Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Appendix: “Consumer Choice and Market Outcomes under Ambiguity in Product Quality”

We first show the extension of the demand curve to multiple products by first considering the simplest scenario involving 3 products and 2 customers. We then extend the demand curve involving 3 customers to demonstrate the notion of symmetric predisposition distributions for each product. Finally we will extend our logic to 3 products with continuous mass of consumer, and show the equilibrium market outcomes when 3 products are engaged in symmetric competition. This result is applicable to n products.
Decomposition-Assisted Computational Technique Based on Surrogate Modeling for Real-Time Simulations

The development of complex simulation systems is extremely costly as it requires high computational capability and expensive hardware. As cost is one of the main issues in developing simulation components, achieving real-time simulation is challenging and it often leads to intensive computational burdens. Overcoming the computational burden in a multidisciplinary simulation system that has several subsystems is essential in producing inexpensive real-time simulation. In this paper, a surrogate-based computational framework was proposed to reduce the computational cost in a high-dimensional model while maintaining accurate simulation results. Several well-known metamodeling techniques were used in creating a global surrogate model. Decomposition approaches were also used to simplify the complexities of the system and to guide the surrogate modeling processes. In addition, a case study was provided to validate the proposed approach. A surrogate-based vehicle dynamic model (SBVDM) was developed to reduce computational delay in a real-time driving simulator. The results showed that the developed surrogate-based model was able to significantly reduce the computing costs, unlike the expensive computational model. The response time in surrogate-based simulation was considerably faster than the conventional model. Therefore, the proposed framework can be used in developing low-cost simulation systems while yielding high fidelity and fast computational output.
Introductory mathematical programming for EC

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Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.
SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.
An Object-Oriented Model for Ex Ante Accounting Information

Present accounting data models such as the Resource‐Event‐Agent (REA) model merely focus on the modeling of static accounting phenomena. In this paper, it is argued that these models are not able to provide relevant ex ante accounting data for operations management decisions. These decisions require dynamic descriptions of the consequences of alternative future courses of actions and the resulting events. Therefore, a new object‐oriented model is presented that enables the use of ex ante accounting data for this purpose. In comparison to the REA model, the object‐oriented model presented in this paper includes new static aspects as “recipes,” “potential contracts,” and “reservations,” together with behavioral aspects expressed as theoretical scripts for the retrieval of relevant accounting data.
Pisa: Arbitration Outsourcing for State Channels

State channels are a leading approach for improving the scalability of blockchains and cryptocurrencies. They allow a group of distrustful parties to optimistically execute an application-defined program amongst themselves, while the blockchain serves as a backstop in case of a dispute or abort. This effectively bypasses the congestion, fees and performance constraints of the underlying blockchain in the typical case. However, state channels introduce a new and undesirable assumption that a party must remain online and synchronised with the blockchain at all times to defend against execution fork attacks. An execution fork can revert a state channel's history, potentially causing financial damage to a party that is innocent except for having crashed. To provide security even to parties that may go offline for an extended period of time, we present Pisa, the first protocol to propose an accountable third party who can be hired by parties to cancel execution forks on their behalf. To evaluate Pisa, we provide a proof-of-concept implementation for a simplified Sprites and we demonstrate that it is cost-efficient to deploy on the Ethereum network.
Uncertainty in basic short-term macroeconomic models with angel-daemon games

We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.
Criteria for the CloudSim Environment

CPU is undoubtedly the most important resource of the computer system. Recent advances in software and system architecture have increased processing complexity, as computing is now distributed and parallel. CloudSim represents the complexity of an application in terms of its computational requirements. CloudSim [9] is a complete solution for simulating Cloud Computing environments and building test beds for provisioning algorithms. This paper analyzes and evaluates the performance of cloud environment modeling using CloudSim. We describe the CloudSim architecture and then investigate the new models and techniques in CloudSim.
Composite Learning Sliding Mode Control of Flexible-Link Manipulator

This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.
Improving Deep Learning through Automatic Programming

Deep learning and deep architectures are emerging as the best machine learning methods so far in many practical applications such as reducing the dimensionality of data, image classification, speech recognition or object segmentation. In fact, many leading technology companies such as Google, Microsoft or IBM are researching and using deep architectures in their systems to replace other traditional models. Therefore, improving the performance of these models could make a strong impact in the area of machine learning. However, deep learning is a very fast-growing research domain with many core methodologies and paradigms just discovered over the last few years. This thesis will first serve as a short summary of deep learning, which tries to include all of the most important ideas in this research area. Based on this knowledge, we suggested, and conducted some experiments to investigate the possibility of improving the deep learning based on automatic programming (ADATE). Although our experiments did produce good results, there are still many more possibilities that we could not try due to limited time as well as some limitations of the current ADATE version. I hope that this thesis can promote future work on this topic, especially when the next version of ADATE comes out. This thesis also includes a short analysis of the power of ADATE system, which could be useful for other researchers who want to know what it is capable of.
Toward a Biometric Security Layer in Accounting Systems

This paper discusses theoretical and practical issues related to the use of a biometric‐enabled security layer in accounting systems aimed at enhancing user authentication and reducing control risk. Originating in criminology, biometric technology has matured over the years with applications in diverse disciplines. However, its use in business and accounting is still in its infancy, and many issues about its role in information systems security are unresolved. The paper proposes an access decision framework that draws from the strategy and risk assessment literature to model processes where biometrics might be used to reduce control risk. Despite its potential strengths, biometric technology is not a panacea and represents one element in a portfolio of security mechanisms needed to protect information resources. The paper discusses challenges in implementing biometric technology and identifies avenues for future research.
Self-Organized Societies: On the Sakoda Model of Social Interactions

We characterize the behavior and the social structures appearing from a model of general social interaction proposed by Sakoda. The model consists of two interacting populations in a two-dimensional periodic lattice with empty sites. It contemplates a set of simple rules that combine attitudes, ranges of interactions, and movement decisions. We analyze the evolution of the 45 different interaction rules via a Potts-like energy function which drives the system irreversibly to an equilibrium or a steady state. We discuss the robustness of the social structures, dynamical behaviors, and the existence of spatial long range order in terms of the social interactions and the equilibrium energy.
Improving Power System Stability with Gramian Matrix-Based Optimal Setting of a Single Series FACTS Device: Feasibility Study in Vietnamese Power System

The Vietnamese power system has experienced instabilities due to the effect of increase in peak load demand or contingency grid faults; hence, using flexible alternating-current transmission systems (FACTS) devices is a best choice for improving the stability margins. Among the FACTS devices, the thyristor-controlled series capacitor (TCSC) is a series connected FACTS device widely used in power systems. However, in practice, its influence and ability depend on setting. For solving the problem, this paper proposes a relevant method for optimal setting of a single TCSC for the purpose of damping the power system oscillations. This proposed method is developed from the combination between the energy method and Hankel-norm approximation approach based on the controllability Gramian matrix considering the Lyapunov equation to search for a number of feasible locations on the small-signal stability analysis. The transient stability analysis is used to compare and determine appropriate settings through various simulation cases. The effectiveness of the proposed method is confirmed by the simulation results based on the power system simulation engineering (PSS/E) and MATLAB programs. The obtained results show that the proposed method can apply to immediately solve the difficulties encountering in the Vietnamese power system.
Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent’s own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.
Complexities in Assessing Structural Health of Civil Infrastructures

The complexity in the health assessment of civil infrastructures, as it evolves over a long period of time, is briefly discussed. A simple problem can become very complex based on the current needs, sophistication required, and the technological advancements. To meet the current needs of locating defect spots and their severity accurately and efficiently, infrastructures are represented by finite elements. To increase the implementation potential, the stiffness parameters of all the elements are identified and tracked using only few noise-contaminated dynamic responses measured at small part of the infrastructure. To extract the required information, Kalman filter concept is integrated with other numerical schemes. An unscented Kalman filter (UKF) concept is developed for highly nonlinear dynamic systems. It is denoted as 3D UKF-UI-WGI. The basic UKF concept is improved in several ways. Instead of using one long duration time-history in one global iteration, very short duration time-histories and multiple global iterations with weight factors are used to locate the defect spot more accurately and efficiently. The capabilities of the procedure are demonstrated with the help of two informative examples. The proposed procedure is much superior to the extended Kalman filter-based procedures developed by the team earlier.
Information Transfer Among Internet Firms: The Case of Hacker Attacks

This study focuses on the stock market reaction to denial of service attacks against certain well‐known Internet firms in February 2000. Investors appear to have used several heuristics in deciding which firms were “similar” to those attacked, and thus predicted that they were also likely to be attacked. The primary heuristic employed appears to have been similarity in reliance on the Internet to conduct operating activities (i.e., the set of Internet firms). We find negative mean abnormal returns among Internet firms not actually attacked (i.e., information transfer). This occurred both within Internet industries in which some firms were attacked, and within Internet industries with no attacks. A secondary heuristic appears to have been that Internet firms similar in size to those attacked (i.e., relatively large) were more likely to be attacked. In contrast to all other Internet industries, providers of Internet security products and services experienced positive mean abnormal returns.
Car manufacturer and model recognition based on scale invariant feature transform

Vehicle analysis involves licence plate recognition, vehicle type recognition, and car manufacturer and model recognition. Car manufacturer and model recognition plays an important role in providing supplementary information to licence plate recognition for the unique identification of a car. In this paper, we propose a framework to recognition car manufacturer and its model based on scale invariant feature transform (SIFT). We first detect a moving car using frame differences; the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is then used to identify a car based on SIFT algorithm. Experimental results show that our proposed framework achieves favourable recognition accuracy.
Leveraging Latent Label Distributions for Partial Label Learning

In partial label learning, each training example is assigned a set of candidate labels, only one of which is the ground-truth label. Existing partial label learning frameworks either assume each candidate label of equal confidence or consider the ground-truth label as a latent variable hidden in the indiscriminate candidate label set, while the different labeling confidence levels of the candidate labels are regrettably ignored. In this paper, we formalize the different labeling confidence levels as the latent label distributions, and propose a novel unified framework to estimate the latent label distributions while training the model simultaneously. Specifically, we present a biconvex formulation with constrained local consistency and adopt an alternating method to solve this optimization problem. The process of alternating optimization exactly facilitates the mutual adaption of the model training and the constrained label propagation. Extensive experimental results on controlled UCI datasets as well as real-world datasets clearly show the effectiveness of the proposed approach.
Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding

Network embedding, as an approach to learn low-dimensional representations of vertices, has been proved extremely useful in many applications. Lots of state-of-the-art network embedding methods based on Skip-gram framework are efficient and effective. However, these methods mainly focus on the static network embedding and cannot naturally generalize to the dynamic environment. In this paper, we propose a stable dynamic embedding framework with high efficiency. It is an extension for the Skip-gram based network embedding methods, which can keep the optimality of the objective in the Skip-gram based methods in theory. Our model can not only generalize to the new vertex representation, but also update the most affected original vertex representations during the evolvement of the network. Multi-class classification on three real-world networks demonstrates that, our model can update the vertex representations efficiently and achieve the performance of retraining simultaneously. Besides, the visualization experimental result illustrates that, our model is capable of avoiding the embedding space drifting.
Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.
Learning SMT(LRA) Constraints using SMT Solvers

We introduce the problem of learning SMT(LRA) constraints from data. SMT(LRA) extends propositional logic with (in)equalities between numerical variables. Many relevant formal verification problems can be cast as SMT(LRA) instances and SMT(LRA) has supported recent developments in optimization and counting for hybrid Boolean and numerical domains. We introduce SMT(LRA) learning, the task of learning SMT(LRA) formulas from examples of feasible and infeasible instances, and we contribute INCAL, an exact non-greedy algorithm for this setting. Our approach encodes the learning task itself as an SMT(LRA) satisfiability problem that can be solved directly by SMT solvers. INCAL is an incremental algorithm that achieves exact learning by looking only at a small subset of the data, leading to significant speed-ups. We empirically evaluate our approach on both synthetic instances and benchmark problems taken from the SMT-LIB benchmarks repository.