and preservation of relative relations is helpful. This is because human perception follows a global-to-local process. These constraints have recently been formed into four general principles, with two for global structure and two for relativity of features. This study develops an automated method by integrating these principles into the mixed-integer programming (MIP) framework. Experimental evaluations have been conducted with two sets of real-world transit networks. In comparison to the existing method, the proposed method has smaller fractal dimensions, better computational performance and higher scores in terms of clarity, recognition of major lines, visual simplicity and satisfaction. Therefore, it is concluded that the proposed method can generate schematic maps with improved clarity and aesthetics. The idea in this study is also helpful for the design of other visual representations.
Sistem Informasi Pemasaran Busana Syar’i dengan Penerapan Customer Relationship Management (CRM) Berbasis Web

Toko Cahaya Sunnah is a business that produces and sells syar'i clothing. In its business development, Toko Cahaya Sunnah keep trying to expand its market target to out of town even all of Indonesia. To achieve its goal, Toko Cahaya Sunnah requires website. Currently in Toko Cahaya Sunnah in making sales report is still manual using excel application so that data processing of sales report still running slow and not yet detailed. Currently Toko Cahaya Sunnah does not have any special activities or strategies that handle marketing, product marketing is done in a conventional manner by providing customers with information about places, features and products to other potential customers. Ineffective information dissemination prevents customers from knowing the latest information in the Sunnah Light Store thus affecting customers to switch to other stores. With the website using the method of Customer Relationship Management (CRM) then the data storage process can be more accurate and stored neatly and making sales reports will be fast and timely and delivery of promotional information will be faster and response to sales will be faster. Keywords: Information System, Customer Relationship Management (CRM), Web
Conditioning Convolutional Segmentation Architectures with Non-Imaging Data

We compare two conditioning mechanisms based on concatenation and feature-wise modulation to integrate non-imaging information into convolutional neural networks for segmentation of anatomical structures. As a proof-of-concept we provide the distribution of class labels obtained from ground truth masks to ensure strong correlation between the conditioning data and the segmentation maps. We evaluate the methods on the ACDC dataset, and show that conditioning with non-imaging data improves performance of the segmentation networks. We observed conditioning the U-Net architectures was challenging, where no method gave significant improvement. However, the same architecture without skip connections outperforms the baseline with feature-wise modulation, and the relative performance increases as the training size decreases.
RUNTIME METHODS TO IMPROVE ENERGY EFFICIENCY IN SUPERCOMPUTING APPLICATIONS

Sridutt Bhalachandra: Runtime Methods to Improve Energy Efficiency in Supercomputing Applications (Under the direction of Allan K. Porterfield and Jan F. Prins) Energy efficiency in supercomputing is critical to limit operating costs and carbon footprints. While the energy efficiency of future supercomputing centers needs to improve at all levels, the energy consumed by the processing units is a large fraction of the total energy consumed by High Performance Computing (HPC) systems. HPC applications use a parallel programming paradigm like the Message Passing Interface (MPI) to coordinate computation and communication among thousands of processors. With dynamically-changing factors both in hardware and software affecting energy usage of processors, there exists a need for power monitoring and regulation at runtime to achieve savings in energy. This dissertation highlights an adaptive runtime framework that enables processors with core-specific power control by dynamically adapting to workload characteristics to reduce power with little or no performance impact. Two opportunities to improve the energy efficiency of processors running MPI applications are identified computational workload imbalance and waiting on memory. Monitoring of performance and power regulation is performed by the framework transparently within the MPI runtime system, eliminating the need for code changes to MPI applications. The effect of enforcing power limits (capping) on processors is also investigated. Experiments on 32 nodes (1024 cores) show that in presence of workload imbalance, the runtime reduces Central Processing Unit (CPU) frequency on cores not on the critical path, thereby reducing power and hence energy usage without deteriorating performance. Using this runtime, six MPI mini-applications and a full MPI application show an overall 20% decrease in energy use with less than 1% increase in execution time. In addition, the lowering of frequency on non-critical cores reduces run-to-run performance variation and improves performance. For the full application, an average speedup of 11% is seen, while the power is lowered by about 31% for an energy savings of up to 42%. Another experiment on 16 nodes (256 cores) that are power capped also shows performance improvement along with power reduction. Thus, energy optimization can also be a performance optimization. For applications that are limited by memory access
Estimation of sinusoidal regression model by stochastic complexity

Stochastic complexity (SC), or equivalently, the negative logarithm of the NML (Normalized Maximum Likelihood) was proven to be successful for the estimation of model structure in the linear quadratic regression problem. Recently, the results have been extended to autoregressive (AR) and autoregressive moving average (ARMA) models, whereas most of the information theoretic methods currently applied for determining the number of sine-waves in additive Gaussian noise still rely on asymptotic two-terms formulae where the ﬁrst term is given by the minus maximum log-likelihood, and the second one is a penalty coeﬃcient that depends on the number of parameters and the sample size. Additionally, the noise is assumed to be white, which is not realistic in most of the practical applications. Our main purpose is to apply sharper approximations of SC for estimating the number of sinusoidal terms in a time series contaminated by AR noise. This is known to be challenging because we have to solve a mixed-spectrum estimation problem. We elaborate on two diﬀerent SC criteria that involve the Fisher information matrix (FIM) of the investigated model. For small and moderate sample sizes, the experimental results show that SC compares favorably with other well-known criteria such as: Bayesian information criterion (BIC), corrected Kullback information criterion (KICc) and the generalized Akaike information criterion (GAIC).
The Effects of One’s individual Cultural Orientation, the Society’s Cultural Orientation, and One’s Sex on the Emergence of Either Pure Depression or Somatization Symptoms

Cross cultural studies indicated that manifestation of depression in somatic complaints was common in non-Western countries that are different from that of Western who generally non-somatic, usually called as pure depression. The differences were assumed to be related to differences in cultural orientation between Western that generally more individualistic and non-Western that more collectivistic. At the individual level, collectivistic orientation is called allocentrism and individualistic is called idiocentrism. From social construction explanation, allocentric tends to develop somatic meaning and idiocentric tends to develop psychological meaning. Some studies also showed that manifestation of depression in somatic symptoms is more commonly found in women than men. The purpose of this study as to examining the role of one‘s individual‘s cultural orientation, society‘s cultural orientation, and one‘s sex in developing somatization and pure depression symptoms of distressed persons. Subjects involved in this study were 185. Research design used was quantitative-qualitative mixed method with dominant-less dominant design conducted in parallel (QUAN + qual). Data were collected by interview, depression scale, somatization scale, and idiocentrism-allocentrism scale. Data were analyzed using Structural Equation Modeling and case study approach. The results showed that: (1) society‘s and one‘s individual‘s cultural orientation have direct effect on the emergence of either somatization or pure depression symptoms; (2) sex has no effect on the emergence of both symptoms; (3) the effect of one‘s individual‘s cultural orientation is moderated by society‘s cultural orientation, but not by one‘s sex; (4) in collectivistic, somatization symptoms are a prodromal of depression and vice versa; (5) in individualistic, there is no correlation between somatization and depression.
Three-Tier Approach for Identifying Malicious Nodes in MANET

In present days, mobile ad hoc networks have grown into an emerging research topic. One of the challenging problem in MANET is securing it in an unsafe environment. In MANET, Innumerable intermediate nodes interchange information without the need of infrastructure. To cooperate with each node all nodes participate in packet forwarding. Due to the inadequate energy of the nodes they do not participate in the routing process correctly. Existing solutions are not enough to secure the wireless network, as there are many vulnerabilities in using ad-hoc networks. To obtain a tolerable amount of security, conventional security mechanisms should relate to an intrusion detection system. We propose a three-tier approach for identifying malicious nodes in the network. Here, in this paper to identify the nature of the nodes as mischievous or genuine three levels are used. The first level is acknowledgement phase it consists of DTQ function, the DTQ value of nodes will be near to a constant or will be dynamic value for a legitimate node and will be continuously decreasing for a mischievous node. The second level is the voting phase, used to confirm the node is malicious. The third level is credit value phase used to identify the malicious nodes among the voter nodes in level two.
Integrating Machine Learning into the UX Design Process

The following paper discusses how machine learning is becoming the new user experience tool for designers. Throughout the last decade, machine learning has vastly improved the user experience and human-machine interfacing by permitting machines to learn who we are and how we would like our systems to communicate with us. Machine learning opens the door for user interface and user experience design opportunities that could further meet users’ needs. To explore this phenomenon, Coupon Buddy was designed using a prototyping strategy to explore how machine learning could classify comments and adapt to user interaction and feedback. More specifically, the application functioned as a research channel to observe how UX designers could improve design processes for better user experiences through the accumulation of machine learning. Coupon Buddy was designed to allow users to save all their coupons in one place and use it for their shopping needs. Not only did the creation of Coupon Buddy prototypes allow us to investigate how much knowledge of machine learning our participants already had, but it facilitated ideas for how machine learning corresponds to a stronger UX design approach.