Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Human Intention Estimation System Based on Eye Movement Tracking

Eyes are the window of the mind, and eye movement contains a lot of effective information. With the breakthrough of artificial intelligence and human-computer interaction, eye movement tracking technology has gradually moved to the foreground in recent years. This paper takes medical health as a typical scenario, carries out innovative research around the potential application of eye movement tracking technology in patient communication and human-computer interaction, proposes and implements an intention estimation system based on eye movement tracking. Based on the physiology, medicine and ergonomics of eye movement, the system makes original innovations, extracts a set of eye movement parameters with high recognition accuracy and high expression efficiency, summarizes eye movement instructions that can meet the application of silent environment, and finally forms a series of embryonic applications related to interactive instructions.
Adult English Language Learners’ Reading Habits and Attitudes Regarding Picturebooks as an Educational Resource

The primary purpose of this study is to verify the validity in identifying the characteristics of the production of consonant clusters of Korean L2 learners through an analysis of vowel insertion occurrences. This is of significance as vowel insertion is regarded as the most frequent form of error in the production of English consonant clusters among Korean learners. For this specific purpose, the pronunciation of Korean L2 learner children and adults was analyzed using a set of six English words which contained consonant clusters in their onsets and fourteen English words which contained consonant clusters in their codas. The results of this study suggest the possibility of further investigation to clarify the characteristics with regard to the production of consonant clusters according to the age of Korean EFL learners. In addition, the phonological analysis process presented in this study is expected to assist researchers in this field in the future.
Automatic Medical Lesion Annotation via Feature Fusion Correlation Network

Implicit correlations between lesions in medical images are the key to improve the performance of automatic medical lesion annotation tasks, as additional information they provide can improve the accuracy of the annotation results. Existing methods use single-scale features to model the correlations. However, single-scale features are not conducive to the annotation of small and variable scale lesions. In this paper, we propose a feature fusion correlation network with a feature fusion module and a correlation learning module. The feature fusion module refines the feature maps extracted from different blocks of the backbone and obtains multi-scale features, while the correlation learning module uses these multi-scale features to capture the correlations between different scales of lesions. We conduct experiments on an in-house dataset, and the experiment results show that our method achieves the best performance.
Mobile Based Traffic Offences Report System

Nowadays, interactive systems are using most of the government institutions very actively. Especially, branches such as medical and tax payment are using this systems successfully but we realize that so far only few people interest develop mobile application for security branch and this factor has been the biggest factor in improving our project. This project consist of two modules. In e-Report module, citizens can login with their Turkish Republic identity number and e-Government system password. After login they can make a report for crimes. For crime which can be provable with photograph(ex. wrong parking), they can making report by only taking photo via our application is enough. Date, time and location information are getting automatically from their device and our server. In e-Approve module, only police officers can login with their enrollment number and password. After login they assess notices from coming e-Notice module and approve punishment or reject. This module also have some statistical data(ex. The most sensitive city etc.) and easily reachable when they want.
Proposal of anonymization dictionary using disclosed statements by business operators

Increasing the number of business operators using anonymously processed information is a critical privacy topic in Japan. To promote the use of the information, an “anonymization dictionary” is proposed and implemented. The dictionary is the system that shares use-cases regarding the manner by which business operators produce and provide anonymously processed information. To develop this system, two technical difficulties are resolved: the lack of (i) a method to acquire the use-cases and (ii) a data structure to store the use-cases. In terms of (i), disclosed statements that specify the production and provisioning processes for anonymously processed information is focused. To recognize the statements described in the business operators’ webpages as the use-cases, a web crawler that acquires the statements is developed. The crawler acquires 331 use-cases (statements) in a short duration. In terms of (ii), to define a concrete data structure to store anonymously processed information use-cases, the structure of the use-cases acquired is analyzed. The use-cases are stored into the structure and then in the DB of the dictionary application. This enables a search function to be provided for identifying the necessary use-cases and organizing use-cases in a readable form to the business operators.
Research on Real-Time Feature Extraction Algorithm for Image Processing

In processing images, the detection and matching of feature points occupy an extremely critical position. Considering the actual application scenarios in image recognition and processing problems, this paper designs experiments on the traditional SIFT (Scale-invariant feature transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) three algorithms from the four aspects of algorithm execution speed, lighting conditions, rotation processing, and blurred pixels, and compares the experimental results. We have the following conclusions: the ORB algorithm has a faster execution speed, and the ratio of execution speed of SIFT, SURF, ORB algorithm is about 2:1:14. In terms of feature point matching, the advantage of SIFT algorithm is more prominent, and it can still show a higher matching rate in three complex situations. SURF is the most robust algorithm. Even in the simulation of various complex image transformation scenarios, it also performs better, SIFT comes next, and the ORB algorithm has the worst robustness.
Insight: AI

Abstract What is next for AI regulation? Senior Technologist Adam Leon Smith CITP FBCS explores the implications of the EU AI Act.
A Re-thinking ASR Modeling Framework using Attention Mechanisms

Several reasons have led to the widespread adoption of neural-based algorithms for end-to-end automatic speech recognition (ASR), including their high performance, elegant model designs, and parallel computing capabilities. Numerous ASR models have been proposed to improve the recognition results, but the gains are still insufficient. This paper proposes a re-thinking ASR model, which aims to bridge the gap by rethinking the regularities of a given hypothesis and the relationship between text-level and acoustic-level characteristics of the input speech utterance. For the re-thinking ASR model, a mixed attention mechanism, a self-and-mixed attention mechanism, and a deep acoustic feature extractor are meticulously designed to enable the notion to be realized. A publicly available benchmark corpus is used to evaluate the proposed model. As the experimental results demonstrate, the proposed re-thinking ASR model can provide significant and consistent improvements over popular baseline systems.
INFORMATION TECHNO MIRAGES OF QUANTUM COMPUTERS

The article is devoted to the trends in the formation of the information space of quantum technologies. As an example, the information construction of the results of an experiment to achieve quantum supremacy at Google and discussions about this in the American IT community are considered.
Normal Bipolar Fuzzy Subalgebras of BCK/BCI-Algebras

In this paper, the notions of normal, maximal and completely normal bipolar fuzzy subalgebras of BCK/BCI-algebras are introduced. Some important properties and equivalent characterizations of normal (resp., maximal, completely normal) bipolar fuzzy subalgebras are discussed. We proved that any non-constant normal bipolar fuzzy subalgebra which is a maximal element of $$\left( \mathbf{NBFA}(X), \sqsubseteq \right) $$ only takes a value among (0, 0), $$(1, -1)$$ and (1, 0), and every maximal bipolar fuzzy subalgebra is completely normal.
Opportunistic Multi-robot Environmental Sampling via Decentralized Markov Decision Processes

We study the problem of information sampling with a group of mobile robots from an unknown environment. Each robot is given a unique region in the environment for the sampling task. The objective of the robots is to visit a subset of locations in the environment such that the collected information is maximized, and consequently, the underlying information model matches as close to reality as possible. The robots have limited communication ranges, and therefore can only communicate when nearby one another. The robots operate in a stochastic environment and their control uncertainty is handled using factored Decentralized Markov Decision Processes (Dec-MDP). When two or more robots communicate, they share their past noisy observations and use a Gaussian mixture model to update their local information models. This in turn helps them to obtain a better Dec-MDP policy. Simulation results show that our proposed strategy is able to predict the information model closer to the ground truth version than compared to other algorithms. Furthermore, the reduction in the overall uncertainty is more than comparable algorithms.
A Fully Integrated, High Speed, Motion Controller Hardware Accelerator for Autonomous Systems

Efficient and accurate motion control for any autonomous system (such as, aerial vehicles, ground robots) is a major challenge for robotics researchers and enthusiasts. Fast moving systems demand accurate control of motion for robust operation. To achieve this, high loop rate with fixed minimum jitter is required. As the size of these robots shrinks, they become more agile and are capable of performing very aggressive maneuvers. For such operations, the overall control loop timing requirements become critical. In state-of-the-art approaches, the problem of motion control is solved using a dedicated microcontroller with a specialized software stack. However, in this approach, the control loop suffers from jitter. Also, this approach fails for a multi-axis system (with increased number of axis in the system). Further, this approach increases the hardware component on the platform which adds to the total cost and weight. These challenging requirements demand a dedicated hardware accelerator for efficient operation. To this end, we devise an architecture and data flow for the design of motion controller hardware accelerator for the autonomous system use cases. This accelerator implements the complete control loop in hardware and allows multiple controllers (hence multiple actuators or motors) to run in parallel. Motion controller (MC) hardware accelerator was implemented and integrated on a test System-on-Chip (SoC) and the performance was fully characterized. Further, we have demonstrated the hardware accelerator operation on a physical mobile autonomous robotic system (ground and aerial vehicle). The experimental results demonstrate efficient control operation and our solution achieves ∼44x faster response time and ∼1000x improvement in overall power consumption compared to the software based solution.
Adding Color Information to Spatially-Enhanced, Bag-of-Visual-Words Models

A late-fusing method, based on the HoNC (Histogram of Normalized Colors) descriptor, is provided for combining color with shape in spatially-enhanced-BOVW models to improve predictive accuracy for image classification. The HoNC descriptor is a pure color descriptor that has several useful properties, including the ability to differentiate achromatic colors (e.g., white, grey, black), which are prevalent in real-world images, and to provide illumination intensity invariance. The method is distinguishable from prior late-fusing methods that utilize alternative descriptors, e.g., hue and color names descriptors, that are lacking with respect to one or both of these properties. The method is shown to boost the predictive accuracy by between about 1.9%–3.2% for three different spatially-enhanced BOVW model types, selected for their suitability for real-time use cases, when tested against two datasets (i.e., Caltech101, Caltech256), across a range of vocabulary sizes. The method adds between about 150–190 mS to the model’s total inference time.
Verification Model for Agent Coordination of Distributed Information Systems

Modern distributed information systems, among many applied technologies, rely on multi-agent systems, which is due to the characteristic properties of the latter - autonomy, mobility, intelligence and cooperation. These properties, providing power, flexibility and adaptability in solving many distributed, dynamic, fuzzy problems, are gaining in importance. As in many cases, the features of these properties require a special analysis for them while ensuring the operability and reliability of the functioning of distributed information systems, especially in critical applications. The considered model of verification of the dynamic coordination property - one of the basic properties of agents&#x2019; cooperation - is based on extended Petri nets and automata behavioral experiments of check and recognition. The model for verifying the coordination of agent groups is distinguished by the behavioral check of cause-and-effect coordination-synchronization of conditions, events, actions, functions, both agents and the resource environment of their placement with the individuality and independence of agent goals. The verification model of agent coordination is designed to determine the conditions for the check and recognition of group agent interactions in the methods of testing of distributed information systems with agent properties and allows to reduce the computational complexity of behavioral analysis, check, recognition and diagnosis in general.
Development of Practical Experimental Program Using a Low-cost Motor Control Experiment System

This paper proposes the program of practical training by using developed experimental DC motor control system. In control engineering, it is important to training in practical control design based on control theory. The developed experimental system realizes effective motor control training for beginner, because, that is made low-cost, and easy use for students. The practical experiment program is based on general designing of control system. Students learn the modeling process by step response test, design process of PI speed control system, robustness to disturbance of the control system in the program. The effectiveness of the education has been verified from the examination results of control engineering and the report evaluation of the experiments for the past three years.
3D Pose Estimation of Athlete by Integrating End-Joint and 2D Pose Estimator

In this paper，we present a video motion capture system for sport athletes．Although marker-less video motion capture is suitable for sports motion analysis，the 2D human pose estimation used in the system often fails in estimating postures in sports．This is because those postures are rarely observed in daily life．In the proposed method，we integrate the 2D human pose estimation with an end-joint estimation，which is robust to postures in sports．We validate the proposed method measuring a kick motion of a taekwondo player．
Solving the Cross Domain Problem with Functional Encryption

A Cross Domain Problem (CDP) is the question of how to securely access and exchange information between the domains of varying security levels. A Cross Domain Solution (CDS) addresses the CDP by designing the framework and protocols for such access and transfers. Most existing CDS methods rely on policies and trusted parties to manage different security levels. A CDS that can function in the presence of untrusted parties is a challenge. Functional Encryption (FE) is an encryption scheme in which a secret key allows one to compute a specific function of plaintext from the ciphertext. FE is a generalization of identity-based and attribute-based encryption frameworks. General and simultaneously practical FE is an emerging area, and only special types of encryption schemes and functions are effectively handled within existing systems. We apply the concepts of FE to explore a new solution to the CDP, and we argue that our solution does not leak information, provided that widely accepted assumptions about standard digital signatures hold. We built a practical software case study application using a trusted Key Distribution Center (KDC), a standard symmetric key block cipher component (like the AES), and using the Elliptic Curve Digital Signature Algorithm (ECDSA). The experiments show that the computational overhead introduced to routing by our method is cost effective, where the additional cost is equivalent to just a few applications of standard digital signatures.
Introduction

African governments have long lists of investment ideas that are vital for improved standards of living. These exceed current available financing, so African countries need a greater share of global capital. Borrowing is an important source of self-financed development, although debt risks are elevated. In light of the risks and challenges, urgent action is needed to encourage more responsible lending and borrowing. The potential for African countries to raise living standards over the coming decades is enormous, if the larger debt slips can be avoided. A repeat of the debt relief of the early 2000s would be positive for many of the poorest countries already cut off from capital. But encouraging an exit from global capital markets would be bad advice for most frontier African economies and terrible advice for Africa’s larger emerging markets. Instead of these swings in sentiment, a calmer, more balanced approach is needed.
NOVEL CBIR System Using Spark MAP-Reduce with a Firefly Macqueen’s K-Means Clustering Algorithm

Nowadays content-based image retrieval (CBIR) has been an active research area, demonstrating a feasible solution for recovering similar images from an image mine. The amount of digital images increases exponentially day by. Storage requirements for these images may increase from Gigabytes to Petabytes. Searching and retrieving the relevant images from such a large volume of image datasets, based on their contents, play a dynamic role in various applications of computer vision. The time taken to retrieve the images is more, and the accuracy of retrieved images is less in the existing systems. The limitation of Hadoop map-reduce is the lack of performing real-time tasks efficiently. A proficient content-based image retrieval framework based on Spark Map-Reduce with a Firefly MacQueen’s k-means clustering (FMKC) algorithm and Bag of visual word (BoVW) is proposed to achieve high accuracy for big data. The Apache spark programming can be used to productively recover pictures with less retrieval time and retrieve the accurate images from the big database that resembles the query image. The experimental results demonstrate that the method proposed in our work outperforms the state-of-the-art methods in terms of accuracy of the retrieved images and average retrieval time. The proposed system is 93% accurate, and it is easier to retrieve the images from the large database.
Towards a multi-agent non-player character road network: a Reinforcement Learning approach

Creating detailed and interactive game environments is an area of great importance in the video game industry. This includes creating realistic Non-Player Characters which respond seamlessly to the players actions. Machine learning had great contributions to the area, overcoming scalability and robustness shortcomings of hand-scripted models. We introduce the early results of a reinforcement learning approach in building a simulation environment for heterogeneous, multi-agent non-player characters in a dynamic road network game scene.
Playing Geister by Estimating Hidden Information with Deep Reinforcement Learning

A number of attempts have been made to solve imperfect information games using reinforcement learning. Since it is not easy to apply reinforcement learning directly to imperfect information games, methods, such as Neural Fictitious Self-Play (NFSP), have been proposed. In this study, we investigate an alternative method to solve imperfect information games using reinforcement learning. The basic idea is to learn from self-playing the perfect information games where the hidden information in the original game is revealed. Based on the policy obtained from this learning, the current state of the game is estimated from the opponent's moves and the own best move. We applied the proposed method to an imperfect information game called Geister and evaluated its effectiveness through experiments. As a result, we obtained that the winning rate was higher than that of the direct learning of imperfect information games using reinforcement learning.
Short-term PV Power Prediction Based on Data Mining and Multi-kernel SVM

With the rapid increase of the total installed capacity of photovoltaic (PV) power generation, more accurate PV power prediction is required to ensure the safe and stable operation of the power grid. In order to improve the prediction accuracy of PV power when only irradiance and PV power data can be obtained, while other multi-source data such as temperature, precipitation and other meteorological data, are unavailable, the paper proposes a PV power prediction model based on data mining and the multi-kernel support vector machine (SVM). Firstly, the wavelet threshold denoising method is used to denoise the data of irradiance and PV power which contains many burrs and the large signal fluctuation. Then, the parameters are extracted by irradiance and power characteristic representation, which include six irradiance characteristic parameters and two power characteristic parameters. With the characteristic parameters, the similar days are selected by the data mining technology, a clustering algorithm using SOM and K-Means. Finally, the multi-kernel SVM is used for PV power prediction, where the multi-kernel function is used to deal with the distribution characteristics of data and improve the accuracy of PV power prediction. The experimental results show that the prediction accuracy can be improved by the wavelet threshold denoising and multi-kernel SVM. The high precision PV prediction results can also be obtained with the irradiance and PV power data only, and the PV prediction accuracy of multi-kernel SVM is higher than that of the single-kernel SVM and classical back propagation (BP) neural network.