Reviewerly/exHarmony huggingface dataset:
Hybrid Inspector-Inspectee-Agent Games in Mobile Cloud Computing

Within the paradigm of Mobile Cloud Computing (MCC) mobile devices such as mobile phones and tablets can unload computation to a local Cloud consisting of both static and mobile devices. Due to the proximity, such Cloud can better fulfill service and latency requirements for QoS sensitive applications. However, in order to function properly, such mechanics requires a collaborative approach that not all the devices might follow: some can contribute much less than others and deviate from the Service Level Agreement. This creates a free-riding problem in MCC and a corresponding QoS issue. An approach towards this problem consists of providing incentives to nodes so that they act as inspectors and occasionally audit the recent behavior of the nodes with which they interact. This double role of potential inspectee and inspectors can be modeled within Game Theory (GT) to predict the behavior of the agents. In this work, we show that an appropriate model for this game is a symmetric four-strategies social dilemma. The solution (a.k.a. equilibrium) of the game provides a unique way to set the incentives so as to drive the system toward the desired behavior. However, even the hybrid inspector-inspectee agent approach is in principle open to a potential flaw: the possibility of inspector-inspectee collusion. The main contribution of this work is that the collusion behavior is not an equilibrium of the game, thus hybrid agent rational players should never collude.
Utilizing Minimum Set-Cover Structures with Several Constraints for Knowledge Discovery on Large Literature Databases

A lot of problems in natural language processing and knowledge discovery can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based approach. This problem can be described as a Minimum Set Cover Problem with several constraints. The goal is to find a minimum covering of documents with the given context for a fixed weight function. The aim of this problem reformulation is a deeper understanding of both the hierarchical problem using union and cut as well as the non-hierarchical problem using the union. We thus choose a modeling using bipartite graphs and suggest a novel reformulation using an integer linear program as well as novel graph-theoretic approaches.
Intrinsic and Extrinsic Evaluation of Word Embedding Models

In this study, we aimed to understand and analyze how word embedding models work on both Turkish and English. We focused on the word2vec word embedding model. We tried to improve the quality of word representations by changing the orientation of context windows. By changing the context window orientation, we aimed to train models with better accuracy results without increasing the training time. The impact of different window sizes and vector dimensions on the quality of word representations was analyzed both intrinsically and extrinsically.
Appearance Controlled Face Texture Generation for Video Game Characters

Manually creating realistic, digital human heads is a difficult and time-consuming task for artists. While 3D scanners and photogrammetry allow for quick and automatic reconstruction of heads, finding an actor who fits specific character appearance descriptions can be difficult. Moreover, modern open-world videogames feature several thousands of characters that cannot realistically all be cast and scanned. Therefore, researchers are investigating generative models to create heads fitting a specific character appearance description. While current methods are able to generate believable head shapes quite well, generating a corresponding high-resolution and high-quality texture which respects the character’s appearance description is not possible using current state of the art methods.
Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)

Abstract Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done to reduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%.
A Semantic-based Multi-agent Dynamic Interaction Model

Due to the autonomy of agents and their ability to perceive the environment, multi-agent systems have been widely used in many fields. The design of multi-agent systems requires the support of interactive models. The traditional multi-agent interaction model has certain feasibility in solving specific tasks. However, in a distributed environment, a relatively static multi-agent interaction model is not sufficient to support a dynamically changing interaction process. Frequent data interactions will also consume resources of multi-agent systems, thereby reducing agent performance. In this study, we propose a semantic-based multiagent dynamic interaction model (MADIM). MADIM uses semantic ontology to map the objects in the interaction model, and defines the interaction protocol through the rule description language. This model is attached with dynamically configurable semantic templates and interaction rule base. We added a reusable dynamic resolution engine component to MADIM to provide dynamic resolution services for the semantic information in the model. MADIM supports dynamic interactive behavior and has good interoperability and interpretability. Our model provides a flexible solution to the multi-agent interaction process. Finally, we verified the feasibility of the model design scheme through a simple example.
Interactive Training of Drone Flight Control in Mixed Reality

This paper presents a mixed reality system for remote interactive training of first-person view drone flight control. A remote trainer conducts training scenario design and management in a virtual environment. A drone player practices on a first-person view drone flight control in a mixed reality environment according to the training scenario designed by the trainer. Both users can interact and communicate with each other while sharing the same virtual environment, even if they are present in separate locations.
Downhole robot path planning based on improved D* algorithmn

In order to address the possible problems of the traditional D* algorithm in the path planning process, such as the proximity to obstacles, this paper improves both the distance function and the selection of child nodes. Firstly, the traditional D* algorithm using Euclid distances was improved to Manhattan distances that are more suitable for the special environment in the mine, thus reducing the cost of computing time. Then, improve the selection of child nodes, increase the danger factor as the node attribute, and exclude the node whose danger factor is 1, thereby increasing the safety distance of the planned route and reducing the number of search points in the path planning process. The experimental results show that for the downhole roadway environment simulation map, the calculation efficiency of the method is significantly improved and the planned path is safer and reasonable.
Performance Analysis of Implemented MFCC and HMM-based Speech Recognition System

This paper describes the performance analysis of designed speech recognition system whereby the front end method uses MFCCs feature extraction algorithm and defined HMM recognition as the back end. The dataset includes 30 phonemes and 2200 utterances by different speakers. Each speech signal is sampled to 16kHz, 16-bit PCM, and in a mono channel format. The extracted feature of each signal consists of 39 feature vectors which are 12 Mel Cepstrum Coefficients, Log Energy, Delta (first-order derivative) coefficients, and Acceleration coefficients (second-order derivative). The Baum-Welch algorithm is applied for HMM training and the Viterbi algorithms for decoding. The overall system performance accuracy of this experiment is 95.00%.
Web Service Composition Optimization Based on Adaptive Mutant Beetle Swarm

Abstract with the continuous development of cloud computing technology and big data, web services are widely used in intelligent information processing in all walks of life, when facing a large number of services with similar functions and quality, web service composition technology is a crucial technology in web services, which enable common services to be combined and select the optimal service combination to meet the wide range of users’ needs. Aiming at the problems of algorithm accuracy and stability of existing service composition technologies, the penalty function is used to construct the fitness function in this paper, which transforms the constrained optimization problem into an unconstrained optimization problem, and realizes the global optimization. Traditional web service composition models are aimed at QoS attribute perception. This paper establishes a QoE model that focuses on user experience, which can better meet the needs of users. An adaptive mutant beetle herd algorithm was proposed in this paper, the author introduced Beetle Antennae Searching Algorithm into the common particle swarm optimization algorithm, and added an adaptive mutation factor to enhance the searching ability of the algorithm; The use of dynamic learning factors allows the algorithm to have better population diversity and enhanced convergence. The experimental results that the algorithm proposed in this paper has higher algorithm accuracy, comparatively faster convergence speed and stability.
Investigating Users’ Natural Engagement with a 3D Design Approach in an Egocentric Vision Scenario

User interfaces based on modalities such as touch, gestures or voice are often referred to as Natural User Interfaces (NUI). Particularly, gesture interactions have become an adequate form of interaction on touch devices, due well established conventions for gestures in real life. In virtual and augmented reality, gestures are also a very natural way to interact with digital objects, but there is a lack of clear guidelines for how to design gesture-based interaction in AR and VR. This work presents an approach based on user centered design for the usage of bare hands to control an immersive system using commercial low-end HMD's from an egocentric perspective and built from a proposed case study methodology. Our design approach generated an interaction vocabulary, considering users' preferences, seeking a set of natural and comfortable hand poses and evaluated users' satisfaction and performance.
Test and Evaluation for Hybrid System of UAV and Mobile Robot

Research and development for UAV(Unmanned Aerial Vehicle) and mobile robot are continuously running in various companies, and these UAVs and mobile robots are able to use for military and everyday life. In this paper, we propose how to test the performance of hybrid UAV and mobile robot which are combined system of UAV and mobile, and its evaluation is also proposed.
GPS Positioning Method of UAV Based on Improved Particle Filter

With the development of artificial intelligence, particle filter algorithm has become a research hotspot of Chinese and foreign scholars. Since the particle filter algorithm has better performance in the non-linear Gaussian system, the particle filter is applied in the UAV positioning system. The Monte Carlo sampling method is used for the posterior distribution. In view of the particle degradation problem existing in the particle filter algorithm, the re-sampling of the particle filter method is improved. In order to verify the performance of the improved algorithm, experiments were carried out on a quadrotor UAV platform based on STM32. The results show that the improved positioning algorithm can effectively improve the positioning accuracy of the UAV, and has good practicability.
Explaining and Interpreting Machine Learning CAD Decisions: An IC Testing Case Study

We provide a methodology to explain and interpret machine learning decisions in Computer-Aided Design (CAD) flows. We demonstrate the efficacy of the methodology to the VLSI testing case. Such a tool will provide designers with insight into the "black box" machine learning models/classifiers through human readable sentences based on normally understood design rules or new design rules. The methodology builds on an intrinsically explainable, rule-based ML framework, called Sentences in Feature Subsets (SiFS), to mine human readable decision rules from empirical data sets. SiFS derives decision rules as compact Boolean logic sentences involving subsets of features in the input data. The approach is applied to test point insertion problem in circuits and compared to the ground truth and traditional design rules.
Occupational Orientation of Students in Terms of Distance Learning

This article analyses the system of occupational orientation of modern students, its impact on the country's economy and the labor market. The main problems of pre-university training arising during the transition of educational organizations to distance learning are revealed. The main ideas of using digital technologies in career guidance activities are justified.
Causality in Probabilistic Fuzzy Logic and Alternative Causes as Fuzzy Duals

Causal inference is an essential part of human reasoning. Computer scientists often use inferential logic and probability theories to find the causes of the events. In causal inference, researchers are interested in finding the relationship between two observable events. However, most of the time, we can only perceive the co-occurrences of events or usual successions of events. So, we must be careful in trying to establish causality between events. Also, many alternative causes or conjoint causes can be at work. So, we need good tools to deal with the complex problem of causality. In this paper, we will explore probabilistic fuzzy logic (PFL) as a sophisticated alternative to establish causal inference and fuzzy duals as tools for the expression of alternative possible causes.
Battery Management for Automated Warehouses via Deep Reinforcement Learning

Automated warehouses are widely deployed in large-scale distribution centers due to their ability of reducing operational cost and improving throughput capacity. In an automated warehouse, orders are fulfilled by battery-powered AGVs transporting movable shelves or boxes. Therefore, battery management is crucial to the productivity since recovering depleted batteries can be time-consuming and seriously affect the overall performance of the system by reducing the number of available robots. In this paper, we propose to solve the battery management problem by using deep reinforcement learning (DRL). We first formulate the battery management problem as a Markov Decision Process (MDP). Then we show the state-of-the-art DRL method which uses Gaussian noise to enforce exploration could perform poorly in the formulated MDP, and present a novel algorithm called TD3-ARL that performs effective exploration by regulating the magnitude of the outputted action. Finally, extensive empirical evaluations confirm the superiority of our algorithm over the state-of-the-art and the rule-based policies.
Towards Transparent Control-Flow Integrity in Safety-Critical Systems

Protecting safety-critical Cyber-Physical Systems (CPS) against security threats is becoming a growing necessity. Due to the high level of network integration, CPS pose new targets to remote code-reuse attacks, such as Return-Oriented Programming (ROP). An effective mechanism to detect code-reuse attacks is Control-Flow Integrity (CFI). However, because of the intrusiveness of most current CFI solutions, i.e., their requirement for program instrumentation and run-time interference, we cannot directly apply them to safety-critical CPS. To the best of our knowledge, there is no CFI solution designed for CPS; and more specifically, we are not aware of any solution that fully monitors the forward-edges and backward-edges of an application’s control-flow, while providing independence and freedom from interference guarantees. Hence, for the first time, we propose a safety certifiable, separation kernel-based partitioning architecture to integrate CFI monitoring in a safety-critical system to protect applications with real-time constraints. Our solution leverages ARM CoreSight to transparently enforce both forward-edge and backward-edge CFI for an application at run-time. Despite imposing a significant overhead on the overall system, our approach reliably protects the control-flow of the monitored application, while guaranteeing its real-time constraints. We evaluate our solution by analyzing its timing impact and discussing the resulting considerations for the integration and practical deployment in a safety-critical CPS.
TrustICT

The Trusted Execution Environment (TEE) has been widely used to protect the security-sensitive sensing systems on Internet-of-Thing (IoT) devices. In the TEE systems, the execution environment is securely divided into a normal domain and a higher privileged secure domain which executing sensing systems through hardware. One common way to achieve the protection is implementing the sensitive functions of the sensing systems as trusted applications (TAs) in the well-isolated secure domain. Users in rich OS have to call TAs through the client applications (CAs), and the invocations must pass through the rich OS kernel. However, an untrusted rich OS may launch man-in-the-middle attacks on the communication between the CAs and TAs, and the misuse of cross-domain communication channel is becoming one severe threat on the TEE systems. In this paper, we develop a defense system named TrustICT to construct a lightweight trusted interaction channel between CAs and TAs without modifying existing TEE architecture. The main idea is to block attacks on the cross-domain interactions via dynamically setting the access permission of domain-shared memory, locking it from kernel mode and unlocking it only to legal CAs in the user mode. Particularly, we propose a multi-core scheduling strategy to defeat potential attacks from all privileged cores. Compared to existing cryptography-based methods, TrustICT dramatically reduces the system overhead since it does not require time-consuming cryptographic computation or sophisticated real-time kernel protection. We implement a prototype of TrustICT on a Freescale i.MX6Quad platform with the OP-TEE software system and evaluate its impacts on rich OS and the cross-domain transactions.
The Recognition and Information Extraction of Grid Wiring Diagram Based on Convolutional Neural Network

With the rapid development of digital image processing and recognition, the intelligent grid wins a great opportunity. We could be able to get the information from digital images quickly based on computer analysis, algorithms of deep learning and machine learning, grid data extraction, model training and image recognition. The article firstly reviewed the current status of traditional grid, including a broad overview of the specific strengths and weaknesses, secondly analyzed the intelligence algorithms of deep learning and machine learning based on neural network, then achieved the recognition and reconstruction of wiring diagram through image recognition based on neural network, lastly showed the prospects of image recognition in power system.
Augmented Reality Applied to the Tourism in Churches of the Historic Center of Quito

Augmented Reality (AR) is a technology that combines the real world with a virtual object in 2 or 3 dimensions. It is related to a real environment that contains virtual objects of different forms, including images, videos and audios. Such technology is being currently utilized in the tourism industry with the purpose of promoting and improving the tourist experience. The application developed enables the user to interact and manipulate objects using a mobile device to know about the place that the user wishes to visit, the location, the features and a photo gallery of each church, for which Vuforia, Unity and Visual Studio were utilized. Results show that the application offers a new tourist experience and makes the visits and tours more enjoyable, providing attractive, informative and interactive means to the tourist.
High-Performance Password Recovery Hardware Going From GPU to Hybrid CPU-FPGA Platform

Password recovery has a high demand for computing power. This article aims to provide an overview of password recovery systems, including the basic concepts of password recovery, the popular hardware platforms ranging from the traditional application-specific integrated circuits and field-programmable gate arrays to modern graphic process units and hybrid CPU-FPGA chips, the password guessing methods, and the acceleration techniques applicable to crack various password encryption algorithms.
Single- and two-photon interference within the simulated photon wave function in coordinate representation

Abstract The main provisions of the construction of the single-particle wave function of a photon in the coordinate representation are expounded and its application to the description of single-photon interference phenomena is discussed. The connection between the concepts of electromagnetic field strengths used in quantum electrodynamics and the characteristics of wave packets in photon quantum mechanics is substantiated. For illustration, we consider the relationship between the structure of the photon wave function in coordinate representation and the polarization of photon in the formalism of quantum transition amplitudes. The meaning of the concept of linear polarization of a photon in the state of a wave packet is explained. The wave function is constructed in the coordinate representation of the state of two entangled photons at the exit from a nonlinear crystal by modeling the polarization vectors and Gaussian momentum distribution for each of the photons.
CROSS-AGE FACE VERIFICATION USING FACE AGING

Faces are inherently associated with a person's identity. By extracting the relevant features from facial images, we can differentiate between people. Most of these features are age-invariant and can be used to determine the identity of the person even after a long span of time. In this paper, we propose a face verification model that can unambiguously verify a person's identity across the age-progression. For the task of face verification, we firstly use a Generative Adversarial Network (GAN), which preserves the distinguishing facial features and ages the person's image. We then compare the aged image with a ground truth old image to verify the identity. We apply three metrics namely, SSIM, AbsDiff, and Face++ Online API to achieve this task. Qualitative results on CACD and FG-NET databases show that aging the image improves the accuracy of face verification. Quantitative experiments on both the databases demonstrate the high applicability of our model to existing biometric systems. We hope that the proposed work can be used to tackle impersonation frauds and for finding lost children.