Miracle-dz/UniFAR-FTUs huggingface dataset:
Assortment and inventory optimization : from predictive choice models to near-optimal algorithms

Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that achieve essentially best-possible factors with respect to the price ratio, size of customers’ consideration sets, etc. Second, we develop a novel tractable approach to choice modeling, in the vein of nonparametric models, by leveraging documented assumptions on the customers’ consider-then-choose behavior. We show that the assortment optimization problem can be cast as a dynamic program, that exploits the properties of a bi-partite graph representation to perform a state space collapse. Surprisingly, this exact algorithm is provably and practically efficient under common consider-then-choose assumptions. On the estimation front, we show that a critical step of standard nonparametric estimation methods (rank aggregation) can be solved in polynomial time in settings of interest, contrary to general nonparametric models. Predictive experiments on a large purchase panel dataset show significant improvements against common benchmarks. Third, we turn our attention to joint assortment optimization and inventory management problems under dynamic customer choice substitution. Prior to our work, little was known about these optimization models, which are intractable using modern discrete optimization solvers. Using probabilistic analysis, we unravel hidden structural properties, such as weak notions of submodularity. Building on these findings, we develop efficient and yet conceptually-simple approximation algorithms for common parametric and nonparametric choice models. Among notable results, we provide best-possible approximations under general nonparametric choice models (up
Redirection on Mixed Reality Walking Surfaces

Redirection techniques are a compelling way to enable natural walking in immersive virtual environments when physical space is limited. It is challenging to employ redirection in mixed reality environments, however, because these techniques disrupt the spatial alignment between virtual objects and corresponding real world props. In this paper, we adapt a recently developed redirection technique based on change blindness for use in mixed reality environments. In contrast to continuous redirection techniques that rely on rotation or translation gains, this approach shifts the environment between discrete structural states. Thus, users can walk repeatedly to physical objects and stimuli in fixed real world positions, and they will perceive that they are interacting with separate virtual objects. We describe a proof-of-concept virtual environment that redirects users over real world concrete and gravel walking surfaces as they explore multiple buildings in an expansive outdoor scene. Our initial qualitative observations suggest that this passive haptic feedback contributes to the illusion of being in a virtual environment that is considerably larger than the physical space.
Review of Time Domain Waveguide Modes in Perspective of Evolutionary Approach to Electromagnetics (EAE)

ARTICLE INFO ABSTRACT Article history: Received 30 December 2012 Accepted 1 February 2013 Available online 6 February 2013 A problem for electromagnetic fields is considered by a direct analytical time-domain method called Evolutionary Approach to Electromagnetics (EAE). The problem is solved analytically in compliance with a causality principle. The method for timedomain modes has brought a breath of fresh. EAE method is most useful and available one by means of t ∂ time derivative. The method was developed at the end of 80s by O.A. Tretyakov. This study is distinguished a few studies via the method in the last decades. The studies are demonstrated for time-domain waveguide modes.
Universal graph literacy: understanding how blind and low vision students can satisfy the common core standards with accessible auditory graphs

Figure 22: Code for importing a saved graph file (abstraction), then exporting an image (reification). is a software library for developer implementation underneath end-user software. Danny could be using the Sonification Sandbox, a web-based alternative, or a completely separate program6. Part of this flexibility stems from a common model, the AGM, and part stems from the conversion tools found in the AGFC. Many abstractors and reifiers are provided as part of the core AGFC. Table 8 provides an overview. The translation architecture has a few major components. First, every translator object is either an Abstractor or a Reifier. An abstractor takes an argument which captures the input artifact, and outputs a graph model. A reifier takes in a graph model and an output target, and sets up the output at the target. As mentioned before, Abstractors and Reifiers are Translators, and have a lot in common. Translators must be triggered to begin. The translation can be threaded or unthreaded (in other words, it can be a background task, behind other software activities). The translator must provide its basic status, such as ready, translating, or done. The translator can also provide other information, such as the approximate completion percent. This can then be shown to the user, in user interface widgets such as progress bars. It may be apparent in the example above that it is common to convert to a lot of reified formats from a single model. During the process of understanding the data or saving it for Since the development of the AGE, the Sonification Sandbox code has become much simpler. Since it no longer has model or conversion functions, its major components make options visible to the user.
Big Data Sanitization Using Scalable In-Memory Frameworks

As more and more data is collected, it is growing beyond the scale humans could ever have imagined. Not only data but also data collection and analysis techniques have evolved and have enabled researchers to advance many fields such as medical science. Although health data can have a huge impact on the future success of research, data is usually distributed among various stakeholders. Organizations need to share this data to help research move forward, but health data sharing is a regulated domain. Due to privacy concerns, the U.S. Department of Health and Human Services (HHS) has taken steps to ensure privacy protection of individuals by regulating data sharing through Health Insurance Portability and Accountability Act of 1996 (HIPAA). HIPAA policy restricts publishers from sharing identification information as well as any auxiliary information that can be used for record re-identification. To make data sharing compliant with the HIPAA policy, various data privacy protection techniques evolved. Differential privacy techniques focused on query accuracy maximization in statistical databases while minimizing the “risk” of record identification, whereas, data anonymization allows the publisher to share original data at lesser precision, i.e., sharing attribute value of age as 25-35 instead of 30. These techniques are considered as an industry standard.
Towards Human-Like Robustness in an Intelligent Tutoring System

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few humanlike attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.
Advanced security schemes in sensor networks and mobile networks = 센서 네트워크와 이동 통신망을 위한 보안 기술 연구

Recent advance of wireless sensor network and mobile communication network technologies bring several new issues such as the mobility of sensor nodes, the deployment of PKI. Moreover, the convergence of such different networks are one of rising issues. Mobility of sensor node in Wireless Sensor Networks (WSN) brings security issues such as re-authentication and tracing the node movement. However, current security research on WSN are insufficient to support such environments since their designs only considered static environments. In this thesis, we propose efficient node authentication and key exchange protocols that reduces the overhead in node re-authentication and also provides untraceability of mobile nodes. We propose not only symmetric key based authentication protocol, but also the asymmetric key based authentication protocol for supporting the advanced sensor technologies. Compared with previous protocols, our protocol has only a third of communication and computational overhead. We expect our protocol to be an efficient solution that increases the lifetime of sensor network. We also propose the sensor node authentication protocol in the 3G-WSN network that the sensor network is integrated with mobile network. For the deployment of PKI into the next generation mobile network, we propose the security protocols for the application security architecture, Voice over IP application, and the lawful interception protocol deploy the ID based cryptosystem. In each chapter we analyze the efficiency and the security of our protocols by comparing with previous protocols.
Real-time human feature acquisition and human tracking by omnidirectional image sensor

Recently, research into personal identification and the automation of crime-prevention systems or room access administration systems has gained prominence. It is seen as being desirable to identify many people at the same time without any specific focus. Thus, the use of an omnidirectional image sensor called Hyper Omni Vision is effective, since this image sensor can observe a 360 degree field of view. In this paper, we propose a real-time human feature acquisition and human face tracking method from an omnidirectional image sequence. The characteristic of this method is not only the tracking of faces but also the memorization of useful face images for personal identification.
Segmentation of White Blood Cells Using Fuzzy C Means Segmentation Algorithm

The objective of this paper is to develop a tool to identify the white blood cells from blood smear images through segmentation. In our research work, we have identified white blood cells from color images of blood smears. We have used Fuzzy C Means segmentation method and erosion and dilation post processing method to identify the white blood cells. In our obtained result we find that this method successfully identified 98% of white blood cells from which is a satisfactory result.
EFL Textbook Analysis

The purpose of the present study was to determine how well a nationally mandated Malaysian EFL Upper Secondary textbook prepares upper secondary students for tertiary reading in English. The data were in the form of comprehension reading passages in the selected EFL textbook. In this study, reading instructional design in the EFL textbook was analyzed in terms of types and the grade-level length of passages. Using the data acquired from an analysis of this mandated textbook, the author argues that the ESL reading instructional design in the national Malaysian EFL Upper Secondary curriculum significantly under prepares students because of its overemphasis on narrative passages that are below grade-level texts.
Enhancing the Narrow-down Approach to Large-scale Hierarchical Text Classification with Category Path Information

The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.
The Dynamic Simulation of Transient Systems Described by Index Two Differential-algebraic Equations

Index two Differential-Algebraic Equations arise in the mathematical modelling of Chemical Engineering systems under transient conditions. The problems associated with the numerical solution of such mathematical systems are discussed. Particular emphasis is placed on the strategies employed to control the integration errors. The unsuitability of error control techniques traditionally used for ordinary differential equation systems is demonstrated. Possible alternatives and their practical implementation are discussed. Numerical results arising from the solution of an index two DAE system describing a plate distillation column are presented.
BAYESIAN NETWORKS FOR DETECTION OF VEHICLES UTILIZING PIXEL INSIGHTFUL GROUPING APPROACH

Abstract: In this paper we proposed an Automatic Vehicle identification framework For Aerial observation. In this framework, a pixel saliently transcription approach for vehicle location is proposed. From the survivable system of finite designation in flying reconnoiter; orders in illumination of locale and sliding window are gotten away. Since the principle drawback is that a vehicle has a tendency to be isolated as various districts when Used to different chromaticity, besides every one of the vehicles are may be gathered as single area in the event that they are comparable . To leave this issue, a Dynamic Bayesian Network (DBN) for vehicle recognition in flying reconnaissance is executed, which depends on the pixel-wise arrangement approach. This pixel-wise grouping protected with the trademark mining process. The highlights are enclosed with vehicle blending and neighborhood the highlights. Hence execution these highlights a Dynamic Bayesian Network (DBN) is worked for the stereotype reason, it changes the local nearby highlights into quantitative location. This investigation is went with a few elevated recordings and the created system is testing the issue with aeronautical observation pictures taken at different statures under the unique point of camera.
Techniques for low power analog, digital and mixed signal CMOS integrated circuit design

ii ACKNOWLEDGEMENTS I would like to dedicate my work to my parents, Mr. Jinquan Zhang and Mrs. Lingyi Yu and my wife Yiqian Wang, for their constant encouragement throughout my life. I am very grateful to my advisor Dr. Ashok Srivastava for his guidance, patience and understanding throughout this work. His suggestions, discussions and constant encouragement have helped me to get a deep insight in the field of Mixed Signal IC design. supporting me financially during my stay at LSU. I take this opportunity to thank my friends Tinghui, Chi, Anand for their help and encouragement at times when I needed them. I would also like to thank all my friends here who made my stay at LSU an enjoyable and a memorable one. Last of all I thank God for keeping my family in good health and spirits throughout my stay at LSU. Figure 1.1: MOSFET as a three-terminal device (a) n-MOSFET (b) p-Figure 1.2: CMOS inverter under (a) body-source short and (b) forward-body bias conditions. Figure 2.6: Dependence of calculated and measured magnitude of threshold voltage in n-MOSFET and p-MOSFET with magnitude of forward body-bias |V BS |. 30 Figure 2.11: Input bias current versus bias voltage for the current mirror circuits of Figs. 2.8, 2.9 and 2.10. (a) Basic current mirror circuit of Fig. 2.8, (b) Level shift current mirror circuit of Fig. 2.9 and (c) New level shift current mirror circuit of Fig. 2. Figure 3.1: Equivalent noise voltage spectral density variation with forward body-bias corresponding to shot noise (dotted line) and thermal noise (solid line) in an n-MOSFET.
An Effective Implementation of Multi-Path Routing Protocol in Wireless Mesh Networks

This paper's contribution is a multi-path routing protocols that emphasize different aspects of multi-path routing. Multi-path protocols, which can provide larger bandwidth to the WMN. The network performance (end-to-end delay and packet loss ratio) compares favorably to the single-path DSR protocol, though as might be expected there is a trade-off against control packet overhead. In a WMN, the traffic load tends to be unevenly distributed over the network. We design a routing scheme which maximizes the utility, i.e., the degree of user satisfaction, by using the dual decomposition method. The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The proposed routing scheme in a fully distributed way. With the proposed scheme, a WMN(1) is divided into multiple clusters for load control. A cluster head estimates traffic load in its cluster. Based on the routing metrics, user traffic takes a detour to avoid overloaded areas, and as a result, the WMN achieves global load balancing.
EXPANSION IN VALORIZATION BASED CONFINING

Colorization is a process of adding colors to a black and white image. The main task in colorization based compression is to automatically extract these few representative pixels in the encoder. In other words, the encoder selects the pixels required for the colorization process, which are called representative pixels (RP) and maintains the color information only for these RP. The position vectors and the chrominance values are sent to the decoder only for the RP set together with the luminance channel, which is compressed by conventional compression techniques. Then, the decoder restores the color information for the remaining pixels using colorization methods.
Time series segmentation by Cusum, AutoSLEX and AutoPARM methods

Time series segmentation has many applications in several disciplines as neurology, cardiology, speech, geology and others. Many time series in this fields do not behave as stationary and the usual transformations to linearity cannot be used. This paper describes and evaluates different methods for segmenting non-stationary time series. We propose a modification of the algorithm in Lee et al. (2003) which is designed to searching for a unique change in the parameters of a time series, in order to find more than one change using an iterative procedure. We evaluate the performance of three approaches for segmenting time series: AutoSLEX (Ombao et al., 2002), AutoPARM (Davis et al., 2006) and the iterative cusum method mentioned above and referred as ICM. The evaluation of each methodology consists of two steps. First, we compute how many times each procedure fails in segmenting stationary processes properly. Second, we analyze the effect of different change patterns by counting how many times the corresponding methodology correctly segments a piecewise stationary process. ICM method has a better performance than AutoSLEX for piecewise stationary processes. AutoPARM presents a very satisfactory behaviour. The performance of the three methods is illustrated with time series datasets of neurology and speech.
ROLE OF META-HEURISTIC ALGORITHMS IN CLOUD COMPUTING

Efficient Resource Scheduling in cloud has become a challenge for Cloud Service Providers. Resource Scheduling should be such that it minimizes the overall resource cost, minimizes the overall execution time, increases throughput, fails less frequently, balances the load on server and delivers the best possible QoS. The algorithms that help in achieving these aims while doing an efficient resource scheduling come under the category of meta-heuristic algorithms. Some of the examples are Genetic Algorithms, Ant Colony Optimization, and Particle Swarm Optimization etc.
Collecting neurophysiological data to investigate users’ cognitive states during game play

This paper explores the potential of collecting neurophysiological data in order to further understand user’s learning experience. The experimental setup involves collecting electroencephalographic signal (EEG) from the brain cortex to infer users’ cognitive state while they played an educational video game designed to support the learning of Newtonian mechanics. Preliminary results suggest that this neuroscience perspective is quite promising in the idea of quantitatively characterizing users’ learning experience. This could be an innovative and promising avenue in general game development or in educational videogame research field.
A perfectly matched layer for the absorption of electromagnetic waves

A new technique of free-space simulation has been developed for solving unbounded electromagnetic problems with the finite-difference time-domain method. Referred to as PML, the new technique is based on the use of an absorbing layer especially designed to absorb without reflection the electromagnetic waves. The first part of the paper presents the theory of the PML technique. The second part is devoted to numerical experiments and to numerical comparisons with the previously used techniques of free-space simulation. These comparisons show that the PML technique works better than the others in all cases; using it allows us to obtain a higher accuracy in some problems and a release of computational requirements in some others.
Animation of Facial Expressions by Physical Modeling

In this paper, we propose a physically-based 3D dynamic facial model based on anatomical knowledge for realistic facial expression animation. The facial model incorporates a physically-based approximation to facial skin tissue and a set of anatomically-motivated facial muscle actuators. The tissue model has multilayered mass-spring structure which approximates different types of facial tissue. Two kinds of biphasic springs, structural springs and shear springs, are included in our model to simulate nonlinear elastic behavior of the skin. Facial muscle models are presented to emulate facial muscle contraction. In the muscle model, two factors, the muscle force scaling factor and muscle strength factor provide us macro and micro control of the muscle inﬂuence respectively. Based on the facial anatomy, these contractile muscles are inserted at anatomically correct position within the dynamic skin model. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscle forces. The dynamic facial animation algorithm runs at interactive rate with continuous 3D display on a graphics workstation.