Reviewerly/exHarmony huggingface dataset:
Sequential Non-Rigid Factorisation for Head Pose Estimation

Within the context of eye-gaze tracking, the capability of permitting the user to move naturally is an important step towards allowing for more natural user interaction in less constrained scenarios. Natural movement can be characterised by changes in head pose, as well as non-rigid face deformations as the user performs different facial expressions. While the estimation of head pose within the domain of eye-gaze tracking is being increasingly considered, the face is most often regarded as a rigid body. The few methods that factor the challenge of handling face deformations into the head pose estimation problem, often require the availability of a pre-defined face model or a considerable amount of training data. In this paper, we direct our attention towards the application of shape-and-motion factorisation for head pose estimation, since this does not generally rely on the availability of an initial face model. Over the years, various shape-and-motion factorisation methods have been proposed to address the challenges of rigid and non-rigid shape and motion recovery, in a batch or sequential manner. However, the real-time recovery of non-rigid shape and motion by factorisation remains, in general, an open problem. Our work addresses this open problem by proposing a sequential factorisation method for non-rigid shape and motion recovery, which does not rely on the availability of a pre-defined face deformation model or training data. Quantitative and qualitative results show that our method can handle various non-rigid face deformations without deterioration of the head pose estimation accuracy.
An improved algorithm for learning sparse parities in the presence of noise

We revisit the Learning Sparse Parities with Noise (LSPN) problem on k out of n variables for k≪n, and present the following findings. For true parity size k=nu for any 0<u<1, and noise rate η<1/2, the first algorithm solves the (n,k,η)-LSPN problem with constant probability and time/sample complexity n(1−u+o(1))k(1/2−η)2. For any 1/2<c1<1, k=o(ηn/log⁡n), and η≤n−c1/4, our second algorithm solves the (n,k,η)-LSPN problem with constant probability and time/sample complexity n2(1−c1+o(1))k. We show a “win-win” result about reducing the number of samples. If there is an algorithm that solves (n,k,η)-LSPN problem with probability Ω(1), time/sample complexity nO(k) for k=o(n1−c), any noise rate η=n1−2c/3 and 1/2≤c<1. Then, either there exists an algorithm that solves the (n,k,μ)-LSPN problem under lower noise rate μ=n−c/3 using only 2n samples, or there exists an algorithm that solves the (n,k′,μ)-LSPN problem for a much larger k′=n1−c with probability n−O(k)/poly(n), and time complexity poly(n)⋅nO(k), using only n samples.
Dynamic SPA-Markov Evaluation Model of Operation Safety for Terminal Area Airspace System

In order to solve the problem of uncertainties in operation safety evaluation for terminal area airspace system, Set Pair Analysis (SPA) and Markov chain are combined to propose a dynamic evaluation model for operation safety in terminal area airspace system. In the model, firstly, on the basis of system analysis theory, the operation safety evaluation index system is established for terminal area airspace system, then based on this the conception and definition about relationship degree in SPA is adopted to describe the safety level and change characteristics of operation safety in terminal area airspace system, finally the safety state transition probability matrix is determined by Markov chain to predict the dynamic change tendency in operation safety for terminal area airspace system. The actual example on Shanghai terminal area shows that, the proposed model can accurately describe the dynamic change characteristics of operation safety for terminal area airspace system. This verifies its effectiveness and feasibility.
Game AI General Theory and its Implementation in AAA Digital Game

A game AI general theory has been researched and developed in game industry in the world, and a new game AI general theory of AI in digital game is supposed with three AIs. For a large scale of game, a game AI system consists of three types of AI such as meta-AI, character AI, and navigation AI. A meta-AI is to control a game dynamically from a bird-view by watching a player’s behavior. A character AI is a brain of a game character such as a buddy, a monster, or a villager to make a decision in real-time. A navigation AI is to recognize an environment of a game to find a path or a best location to move dynamically. Especially, character AI is a main topic to study in game development, and it includes many fields such as multi-layered structure, character animation, agent architecture, decision-making modules, and so on. A new method of decision-making of combination of behavior trees and state machines is supposed. It is called AI Graph. The game AI general theory was applied to an AI system of an action-RPG game “FINAL FANTASY XV”. The results are showed in the paper. All characters’ decision-making system in FINAL FANTASY XV are based on AI Graphs. An AI Graph Editor is a tool to make an AI Graph only by using a mouse and simple text inputs. A dynamics of the new method is showed by explaining AI Graph Editor precisely.
Fast Clustering of Communication Signals

Abstract In the modern and digital battlefield, grasping the electromagnetic power of battlefield system is the key to deciding the outcome of the war, and accurate and comprehensive electromagnetic situational awareness is of great importance. Communication protocol analysis is a prerequisite for smart jamming in non-cooperative electronic countermeasures. The design of efficient jamming strategy for synchronous or networked signals can make up for the shortcoming of insufficient jamming power, solve the problems of remote and covert jamming, greatly reduce the cost of jamming equipment and improve the survivability of battlefield. Therefore, the rapid analysis of communication signal protocol has important military application value. Traditional protocol analysis requires parameter estimation layer by layer on the communication protocol, and bitstream analysis of the protocol word can only be carried out on the premise of solving modulation pattern identification and demodulation, interweave and scrambling parameter estimation and demodulation, channel decoding parameter estimation and decoding. This analysis method has the following disadvantages: long analysis cycle, strong expert dependence and high algorithm complexity. It is difficult to meet the demand of real-time analysis of unknown and flexible electromagnetic signals. In order to solve this problem, this paper proposes an idea of fast clustering of signal protocols in the physical layer by artificial intelligence method.
Key Competencies in Pedagogical Activities

Purpose: to conduct a comparative analysis and correlation of the key (universal) competencies and character qualities of the international version specialist with the Federal State Educational Standard of Higher Education of Russia (using the example of pedagogical directions of training), as well as identify the opinions of students and teachers on the expression of these competencies at home and their significance for pedagogical activities.Design/Methodology/Approach: International and domestic studies of the issue show the use of systemic and comparative approaches to justify the development of key competencies in the practice of different countries. This revealed the set of “4К” and character qualities necessary for the successful implementation of the specialist. The authors complement the study with ideas of a competent approach for higher education.Results: Competency groups have been identified that are most relevant and provide basic training in “4К” recruitment and character qualities. The most popular competence from the set “4К” and similarly - character qualities are determined. Expressed and significant competency groups in students and teachers are shown.Originality/value: The results of the study can form the basis for the development of a personalized learning model, as a response to the challenge of receiving education in the form and scope that each student needs personally, and which will correspond to his understanding of the importance of key competencies.
Knowledge Graph Driven Dialogue Management for Task-oriented Dialogue

Task-oriented dialogue systems have received much attention nowadays. As a core part of the dialogue system, dialogue management often uses a set of predefined slots to represent the dialogue state and decides what to reply to users. However, due to the flat and independent structure between different slots, it is difficult for the system to handle complex dialogue tasks. Besides, the slots are usually coded by human expert for specific task domain, which is hard for domain migration. This paper implements a knowledge graph-driven dialog management method, which introduces the knowledge graph to represent the dialogue state instead of slots. In order to solve the problem of excessive dialogue state space, we investigate a hierarchical entangled state tracking method, which can handle complex conversation and is easy for domain migration. Finally, the effectiveness of the proposed method is verified on two different dialogue tasks of electricity account application and electricity purchasing.
CF-RDF: A Lightweight and Efficient Large Scale RDF Data Management System

As more and more scholars focus on cognitive intelligence, knowledge graph has been widely concerned. Because knowledge graph uses RDF to describe various resources, the storage and management of massive RDF data becomes a challenge. Existing methods are mainly based on relational database, RDF triples and graph model to store and query RDF data [1]. The method based on RDF triples is to build index according to (subject, predict, object) model. It is characterized by building multiple indexes to speed up the query, but it brings a great burden to the database storage. For example, RDF-3X constructs six three-dimensional indexes, six two-dimensional indexes and three one-dimensional indexes, uses a lot of storage space. At the same time, it uses the mapping dictionary to compress the storage space, which will slow down the query speed.To address the above issues, this paper proposes a lightweight and efficient method to store and query RDF data. Instead of mapping RDF data into a relational database or building an index based on RDF triples as most methods do, we develop a new index based on the Cuckoo Filter. In addition, the method of data block storage is adopted, which not only facilitates the storage management, but also makes the storage structure compact, and speeds up the data reading. At the same time, in order to ensure the uniqueness of the data in the index, we use the method of fingerprint value. Through comparative experiments on three large datasets, it is proved that our method improves the query performance by 450% over RDF-3X and 64% over gStore.
Fine-Grained Pose Temporal Memory Module for Video Pose Estimation and Tracking

The task of video pose estimation and tracking has been largely improved with the development of image pose estimation recently. However, there are still many challenging cases, such as body part occlusion, fast body motion, camera zooming, and complex background. Most existing methods generally use the temporal information to get more precise human bounding boxes or just use it in the tracking stage, but they fail to improve the accuracy of pose estimation tasks. To better solve these problems and utilize the temporal information efficiently and effectively, we present a novel structure, called pose temporal memory module, which is flexible to be transferred into top-down pose estimation frameworks. The temporal information stored in the pose temporal memory is aggregated into the current frame feature in our proposed module. We also transfer compositional de-attention (CoDA) to solve the unique keypoint occlusion problem in this task and propose a novel keypoint feature replacement to recover the extreme error detection under fine-grained keypoint-level guidance. To verify the generality and effectiveness of our proposed method, we integrate our module into two widely used pose estimation frameworks and obtain notable improvement on the PoseTrack dataset with only a few extra computing resources.
The Security Encryption System Based on Special Permutation and Combination Algorithms

In the past years, some encryption algorithms were designed and developed through the process of combination and permutation. Most previous algorithms independently encrypted the network components. Hence, an encryption algorithm is put forward in this paper according to a special permutation and combination algorithm. First, the new algorithm uses permutation and combination logic diagram to diffuse the relationship between network speed and network component. Next, the key stream generated by the special permutation and combination can not only modify the value of the diffusion network speed, but can also replace the network speed components spatially at the same time, connecting those three components to each other. Finally, the outcome of the modified and replaced network speed use a random cyclic shift operation for the rearrangement of the bits of every encrypted network speed and the achievement of the great complicatedness and randomness between these key streams generated, the special permutation and combination graph is independently coupled with the nearest neighboring network. The experimental outcomes display that the algorithm can be used in network security and safe communication with high security, sensitivity and speed.
Wigner Function Non-Classicality Induced in a Charge Qubit Interacting with a Dissipative Field Cavity

We explore a superconducting charge qubit interacting with a dissipative microwave cavity field. Wigner distribution and its non-classicality are investigated analytically under the effects of the qubit–cavity interaction, the qubit–cavity detuning, and the dissipation. As the microwave cavity field is initially in an even coherent state, we investigate the non-classicality of the Wigner distributions. Partially and maximally frozen entanglement are produced by the qubit–cavity interaction, depending on detuning and cavity dissipation. It is found that the amplitudes and frequency of the Wigner distribution can be controlled by the phase space parameters, the qubit–cavity interaction and the detuning, as well as by the dissipation. The cavity dissipation reduces the non-classicality; this process can be accelerated by the detuning.
Feature Extraction by Joint Robust Discriminant Analysis and Inter-class Sparsity

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA _FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the l2,1 constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.
Data Protection On Cloud Using GWOA Model

In the current era, we can see that a lot of data lots of data is getting generated and it is getting stored in the cloud. Like earlier systems, people are not using a floppy disk, hard disk, and pen drive for storing the data. Each and every individual and organization are opting for cloud storage because of its flexibility. So, the medical field is also progressed and they are also storing their patient's sensitive data on the cloud. Benefits of storage there come to the security issues for protecting the privacy of the data. In this paper, we have implemented a framework to protect patients' sensitive information on the cloud. We have combined two metaheuristic algorithms, the Glowworm Swarm Optimization algorithm and the Whale Optimization algorithm, and developed GSOA that is a glowworm Swarm whale Optimization algorithm. The experiments are performed on the Cleveland data set.
ViStA: Video Streaming and Analytics Benchmark

With the rapid growth of video streaming across the world, a great need has emerged for the advancement of video analytics and streaming platforms. In this paper, we introduce ViStA, a video streaming and analytics benchmark that can be used to measure the performance of systems running these workloads. We hereby present the design of our benchmark along with select initial studies regarding this benchmark.
A multi-scale language embedding network for proposal-free referring expression comprehension

Referring expression comprehension (REC) is a task that aims to find the location of an object specified by a language expression. Current solutions for REC can be classified into proposal-based methods and proposal-free methods. Proposal-free methods are popular recently because of its flexibility and lightness. Nevertheless, existing proposal-free works give little consideration to visual context. As REC is a context sensitive task, it is hard for current proposal-free methods to comprehend expressions that describe objects by the relative position with surrounding things. In this paper, we propose a multi-scale language embedding network for REC. Our method adopts the proposal-free structure, which directly feeds fused visual-language features into a detection head to predict the bounding box of the target. In the fusion process, we propose a grid fusion module and a grid-context fusion module to compute the similarity between language features and visual features in different size regions. Meanwhile, we extra add fully interacted vision-language information and position information to strength the feature fusion. This novel fusion strategy can help to utilize context flexibly therefore the network can deal with varied expressions, especially expressions that describe objects by things around. Our proposed method outperforms the state-of-the-art methods on Refcoco, Refcoco+ and Refcocog datasets.
Medical Image Retrieval Based on Efficient Approach using Bioorthogonal Transform and Encoding Methods

Image compression is the process of compression an image in such a way that it has less space than the original image. It is a classification of compression technique that reduces the size of an image file without affecting its quality to a greater extent. The biorthogonal transform used for decomposing the Lung images. After decomposition different methods of encoding are performed and finally the proposed compression methods are evaluated for finding optimum algorithm for Medical Image.
Protection of Personal Data Transmitted via Web Service Against Software Developers

Through the widespread use of information technologies, institutions have started to offer most of their services electronically. The best example of this is e-government. Since institutions provide their services to the electronic environment, the quality of the services they provide increases and their access to services becomes easier. Since personal information can be verified with inter-agency information sharing systems, wrong or unfair transactions can be prevented. Since information sharing between institutions is generally done through web services, protection of personal data transmitted via web services is of great importance. There are comprehensive national and international regulations on the protection of personal data. According to these regulations, protection of personal data shared between institutions is a legal obligation; protection of personal data is an issue that needs to be handled comprehensively. This study, protection of personal data shared between institutions through web services against software developers is discussed. With a proposed application, it is aimed to take a new security measure for the protection of personal data. The proposed application consists of a web interface prepared using React and Java programming languages and rest services that provide anonymization of personal data.
Leveraging Sequential Pattern Information for Active Learning from Sequential Data

This paper presents a novel active learning technique aimed at the selection of sequences for manual annotation from a database of unlabelled sequences. Supervised machine learning algorithms can employ these sequences to build better models than those based on using random sequences for training. The main contribution of the proposed method is the use of sequential pattern information contained in the database to select representative and diverse sequences for annotation. These two characteristics ensure the proper coverage of the instance space of sequences and, at the same time, avoids over-fitting the trained model. The approach, called SPIAL (Sequential Pattern Information for Active Learning), uses sequential pattern mining algorithms to extract frequently occurring sub-sequences from the database and evaluates how representative and diverse each sequence is, based on this information. The output is a list of sequences for annotation sorted by representativeness and diversity. The algorithm is modular and, unlike most current techniques, it is independent from the sequential machine learning technique that builds the model as SPIAL does not need to know the set of the features taken into account by the training algorithm. Experiments done on well-known benchmarks involving sequential data show that the models trained using SPIAL increase their convergence speed while reducing manual effort by selecting small sets of very informative sequences for annotation. In addition, the computation cost using SPIAL is much lower than for the state-of-the-art algorithms evaluated.
Research Findings in the Domain of Agile Methodologies

This chapter focuses on examining the impact of distributed agile software development on team performance. In order to get the answer, literature is examined to get necessary information associated with distributed agile software development and team performance along with the models/tools/frameworks, which were used to evaluate the team performance of agile teams. The chapter will then explain how the case study research design was used to get statistics using the data collection method and how the data has been analysed to derive any sort of conclusion and help in answering the research question in more quantified way. The chapter also elaborates with the help of literature analysis the characteristics, existing challenges, success factors, and practices associated with distributed agile software development, team performance, and existing maturity models. This chapter aims to provide insight into the core practices teams can apply when working on digital platforms.