Reviewerly/exHarmony huggingface dataset:
Effects of Managerial Overconfidence on the Relationship between Corporate Risk-Taking and Firm Value

<Abstract>This study investigates two issues pertinent to managerial overconfidence (hereafter, MOC) and risk-taking from the perspective of behavioral corporate finance: (1) Does MOC influence the relationship between corporate risk-taking and firm value?(2) Can the effects of MOC be moderated through certain internal and external governance or personal factors?Using the KOSPI for non-financial firms from 2002 to 2015, this study examines the issues by identifying the firms with MOC from the measurement variables that are related to both the determinants and outcome of MOC.The main findings are as follows.First, firms with overconfident managers tend to experience lower firm value compared to those with nonoverconfident managers because of their inappropriate or excessive risk-taking behavior.This finding suggests that in addition to agency problems, MOC can have negative effects on the relationship between risk-taking and firm value in Korean firms.Second, there are no significant internal or external governance control effects, such as outside directors' control over obligations, a sound board of directors, the control of chaebols, a less-competitive indus-try, and less economic policy uncertainty over the negative effects of MOC, on the relation-ship between risk-taking and firm value.In contrast, the negative effects of MOC turn weak in terms of personal factors when the managers become older.These results show that con-trolling for the negative effects of MOC through internal or external governance mechanisms is not easy, even though these mechanisms may be helpful to control for the agency problem in Korean firms.Nevertheless, the results confirm that the effects of MOC depend on managers' personal traits.This study shows that managers should be wary of their own overconfidence and restrain themselves from making irrational decisions.Furthermore, this study suggests that the board of directors needs to react against MOC and improve the board's control over MOC in order to avoid its negative effects.
A self-adaptive mechanism using weibull probability distribution to improve metaheuristic algorithms to solve combinatorial optimization problems in dynamic environments

In last decades, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristics have been used to find good solutions in a reasonably low time, and the use of self-adaptive strategies has increased considerably due to these kind of mechanism proved to be a good alternative to improve performance in these algorithms. On this research, the performance of a genetic algorithm is improved through a self-adaptive mechanism to solve dynamic combinatorial problems: 3-SAT, One-Max and TSP, using the genotype-phenotype mapping strategy and probabilistic distributions to define parameters in the algorithm. The mechanism demonstrates the capability to adapt algorithms in dynamic environments.
Production processes of official statistics and analytics processes augmented by trusted smart statistics: Friends or foes?

National statistical institutes are using frameworks to organise and set up their official statistical production, e.g. GSBPM. As a sequential approach of statistical production, GSBPM has become a well-established standard using deductive reasoning as analytics’ paradigm. For example, the first GS BPM steps are entirely focused on deductive reasoning based on primary data collection and are not suited for inductive reasoning applied to (already existing) secondary data (e.g. big data resulting, for example, from smart ecosystems). Taken into account the apparent potential of big data in the official statistical production, the GSBPM process needs to adapted to incorporate both complementary approaches of analytics (i.e. inductive and deductive reasoning) and, for example, through the usage of, for example, data-informed continuous evaluation at any GSBPM step. This paper discusses the limitations of GSBPM with respect to the usage of big data (using inductive reasoning as analytics’ paradigm), and also with respect to trusted smart statistics. The authors give insights on how to augment and empower current statistical production processes by analytics, and also by (trusted) smart statistics. In addition, the paper also highlights challenges and opportunities that should be addressed to embrace this major paradigm shift.
Pengujian Usability dengan Teknik System Usability Scale pada Test Engine Try Out Sertifikasi

Test engine try out is an information system used by prospective alumni students to practice the questions in taking the certification exams at Bina Darma University. Exercises that can be done on this test engine are vendor certifications such as Microsoft Technology Associate, SAP, NIIT, PASAS, CISCO, and various other certifications. To ascertain whether the test engine is in line with the expectations and needs of the participants try out then an evaluation is carried out. The evaluation technique used is usability with the system usability scale approach. System usability scale is an information system evaluation technique that looks at three aspects, namely adjective rating, grade scale, acceptability by involving end users in the evaluation process. In the process of evaluating the usability scale system, ten instruments were used as a measure of evaluation. From the evaluation, the final result is 87.33. According to these conditions, it can be concluded that the test engine try out from the side of the adjective rating includes the excellence group, from the grade scale side including B group, and from the acceptability side including the acceptable group.
Generalized Boolean Functions and Quantum Circuits on IBM-Q

We explicitly derive a connection between quantum circuits utilising IBM's quantum gate set and multivariate quadratic polynomials over integers modulo 8. We demonstrate that the action of a quantum circuit over input qubits can be written as generalized Walsh-Hadamard transform. Here, we derive the polynomials corresponding to implementations of the Swap gate and Toffoli gate using IBM-Q gate set.
The Parameters of Minimal Linear Codes

Let $k\leq n$ be two positive integers and $q$ a prime power. The basic question in minimal linear codes is to determine if there exists an $[n,k]_q$ minimal linear code. The first objective of this paper is to present a new sufficient and necessary condition for linear codes to be minimal. Using this condition, it is easy to construct minimal linear codes or to prove some linear codes are minimal. The second objective of this paper is to use the new sufficient and necessary condition to partially give an answer to the basic question in minimal linear codes. The third objective of this paper is to present four classes of minimal linear codes, which generalize the results about the binary case given in [X. Li and Q. Yue, Des. Codes Cryptogr., 2019]. One can find that our method is much easier and more effective.
Training Neural Networks for Likelihood/Density Ratio Estimation

Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are assumed known or to belong to some known parametric family. In a data-driven version we replace this requirement with the availability of data sampled from the densities of interest. For most well known problems in Detection and Hypothesis testing we develop solutions by providing neural network based estimates of the likelihood ratio or its transformations. This task necessitates the definition of proper optimizations which can be used for the training of the network. The main purpose of this work is to offer a simple and unified methodology for defining such optimization problems with guarantees that the solution is indeed the desired function. Our results are extended to cover estimates for likelihood ratios of conditional densities and estimates for statistics encountered in local approaches.
Epona and the Obfuscation Paradox

Code obfuscation aims at protecting the intellectual property of applications delivered in an unmanaged environment. The process usually involves selecting and successively applying various transformation techniques, for instance at compile time, with the goal of providing a good trade-off between protection and performance. However, solving this problem is known to be very difficult as the selection of the assets to protect is application and context dependent, the number of transformation combinations can be overwhelming, and modeling the transformation combinations impact on performances can be tricky.
Hand Gesture Classification Based on Nonaudible Sound Using Convolutional Neural Network

Recognizing and distinguishing the behavior and gesture of a user has become important owing to an increase in the use of wearable devices, such as a smartwatch. This study is aimed at proposing a method for classifying hand gestures by creating sound in the nonaudible frequency range using a smartphone and reflected signal. The proposed method converts the sound data, which has been reflected and recorded, into an image within a short time using short-time Fourier transform, and the obtained data are applied to a convolutional neural network (CNN) model to classify hand gestures. The results showed classification accuracy for 8 hand gestures with an average of 87.75%. Additionally, it is confirmed that the suggested method has a higher classification accuracy than other machine learning classification algorithms.
Calibrating Expert Assessments Using Hierarchical Gaussian Process Models

Expert assessments are routinely used to inform management and other decision making.However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible.Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult.Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter.The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background.We follow the fully Bayesian approach (the so-called supra-Bayesian approach) and model experts' bias functions explicitly using hierarchical Gaussian processes.We show how to use calibration data to infer the experts' observation models with the use of bias functions and to calculate the bias corrected posterior distributions for an unknown system parameter of interest.We demonstrate and test our model and methods with simulated data and a real case study on data-limited fisheries stock assessment.The case study results show that experts' biases vary with respect to the true system parameter value and that the calibration of the expert assessments improves the inference compared to using uncalibrated expert assessments or a vague uniform guess.Moreover, the bias functions in the real case study show important differences between the reliability of alternative experts.The model and methods presented here can be also straightforwardly applied to other applications than our case study.
The use of the Synthesia application to simplify Angklung learning

Abstract The majority of students in the department of pre-service elementary school teacher cannot read the notation correctly. However, they have a strong motivation to learn music composition, such as angklung music composition. This paper aims to analyze the development of students who are members of the music community in studying the composition of angklung music by using the Synthesia application. The research method was carried out in the following stages: (1) making angklung musical composition in the type of MIDI (Musical Instrument Digital Interface) files; (2) input MIDI files for the Synthesia application; (3) setting up the Synthesia application; (4) learning Angklung using the Synthesia application; and (5) evaluating the learning process. Research subjects were students who were members of the music community on campus, so that their age and musical abilities were very heterogeneous. The research subjects were 15 people, as many as one angklung player group. The results showed that angklung learning for students who had the low notation reading ability, became easier by using a touch of technology. Students can read, visual-based notation correctly. It can be concluded; that technology can provide the convenience in the process of learning music composition, especially in angklung music composition.
T2WML: Table To Wikidata Mapping Language

The web contains millions of useful spreadsheets and CSV files, but these files are difficult to use in applications because they use a wide variety of data layouts and terminology. We present Table To Wikidata Mapping Language (T2WML), a language that makes it easy to map and link arbitrary spreadsheets and CSV files to the Wikidata data model. The output of T2WML consists of Wikidata statements that can be loaded in the public Wikidata knowledge base or in a Wikidata clone repository, creating an augmented Wikidata knowledge graph that application developers can query using SPARQL.
Agent‐based negotiation approach for feature interactions in smart home systems using calculus of the context‐aware ambient

Abstract Smart home systems (SHSs) provide several services that are tailored to different residents' preferences. As a result, SHSs are highly exposed to undesirable interactions, known as feature interactions (FIs). FIs might occur as a result of a conflict in services' goals or a conflict with residents' preferences. Previous studies have proposed solutions based on applying priorities, in which some services or preferable features are disabled in favor of other services. Alternatively, the agent‐based negotiation approach (ABNA) utilizes agents and applies negotiation, enabling services with contrary features to work simultaneously. ABNA avoids applying priority between services or house residents' preferences whenever a space for a compromise exists. The mechanism of ABNA is based on the use of a hierarchy of features based on their contribution to the function of the service or on the importance of these features to house residents. To achieve a compromise between conflicting services, ABNA models services and residents by using agents, and implements a negotiation algorithm that allows services with conflicting features to work simultaneously. This paper presents a description of ABNA with a formal specification of ABNA in the calculus of context‐aware ambient (CCA). This enables the formal analysis of ABNA by using the execution environment of CCA.
Knowledge Distillation for Incremental Learning in Semantic Segmentation

Deep learning architectures have shown remarkable results in scene understanding problems, however they exhibit a critical drop of performances when they are required to learn incrementally new tasks without forgetting old ones. This catastrophic forgetting phenomenon impacts on the deployment of artificial intelligence in real world scenarios where systems need to learn new and different representations over time. Current approaches for incremental learning deal only with image classification and object detection tasks, while in this work we formally introduce incremental learning for semantic segmentation. We tackle the problem applying various knowledge distillation techniques on the previous model. In this way, we retain the information about learned classes, whilst updating the current model to learn the new ones. We developed four main methodologies of knowledge distillation working on both output layers and internal feature representations. We do not store any image belonging to previous training stages and only the last model is used to preserve high accuracy on previously learned classes. Extensive experimental results on the Pascal VOC2012 and MSRC-v2 datasets show the effectiveness of the proposed approaches in several incremental learning scenarios.
An intelligent agent of finite state machine in educational game “Flora the Explorer”

Abstract Game simulation-based education is designed to simulate the existing problems in order to obtain knowledge that can be used to solve the problem. Simulation games with educational purposes can be used as a medium of education that has the learning patterns of learning by doing. Based on the pattern which is owned by the game, players are required to learn in order to solve existing problems. In making the educational game “Flora the Explorer Introduction to Plant with Android-Based Finite State Machine”. Applications used in making the game is Swish max4 and the shuffle algorithm random expected this game does not become monotonous, and players cannot remember the position of the bubble in the game, then the game agent using the Finite State Machine (FSM), the game agent will give notice to when the players answer wrong or right in the game
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible perturbations. Existing attack methods for k-nearest neighbor~(kNN) based algorithms either require large perturbations or are not applicable for large k. To handle this problem, this paper proposes a new method called AdvKNN for evaluating the adversarial robustness of kNN-based models. Firstly, we propose a deep kNN block to approximate the output of kNN methods, which is differentiable thus can provide gradients for attacks to cross the decision boundary with small distortions. Second, a new consistency learning for distribution instead of classification is proposed for the effectiveness in distribution based methods. Extensive experimental results indicate that the proposed method significantly outperforms state of the art in terms of attack success rate and the added perturbations.
Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm

The recent information explosion may have many negative impacts on college students, such as distraction from learning and addiction to meaningless and fake news. To avoid these phenomena, it is necessary to verify the students’ state of mind and give them appropriate guidance. However, many peculiarities, including subject focused, multiaspect, and low consistency on different samples’ interests, bring great challenges while leveraging the mainstream opinion mining method. To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student’s life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students’ state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. A pipeline is proposed to relieve overfitting during the collected information training. First, the singular value decomposition is used in pretreatment of data set which includes outlier detection and dimension reduction. Then, the genetic algorithm is introduced in the training process to find the proper initial parameters of network, and in this way, it can prevent the network from falling into the local minimum. A method of calculating the importance of students’ features is also proposed. The experiment result shows that the new pipeline works well, and the predictor has high accuracy on predicting fresh samples. The design procedure and the prediction design will provide suggestions to deal with students’ state of mind and the college’s public opinion.
Learning to Synthesize Fashion Textures

Existing unconditional generative models mainly focus on modeling general objects, such as faces and indoor scenes. Fashion textures, another important type of visual elements around us, have not been extensively studied. In this work, we propose an effective generative model for fashion textures and also comprehensively investigate the key components involved: internal representation, latent space sampling and the generator architecture. We use Gram matrix as a suitable internal representation for modeling realistic fashion textures, and further design two dedicated modules for modulating Gram matrix into a low-dimension vector. Since fashion textures are scale-dependent, we propose a recursive auto-encoder to capture the dependency between multiple granularity levels of texture feature. Another important observation is that fashion textures are multi-modal. We fit and sample from a Gaussian mixture model in the latent space to improve the diversity of the generated textures. Extensive experiments demonstrate that our approach is capable of synthesizing more realistic and diverse fashion textures over other state-of-the-art methods.
Human Body Poses Recognition Using Neural Networks with Data Augmentation

The continuous development of technology of IT enabled computers to see and learn.There are many viable applications for computer learning and vision to solve new tasks.In this paper, we propose a framework, able of automatically perceiving the human body poses from a single image, acquired by a traditional low-cost camera.Our methodology exploiting the vision of computers features and neural networks to detect and recognize a human from an image.The processing start with detection human in image and then extracting the silhouette from an image then using a neural network to recognize body poses based on silhouettes that extracted.To classified the detected silhouettes with body poses, the neural network was trained with dataset of preprocessed images contains silhouettes and labelled using 0, 1 for standing and walking poses respectively labelled , according to our outcomes that resulted in an accuracy over 93% with the best neural network model.The proposed approach offers promising effects with accepted accuracy.This knowledge will help greatly in decision-making and provide independent vehicles with valuable information for their artificial intelligence to process and make decisions as regards the movement of pedestrians.
A Deep Learning Paradigm for Automated Face Attendance

In this paper, we propose an end-to-end automatic face attendance system using Convolutional Neural Networks (CNNs). Attendance of a student plays an important role in any academic organization. Manual attendance system is very time consuming and tedious. On the other hand, automatic attendance system through face recognition using CCTV camera can be fast and can reduce the man-power involved in that process. Here, we have pipelined one of the best existing architectures such as: (i) Single Image Super-Resolution Network (SRNet) for image super-resolution, (ii) MTCNN for face detection and (iii) FaceNet for face recognition in order to come up with a novel idea of marking attendance. Due to poor video quality of CCTV camera, it becomes difficult to detect and recognize faces accurately and this may reduce the attendance accuracy. To overcome this limitation, we propose a CNN framework called SRNet which super-resolves a given low resolution (LR) image and also increases the face recognition accuracy. We make use of five different datasets i.e. RAISE and DIV2K for SRNet, VGGface2 for FaceNet, LFW and our own dataset for testing and validation purpose. The proposed face attendance system displays a sheet which consists of a list of absent and present persons and the overall attendance record. Our experimental results show that the proposed approach outperforms other existing face attendance approaches.