Reviewerly/exHarmony huggingface dataset:
The Determinant of Bitcoin Prices in Korea

This paper analyzes the factors affecting the price of Bitcoin which has recently received a lot of attention.Specifically, we distinguish three factors of supply and demand, real economic factors, and psychological factors, and examine the effect of these variables on Bitcoin price.This is related to the issues that crypto-currency can function like real money, or it can replace financial investment assets such as existing stock markets as an investment alternative, what effect it has on the real economy, and it will rely on investors' attention and psychology.The results of this study show that the price of Bitcoin is more influenced by investors' attention and psychological factors such as Naver trend index and newspaper articles, rather than being influenced by rational variables such as supply and demand variables or real economic variables.This means that Bitcoin fever and downfall were more likely to be irrational bubbles than were supported by economic value.
Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space

The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.
Static Detection of Uninitialized Stack Variables in Binary Code

More than two decades after the first stack smashing attacks, memory corruption vulnerabilities utilizing stack anomalies are still prevalent and play an important role in practice. Among such vulnerabilities, uninitialized variables play an exceptional role due to their unpleasant property of unpredictability: as compilers are tailored to operate fast, costly interprocedural analysis procedures are not used in practice to detect such vulnerabilities. As a result, complex relationships that expose uninitialized memory reads remain undiscovered in binary code. Recent vulnerability reports show the versatility on how uninitialized memory reads are utilized in practice, especially for memory disclosure and code execution. Research in recent years proposed detection and prevention techniques tailored to source code. To date, however, there has not been much attention for these types of software bugs within binary executables. In this paper, we present a static analysis framework to find uninitialized variables in binary executables. We developed methods to lift the binaries into a knowledge representation which builds the base for specifically crafted algorithms to detect uninitialized reads. Our prototype implementation is capable of detecting uninitialized memory errors in complex binaries such as web browsers and OS kernels, and we detected 7 novel bugs.
Face Recognition of Low-Resolution Video Using Gabor Filter &amp; Adaptive Histogram Equalization

The increasing of CCTV usage for handling security's problems has prompted the demand for face recognition system. Therefore, face recognition with low-resolution data from CCTV is needed. In this study, Viola Jones, Gabor filter and Support Vector Machine (SVM) is used for face detection, feature extraction, and classification, respectively. Adaptive Histogram Equalization (AHE) is applied in the preprocessing stage to increase the recognition accuracy. With the implementation of AHE, the accuracy is increased from 82.85% to 97.14% for class (person) 1. The data in this study consist of 11 classes (people) where each of them has 50 training data and 70 testing data with the image size of 32x32 pixel.
Multi-Step Greedy and Approximate Real Time Dynamic Programming

Real Time Dynamic Programming (RTDP) is a well-known Dynamic Programming (DP) based algorithm that combines planning and learning to find an optimal policy for an MDP. It is a planning algorithm because it uses the MDP's model (reward and transition functions) to calculate a 1-step greedy policy w.r.t.~an optimistic value function, by which it acts. It is a learning algorithm because it updates its value function only at the states it visits while interacting with the environment. As a result, unlike DP, RTDP does not require uniform access to the state space in each iteration, which makes it particularly appealing when the state space is large and simultaneously updating all the states is not computationally feasible. In this paper, we study a generalized multi-step greedy version of RTDP, which we call $h$-RTDP, in its exact form, as well as in three approximate settings: approximate model, approximate value updates, and approximate state abstraction. We analyze the sample, computation, and space complexities of $h$-RTDP and establish that increasing $h$ improves sample and space complexity, with the cost of additional offline computational operations. For the approximate cases, we prove that the asymptotic performance of $h$-RTDP is the same as that of a corresponding approximate DP -- the best one can hope for without further assumptions on the approximation errors. $h$-RTDP is the first algorithm with a provably improved sample complexity when increasing the lookahead horizon.
Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting

We consider the task of predicting the solar power generated by a photovoltaic system, for multiple steps ahead, from previous solar power data. We propose DEN-PF, a dynamic heterogeneous ensemble of prediction models, which weights the individual predictions by considering two components – the ensemble member’s error on recent data and its predicted error for the new time points. We compare the performance of DEN-PF with dynamic ensembles using only one of these components, a static ensemble, the single models comprising the ensemble and a baseline. The evaluation is conducted on data for two years, sampled every 5 min, for prediction horizons from 5 to 180 min ahead, under three prediction strategies: direct, iterative and direct-ds, which uses downsampling. The results show the effectiveness of DEN-PF and the benefit of considering both error components for the direct and direct-ds strategies. The most accurate prediction model was DEN-PF using the direct-ds strategy.
Autonomic Cloud Placement of Mixed Workload: An Adaptive Bin Packing Algorithm

Cloud computing offers a platform where virtual entities, such as virtual machines, containers, and pods, are hosted in a physical infrastructure. Such virtual entities request resources, such as CPU, memory, and GPU, among other constraints. The cloud placement engine, also referred to as the scheduler, needs to place, in real time, such virtual entities in the cloud. Typically, resource demand is heterogeneous and the mix varies over time. Therefore, the scheduler needs to change its placement policy dynamically in order to accommodate the change in the mixed demand, resulting in lower rejection probability. A novel, autonomic, Adaptive Bin Packing (ABP) algorithm which attempts to equalize measures of variability in the demand and the allocated resources in the cloud, without the need to set any configuration, is introduced. ABP is compared to simplistic, extreme packing policies (spread and pack) as well an optimized packing policy. Experimental results based on simulations are presented, and the behavior of ABP and its adaptability to the demand mix is demonstrated. Further, ABP performs close to the optimized policy, yet evolves to an extreme policy as the mix becomes homogeneous.
VRS-DB: preserve confidentiality of users’ data using encryption approach

We focus on security and privacy problems within a cloud database framework, exploiting the DataBase as a Service (DBaaS). In this framework, an information proprietor drives out its information to a cloud database professional company. The Data-Owner (DO) encrypts the delicate information before transmission at the cloud database professional company end to offer information security. Current encryption ideas, nonetheless, are just halfway homomorphic as all of them intend to enable an explicit kind of calculation, which is accomplished on scrambled information. These current plans can’t be coordinated to solve genuine functional queries that include activities of various types. We propose and evaluate a Verifiable Reliable Secure-DataBase (VRS-DB) framework on shared tables along with many primary operations on scrambled information, which enables information interoperability, and permits an extensive possibility of Structured Query Language (SQL) queries to be prepared by the service provider on the encoded data. We show that our security and privacy idea is protected from two forms of threats and are fundamentally proficient.
Classification of the Era Emotion Reflected on the Image Using Characteristics of Color and Color-Based Classification Method

Paintings convey the composition and characteristics of artists; therefore, it is possible to feel the intended style of painting and emotion of each artist through their paintings. In general, basic elements that constitute traditional paintings are color, texture, and composition (formative elements constituting the paintings are color and shape); however, color is the most crucial element expressing the emotion of a painting. In particular, traditional colors manifest the color containing historicity of the era, so the color shown in painting images is considered a representative color of the culture to which the painting belongs. This study constructed a color emotional system by analyzing colors and rearranged color emotion adjectives based on color combination techniques and clustering algorithm proposed by Kobayashi as well as I.R.I HUE &amp; TONE 120 System. Based on the embodied color emotion system, this study confirmed classified emotions of images by extracting and classifying emotions from traditional Korean painted images, focusing on traditional painted images of the late Joseon Dynasty. Moreover, it was possible to verify the cultural traits of the era through the classified emotion images.
Gaussian processes with linear operator inequality constraints

This paper presents an approach for constrained Gaussian Process (GP) regression where we assume that a set of linear transformations of the process are bounded. It is motivated by machine learning applications for high-consequence engineering systems, where this kind of information is often made available from phenomenological knowledge. We consider a GP $f$ over functions on $\mathcal{X} \subset \mathbb{R}^{n}$ taking values in $\mathbb{R}$, where the process $\mathcal{L}f$ is still Gaussian when $\mathcal{L}$ is a linear operator. Our goal is to model $f$ under the constraint that realizations of $\mathcal{L}f$ are confined to a convex set of functions. In particular, we require that $a \leq \mathcal{L}f \leq b$, given two functions $a$ and $b$ where $a < b$ pointwise. This formulation provides a consistent way of encoding multiple linear constraints, such as shape-constraints based on e.g. boundedness, monotonicity or convexity. We adopt the approach of using a sufficiently dense set of virtual observation locations where the constraint is required to hold, and derive the exact posterior for a conjugate likelihood. The results needed for stable numerical implementation are derived, together with an efficient sampling scheme for estimating the posterior process.
Driver-centred Autonomous Vehicle Motion Control within A Blended Corridor

As a potential cornerstone of the future intelligent transport system, autonomous vehicles (AVs) attract much attention of researchers across a wide range of areas from engineering to computer science. In addition, human factors issues, with respect to transfer of control and the interaction between the AVs and other road users have been studied. Current AV control algorithm development has focused on improving the safety of the vehicle, while the comfort of the drivers are normally ignored. Therefore, motion planning must not only avoid collisions between the vehicle and other road users and the road edges, but also needs to provide a sense of security and comfort for the drivers. Moreover, strict lane following can lead to overly cautious AVs relative to other road users, and thereby lead to traffic accidents. To solve these problems, we estimated the acceptable tolerance of the lateral offset based on the measured driving performance of real drivers and their reaction to a range of risk elements. Together with the vehicle dynamic constraints, the risk-based constraints are incorporated into a nonlinear Model Predictive Control (MPC) controller using a blended corridor. The result is a vehicle trajectory that produces a smooth motion within the corridor that considers the drivers’ comfort.
Vulcont: A recommender system based on context history ontology

The usage of recommenders systems is already widespread. Every day people are exposed to different item offerings based on the prediction of their interests and decisions. Context information, such as location, goals, and close entities, plays a key role in the recommendations' accuracy. The use of context histories allows one to identify similar context histories and predict contexts. This article proposes Vulcont, a recommender system based on a context histories' ontology. Vulcont merges the benefits of ontology reasoning with context histories to measure the context history similarity, based on the semantic and ontology properties provided by the context’s domain. Vulcont considers synonymous and classes' relations to measure similarity. After that, a collaborative filtering approach identifies sequences' frequency to identify potential items for recommendation. The proposed recommendation is evaluated and discussed in four scenarios in an offline experiment, which explores the semantic value of context histories. The main contribution of Vulcont is the use of semantic relations and the properties of ontology in a similarity measurement of context histories, which is a data structure more complete than that of single contexts.
Old metadata in a new world: Standardizing the Getty Provenance Index for linked data

What challenges does metadata managers face when embarking on a Linked Data transformation? How can legacy projects be better understood to ensure such data remodels stay true to their original content? Current efforts made by the Getty Provenance Index elucidate how this task can be much more than simple data re-structuring, and how much manual and specialized labor goes into updating a 35 year old database.
Pre- and post-selection paradoxes in quantum walks

Abstract Many features of single-partite quantum walks can be simulated by classical waves. However, it was recently experimentally shown that some temporal sequences of measurements on a quantum walker do not admit a classical description in terms of macro-realistic theories. Here, we extend this observation and present pre- and post-selection scenarios in quantum walks leading to logical paradoxes. Such paradoxes were recently shown to be equivalent to proofs of contextuality, therefore we provide an additional argument for non-classicality of a quantum walk model. The assumptions behind the claims of non-classicality (including contextuality and violations of macro-realism) are also discussed.
Initial Coin Offerings, Information Disclosure, and Fraud

We study the extent of fraud in initial coin offerings (ICOs), and whether information disclosure prior to the issuance predicts fraud. We document different types of fraud, and that fraudulent ICOs are on average much larger than the sample average. Issuers who disclose their code on GitHub are more likely to be targeted by phishing and hacker activities, which suggests that there are risks related to disclosing the code. Generally, we find it extremely difficult to predict fraud with the information available at the time of issuance. This calls for the need to install a third party that certifies the quality of the issuers, such as specialized platforms, or the engagement of institutional investors and venture capital funds that can perform a due diligence and thus verify the quality of the project.
A Unified Tensor-based Active Appearance Model

Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. For each type of face information, namely shape and texture, we construct a unified tensor model capturing all relevant appearance variations. This contrasts with the variation-specific models of the classical tensor AAM. To achieve the unification across pose variations, a strategy for dealing with self-occluded faces is proposed to obtain consistent shape and texture representations of pose-varied faces. In addition, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these advancements, the utility of UT-AAM in practice is considerably enhanced. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained on a number of well-known face datasets demonstrate the merits of the proposed approach.
Precise Inspection Method of Solar Photovoltaic Panel Using Optical and Thermal Infrared Sensor Image Taken by Drones

Abstract The inspection of the solar panel using the drone has already been put into practical use. However, this method requires an initial investment cost as compared with the conventional method, and it may take a cost such as image processing. In addition, there are challenges such as the ability to carry out the necessary maintenance of the law even if technically possible. Nowadays, the introduction of drones is proceeding in such a way as to check the points of abnormalities through simple flight. The advantage of this method is that safety enhancement and inspection speed are greatly improved compared to the case of performing the inspection by the conventional method. For companies, the use of drones and thermal infrared sensors for inspection can be used to reduce the population and to reduce the problems of recruitment, and to improve the efficiency and reduce the cost by simplifying the inspection procedure with fewer personnel will be. The purpose of this study is to develop a method to accurately detect abnormal points by making thermal infrared images of orthographic images by improving the inspection method through simple flight of drone. The study investigated whether it is possible to precisely check the point where the problem is located under the condition of insufficient solar radiation at the site installed by a small developer. We propose a method that can accurately distinguish between normal and abnormal panel points in images taken using drones and thermal infrared sensors, and how to accurately detect abnormal points by focusing on problem panels. Abnormal points were found to be very accurate and efficient to find and inspect panel more than the temperature range value of each panel.
Adapting Computer Vision Algorithms for Omnidirectional Video

Omnidirectional (360°) video has got quite popular because it provides a highly immersive viewing experience. For computer vision algorithms, it poses several challenges, like the special (equirectangular) projection commonly employed and the huge image size. In this work, we give a high-level overview of these challenges and outline strategies how to adapt computer vision algorithm for the specifics of omnidirectional video.
Validating one-class active learning with user studies – A prototype and open challenges

Active learning with one-class classifiers involves users in the detection of outliers. The evaluation of one-class active learning typically relies on user feedback that is simulated, based on benchmark data. This is because validations with real users are elaborate. They require the de-sign and implementation of an interactive learning system. But without such a validation, it is unclear whether the value proposition of active learning does materialize when it comes to an actual detection of out-liers. User studies are necessary to find out when users can indeed provide feedback. In this article, we describe important characteristics and pre-requisites of one-class active learning for outlier detection, and how they influence the design of interactive systems. We propose a reference architecture of a one-class active learning system. We then describe design alternatives regarding such a system and discuss conceptual and technical challenges. We conclude with a roadmap towards validating one-class active learning with user studies.
Design of airborne target tracking accelerator based on KCF

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real-time requirements of UAV target tracking task.