Reviewerly/exHarmony huggingface dataset:
An Efficient Multiple Description Coding for Multi-View Video Based on the Correlation of Spatial Polyphase Transformed Subsequences

For a robust three-dimensional video transmission through error prone channels, an efficient multiple description coding for multi-view video based on the correlation of spatial polyphase transformed subsequences (CSPT_MDC_MVC) is proposed in this article. The input multi-view video sequence is first separated into four subsequences by spatial polyphase transform and then grouped into two descriptions. With the correlation of macroblocks in corresponding subsequence positions, these subsequences should not be coded in completely the same way. In each description, one subsequence is directly coded by the Joint Multi-view Video Coding (JMVC) encoder and the other subsequence is classified into four sets. According to the classification, the indirectly coding subsequence selectively employed the prediction mode and the prediction vector of the counter directly coding subsequence, which reduces the bitrate consumption and the coding complexity of multiple description coding for multi-view video. On the decoder side, the gradient-based directional interpolation is employed to improve the side reconstructed quality. The effectiveness and robustness of the proposed algorithm is verified by experiments in the JMVC coding platform.
Author Correction: A dissipatively stabilized Mott insulator of photons

Change history: In this Article, two additional references (now added as refs 12 and 14) should have been cited at the end of the sentence “Recently, photonic systems have emerged as a platform of interest for the exploration of synthetic quantum matter.”. This has been corrected online.
Ontology Population Framework of MAGNETO for Instantiating Heterogeneous Forensic Data Modalities

The growth in digital technologies has influenced three characteristics of information namely the volume, the modality and the frequency. As the amount of information generated by individuals increases, there is a critical need for the Law Enforcement Agencies to exploit all available resources to effectively carry out criminal investigation. Addressing the increasing challenges in handling the large amount of diversified media modalities generated at high-frequency, the paper outlines a systematic approach adopted for the processing and extraction of semantic concepts formalized to assist criminal investigations. The novelty of the proposed framework relies on the semantic processing of heterogeneous data sources including audio-visual footage, speech-to-text, text mining, suspect tracking and identification using distinctive region or pattern. Information extraction from textual data, machine-translated into English from various European languages, uses semantic role labeling. All extracted information is stored in one unifying system based on an ontology developed specifically for this task. The described technologies will be implemented in the Multimedia Analysis and correlation enGine for orgaNised crime prEvention and invesTigatiOn (MAGNETO).
Signature identification by Minkowski dimension

In this article, we propose and investigate the possibility of signature identification based on its fractal Minkowski dimension. We consider a signature as a trajectory of a pen tip that obeys the Langevin equations, for which we calculate the fractal Minkowski dimension. This parameter is different for original and intentionally falsified signatures, thus allowing one to reliably distinguish between the signatures left by different persons. The proposed approach together with machine learning techniques is a potentially powerful tool for identification and verification of signatures and any other kind of notations.
Deep Residual Output Layers for Neural Language Generation

Many tasks, including language generation, benefit from learning the structure of the output space, particularly when the space of output labels is large and the data is sparse. State-of-the-art neural language models indirectly capture the output space structure in their classifier weights since they lack parameter sharing across output labels. Learning shared output label mappings helps, but existing methods have limited expressivity and are prone to overfitting. In this paper, we investigate the usefulness of more powerful shared mappings for output labels, and propose a deep residual output mapping with dropout between layers to better capture the structure of the output space and avoid overfitting. Evaluations on three language generation tasks show that our output label mapping can match or improve state-of-the-art recurrent and self-attention architectures, and suggest that the classifier does not necessarily need to be high-rank to better model natural language if it is better at capturing the structure of the output space.
Python for Natural Language Processing

In the previous chapter, we took a journey into the world of natural language processing and explored several interesting concepts and domains associated with it. We now have a better understanding of the entire scope surrounding natural language processing, linguistics, and text analytics. If you refresh your memory, we also got our first taste of running Python code to look at essentials with regard to processing and understanding text. We also looked at ways to access and use text corpora resources with the help of the NLTK framework. In this chapter, we look at why Python is the language of choice for natural language processing (NLP), set up a robust Python environment, take a hands-on based approach to understanding essentials of string and text processing, manipulation, and transformation, and conclude by looking at some of the important libraries and frameworks associated with NLP and text analytics. This chapter is aimed to provide a quick refresher for getting started with Python and NLP.
Fast Intra Prediction Algorithm for Virtual Reality 360 Degree Video Based on Improved RMD

Virtual Reality 360 degree video is spherical video. It allows the viewer to freely choose the field of view from any angle, and the video resolution must be high enough for every field of view. During coding process, spherical video is projected into 2D plane video and it has ultra-high resolution and larger data compared to traditional video. Considering reducing the high spatial redundancy of virtual reality 360 degree video, and reducing the computational complexity of intra coding, while ensuring the integrity of the details of each field of view, a fast intra prediction algorithm for virtual reality 360 degree video based on improved Rough Mode Decision (RMD) is proposed. The experimental results show that the proposed algorithm brings 38.73% time saving with only 1.4% Bjontegaard delta rate increase in All-Intra mode.
Three-Dimensional Segmentation of Hippocampus in Brain MRI Images Based on 3CN-net

The hippocampus is very small, with complex in shape and structure, and its boundary with surrounding tissues is blurred in brain MRI images, so the traditional segmentation method cannot achieve ideal segmentation effect. In this paper, an improved deep convolution network model for biomedical imaging (3CN-net) is proposed, which can make full use of the 3d spatial information of MRI image itself, make the feature expression accurate, and reduce the neuron parameter and calculation amount; An 3d hippocampus segmentation algorithm is established, which can make full use of different levels of semantic information, improve the automatic and accurate extraction of image features, and achieve high-precision segmentation of the hippocampus in MRI images; Finally, the algorithm is compared with the experimental results of several existing optimal segmentation methods.
Data-Driven Analysis of Price Change, User Behavior and Wealth Accumulation in Bitcoin Transactions

Bitcoin scheme powered by the blockchain technology is a global payment system that posed great challenges and opportunities for economists, entrepreneurs and consumers. As Bitcoin is still considered to be mysterious and not well understood by many stakeholders, it is essential to analyze the capabilities of Bitcoin and its underlying protocol with regard to different financial aspects. Some of these aspects include Bitcoin ownership, Bitcoin saving trends, transaction authenticity, price stability, and energy consumption. Through this paper we present a data driven analysis of price fluctuations, user behavior, and wealth accumulation in Bitcoin transaction. Of particular interest to stakeholders is the understanding of factors that impact Bitcoin price and the volatility over time. We focus on comparison of Bitcoin price trends vs number of users and the number of transactions. We also analyze the richest addresses and the percentage of wealth in these addresses. Important conclusions are finally summarized.
The Effects of Classroom Scenario and Task-display Method on User Experience in Teaching Simulation

이 연구는 수업시뮬레이션에서 시나리오와 수행 안내 방법을 제시하는 인터페이스에 따라 예비교사가 지각하는 가상실재감과 사용성에 미치는 효과를 분석하려는 것이다. 이 연구에서 사용된 수업시 뮬레이션은 가상교실에서 수업 중 발생하는 문제상황을 관찰하고, 상담을 통해 문제를 해결하도록 구성되었다. 사범대학생 34명이 연구에 참여했으며, 세 가지 수업시나리오를 반복적으로 경험하게 하고 가상실재감과 사용성 지각을 측정하였다. 연구결과는 다음과 같다. 첫째, 가상실재감 하위요인인 사실성 요인에서 수업시뮬레이션의 수행 안내 방법과 시나리오의 상호작용 효과가 나타났다. 부분안내 집단은 수업시뮬레이션의 반복에 따라 사실성을 높게 지각했지만, 전체안내 집단은 가장 처음에 접한 수업시뮬레이션을 가장 사실적으로 인식하였다. 둘째, 수업시뮬레이션의 수행 안내 방법에 따라 예비교사의 사용성 지각 하위요인인 사용의도에서 유의미한 차이가 있었다. 전체안내 집단의 사용의도가 부분안내 집단의 사용의도보다 높게 나타났다. 이 연구를 통해 수업시뮬레이션을 설계할 때 전체적인 수행방법을 한꺼번에 안내하는 방법이 사용의도를 높인다는 점을 확인했다.The purpose of this study was to examine the effective design of the virtual reality-based teaching simulation for pre-service teachers. Mainly this research focused on designing a teaching simulation to train pre-service teachers to handle problem situation during a collaborative learning activity in the class setting. The teaching simulation of this study consists of two sessions: 1) observation of problems in a group learning and 2) one-to-one counseling with a student causing the problem. The teaching simulation of this study used a simulation platform, SimTEACHER that stands for the Simulation for Teaching Enhancement of Authentic Classroom beHavior EmulatoR. It was developed to apply for the virtual reality technology with a head-mounted display known as HMD. In this study, the four-stage model of the communication process was implemented. Two task display methods, a part-task vs. a whole-task method, were applied as an independent variable of this study. Thirty-four pre-service students participated in this study. Three scenarios were used to the teaching simulation, SimTEACHER. The user s perception and virtual presences were measured to examine the effects of display methods. This study resulted that the whole-task showed a significantly higher score of the intention to use the simulation than the part-task did. There was a significant interaction effect between method and scenarios on the realness in the virtual presence. When a participant was using a part-task method, the perception of realness increases through the simulation. However, in the whole-task condition, there was a significant difference only when he or she uses the first scenario. The result indicated that the part-task method helped to keep a user increase the realness of virtual presence. However, the whole-task was not helpful as much as the part-task did. The study showed that it is essential to provide various and authentic classroom scenario to facilitate the use of teaching simulation.
Video segmentation scheme based on AMC

Video segmentation has become a fundamental of various multimedia applications. Spatiotemporal coherence is important for video segmentation. In this study, to balance the spatiotemporal coherence in scenes with deformation or large motion, the authors propose a novel segmentation scheme based on the absorbing Markov chain (AMC) model named directed graph segmentation based on AMC. In their study, they first generate primary proposals per frame. Then, they train weight models by using a part of primary proposals with their features and feature scores. Next, they construct a directed AMC graph, in which states are the generated primary proposals and edge weights are decided by trained weight models. They subsequently perform the first proposal selection per frame by thresholding the modified absorbed time. Afterwards, they design a reselection algorithm to filter the selected proposals and ensure the proposals, which are the most likely to be the motion object in each frame, to be selected as candidates. Finally, they employ the graph-cuts based optimisation algorithm to generate refined per pixel segmentation by using object and background models built by candidate proposals under the concept of Gaussian mixture models. Experimental results demonstrate that the proposed scheme shows competitive performance compared with advanced algorithms.
Enhancing One-Dimensional Chaotic Map Based on Bitstream Dividing Model

The chaotic map has attracted more researchers in chaotic cryptography domain. That is because to large similarity among their characteristics such as unpredictability and sensitivity to an initial condition. Moreover, chaotic maps are a source of entropy that provide diffusion and confusion strength in cryptographic applications. Although existing one dimensional chaotic maps are widely used, many security issues are found in their behaviors. This paper tackles those issues through the new proposed solution model that uses bitstream dividing model. The proposed model uses the fixed-point format to extract bitstream and dividing a chaotic state into N equal parts, each part is a new chaotic state. In order to show the effectiveness of the proposed model, the unimodal maps are used in the simulation, it is shown that the proposed model has better chaotic complexity and performance than the corresponding maps. Moreover, it is a simple structure but effective and can be used as a chaotification model to generate digital chaos. In chaos-based cryptography, the enhanced maps have a better security strength which is highly needed for the development of chaos-based ciphers.
Real Flight Application of a Monocular Image-Based Aircraft Collision Decision Method

This paper considers real flight application of a monocular image-based aircraft collision decision algorithm developed in a previous paper. First, it briefly summarizes the theory based on the previous paper and extends the results with a detailed evaluation of possible special cases. Second, it introduces the UAVs and flight test scenarios together with the camera system and the steps of image processing used in flight testing. A brief analysis about intruder detectability is also provided referencing a more exhaustive work of the authors. The main contribution is the detailed comparison of the image-based estimated collision parameters to the flight trajectory-based ones together with threshold selection for collision decision. Different threshold combinations are evaluated offline, and finally, real flight decision results based on one of the threshold combinations are also discussed. The paper ends with the setup of future research directions.
A new comprehensive solution to handle information security Governance in organizations

In the last decade, several standards, best practices, and frameworks have been created to help organizations govern the information security in modern organizations in order to optimize processes to achieve business goals. With this in mind, organizations use a variety of mechanisms to ensure that their security architecture is aligned with the organization's business objectives and comply with local standards, rules and regulations in force. Despite the large number of options available, there is considerable confusion over the various methods used by IT managers, and between the various terminologies that suddenly emerged (all at once) in the business world, including information security management (ISM), IT governance (IT-GOV) and information system security governance (ISS-GOV) due to their lack of information compressive Governance approach. This paper, first clarified the main confusions of the different terminologies of this field, and secondly proposes a new in house reference combine the various processes of ITIL V3, ISO 38500 and the security controls of ISO / IEC 27001 which can be used effectively as a complete solution by any organization to govern information security and information technology.
A Survey of Blockchain Technology Security

Bitcoin is a popular cryptocurrency that records all transactions in an allotted append-handiest public ledger referred to as a blockchain.The security of Bitcoin heavily relies on the motivation-suitable proof-of-work (PoW) founded dispensed consensus protocol, which is run with the aid of the community nodes known as miners.Because of its inception, blockchain technological know-how has proven promising application possibilities.The spectrum of blockchain functions stages from financial, healthcare, automobile, hazard administration, internet of matters (IoT) to public and social offerings.Several reports focal point on utilizing the blockchain information structure in various applications.These vulnerabilities result in the execution of different security threats to the ordinary functionality of Bitcoin.We then examine the feasibility and robustness of the brand new safety solutions.Moreover, we discuss the current anonymity concerns in Bitcoin and the privatenessrelated threats to Bitcoin customers together with the evaluation of the comprehensive privacy-keeping solutions.
Parameterized Text Indexing with One Wildcard

Two equal-length strings X and Y over an alphabet Σ of size σ are a parameterized match iff X can be transformed to Y by renaming the character X[i] to the character Y[i] for 1 ≤ i ≤ |X| using a one-to-one function from the set of characters in X to the set of characters in Y. The parameterized text indexing problem is defined as: Index a text T of n characters over an alphabet set Σ of size σ, such that whenever a pattern P[1, p] comes as a query, we can report all occ parameterized occurrences of P in T. A position i ∊ [1, n] is a parameterized occurrence of P in T, iff P and T[i,(i+p-1)] are a parameterized match. We study an interesting generalization of this problem, where the pattern contains one wildcard character ϕ ∉ Σ that matches with any other character in Σ. Therefore, for a pattern P[1, p] = P_1ϕP_2, our task is to report all positions i in T, such that the string P_1 P_2 and the string obtained by concatenating T[i,(i+|P_1|-1)] and T[(i+|P_1|+1),(i+p-1)] are a parameterized match. We show that such queries can be answered in optimal O(p+occ) time per query using an O(n log n) space index. We then show how to compress our index into O(n log σ) space but with a higher query cost of O(p(log log n+logσ)+occ logσ).
Human Action Recognition Based on Fusion Features

Human action recognition has a wide range of application prospects in areas such as artificial intelligence and human-computer interaction. Action feature models and action recognition models are the basis of human action recognition. Based on the simplification of human skeleton model, the complementary features information such as the main joint angle, speed and relative position of the human body joint are extracted and fused to describe the behavioral gestures. And the action is expressed with the gesture series. A behavioral action model is established. In order to facilitate calculating, Fourier interpolation is performed on each action sample in the action database which taking the most characteristic dimension of the action video as the standard to keep the action samples feature dimensions consistent and normalized. And the principal components are used to extracting the main components of the feature, reducing the feature dimensions and redundant information. A one-to-many multi-category action recognition model was established based on the theory of support vector machines. The action recognition experiment was carried out with the open human action video database. The results showed that the algorithm has good adaptability and practicality.
Speech Recognition of Moroccan Dialect Using Hidden Markov Models

&lt;p&gt;This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.&lt;/p&gt;
Neurally-Guided Structure Inference

Most structure inference methods either rely on exhaustive search or are purely data-driven. Exhaustive search robustly infers the structure of arbitrarily complex data, but it is slow. Data-driven methods allow efficient inference, but do not generalize when test data have more complex structures than training data. In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods. The key idea of NG-SI is to use a neural network to guide the hierarchical, layer-wise search over the compositional space of structures. We evaluate our algorithm on two representative structure inference tasks: probabilistic matrix decomposition and symbolic program parsing. It outperforms data-driven and search-based alternatives on both tasks.
Reinforcement Learning with Derivative-Free Exploration

Effective exploration is key to sample-efficient reinforcement learning. While the most popular general approaches (e.g., e-greedy) for exploration are still of low efficiency, derivative-free optimization also invents efficient ways of exploration for better global search, which reinforcement learning usually desires for. In this paper, we introduce a derivative-free based exploration called DFE as a general efficient exploration method for early-stage reinforcement learning. DFE overcomes the disadvantage of optimization inefficiency and pool scalability in pure derivative-free optimization based reinforcement learning methods. Our experiments show DFE is an efficient and general exploration method through exploring trajectories with DFE in deterministic off-policy method DDPG and stochastic off-policy method ACER algorithms, and applying in Atari and Mujoco, which represent a high-dimensional discrete-action environment and a continuous control environment.
Car steering in racing games

Many gamers use keyboards for playing computer games. Yet it is a commonly held belief that the control of the game is better with a more dedicated input device such as a joystick. This study set out to explore the effect of using keyboard and joystick for a car driving game. A car racing game experiment with n = 20 participants was set up. The results show that there is no difference between the two input devices. In fact, the spread in the measurements were larger for the joystick.