Reviewerly/exHarmony huggingface dataset:
An Improved Small-World Topology for Optimizing the Performance of Echo State Network

By optimizing the topology of echo state network (ESN) to improve network performance, SW-ESN (an ESN with Small-Worldness) is proposed in this paper. The dynamic neuron pool has small-world property, and the learning performance of the small-world topology as a reservoir of ESN is studied. Then, establish the relationship function between the distance of the network nodes and its connection weight. The inversion operator is used to invert the weight with a certain probability to ensure the reasonable distribution of the positive and negative of the weight. Study the parameter adjustment of SW-ESN, and the impact of small-world topology on the echo state network via small-worldness, memory capacity, and nonlinear time series prediction. Through experiments, it is well proved the superiority of ESN with small-world topology in the aspect of fitting ability and robustness.
Automatic Diacritic Recovery with focus on the Quality of the training Corpus for Resource-scarce Languages

The development and availability of high quality corpus for many African languages is still hampered by dearth of appropriate software tools and devices. To be able to rapidly create large quantities of high quality corpus of majority of African and Nigerian languages, a diacritic tool is required. The presentation of texts of natural languages without diacritic marks presents significant problems to both human and computational processing systems due to partial or total loss of the accompanying grammatical, syntactic and or semantic information. This paper investigated the effect of diacritic quality of a small-sized training corpus on the classification accuracy of some simple and commonly used machine learning algorithms for diacritic restoration tasks following the character-based approach. The classification accuracy of eight of the diacritic-bearing characters of Yoruba language of Nigeria were investigated. The results show that the completeness and correctness of diacritics has a significant effect on the performance of the algorithms; decision tree algorithm produced the overall best accuracy response of 3.22 % to the data quality improvement. The observations from the learning behaviours of the algorithms suggests that a 100,000 words corpus is adequate to train a decision tree model for automatic diacritic restoration for Yoruba language but insufficient to obtain a state-of-the art results for the LDA, LOGREG and SVM algorithms.
StealthPDF: Data hiding method for PDF file with no visual degradation

Conventional data hiding methods for PDF file insert a payload data by slightly modifying the position of characters in a document. Even if the changes are small, a certain degree of visual distortion is inevitably introduced to the PDF file. In this work, we propose a new data hiding method that splits the space value between characters. Specifically, a space value is split into two or more related values. Except for the first value which is reserved to store the corrective data, each of the related values encodes a segment of the payload data. When the PDF file is opened by a PDF viewer, the visual appearance is exactly the same as its original counterpart, i.e., complete quality preservation. To prevent direct observation of PDF file, access control is introduced by setting an owner password, which is a built-in function in the PDF standard. In the best case scenario, 38,160 bits can be hidden, while the observed file size increase is 12,776 Bytes.
Distributed Hybrid Kalman Temporal Differences for Reinforcement Learning

The paper focuses on development of model-free and distributed Reinforcement Learning (RL) algorithms for multi-agent networks. The goal is to learn optimal control policies directly from smart agents’ cooperative interactions among themselves and with the environment. In model-free RL methods with continuous state-space, typically, the value function needs to be approximated. In this regard, Deep Neural Networks (DNNs) provide an attractive modeling mechanism to approximate the value function using sample transitions. Direct utilization of DNN-based single-agent approaches, however, failed to fully overcome the complexities of the multi-agent scenarios. In different multi-agent cooperative scenarios, Kalman-based methodologies could be used as an efficient alternative. Such an approach, however, commonly requires a-priori information about the system (such as noise statistics) to perform efficiently. To address the aforementioned challenge, the paper proposes a Distributed Hybrid (multiple model) Kalman Temporal Difference framework (DH-KTD). The proposed DH-KT framework adapts the parameters of the localized filters in a distributed fashion using the observed states and rewards in an optimized fashion. Experimental results based on a multi-agent benchmark RL problem illustrate efficacy of the proposed framework.
DiSCS: A New Sequence Segmentation Method for Open-Ended Learning Environments

Open-ended learning environments afford students opportunities to explore, manipulate, and test concepts, and have the potential to provide students with feedback and support by leveraging the log data generated by them. However, within open-ended contexts, student log data is often noisy and identifying periods of meaningful activity is difficult. This paper introduces a new sequence mining method to overcome this challenge. The Differential Segmentation of Categorical Sequences (DiSCS) algorithm finds segments within a sequence of actions that are maximally or near-maximally different from their immediate neighbors. Segments are then clustered to reveal common periods of student activity. We examine the performance of this method under a variety of conditions to find how well DiSCS can identify where different states of simulated activity start and end. We report that when provided with only the observed actions, DiSCS is able to identify the hidden states of simulated student activity with strong and very strong associations. This strong performance is robust across a variety of contexts including those where observed actions are noisy or common to multiple states. We discuss the implications and limitations of this method for open-ended learning environments.
Convoluted fashion classifier prediction using Neural Networks

Abstract The conventional way of choosing a fashion catalogue has a lot of drawbacks and is yet to be resolved. Struck in a dilemma of what to choose and what not to, not wearing fashion items that are appropriate, and lured by terms that are misleading such as 50 percent off limited period offer, when in reality the prize was never higher in the first place. These are the flaws that go under the radar for humans, but that is not the case with machines. The proposed system when given an image of a fashion item, by the user makes use of a Convolution Neural Network to find the type of item, then suggest items based on occasion. The system also gives you the prize of that particular item over a period of time in various e commerce sites, and if there is any chance of drop in prize in the future. This eliminates the human part of errors while choosing a fashion catalogue and makes everything automated. The next advantage of our site is to resell our old clothes at best prize so our used clothes are not wasted and it can be used by others.
Causal Dynamic Bayesian Networks for the Management of Glucose Control in Gestational Diabetes

Patients suffering from chronic conditions may need to make frequent decisions about the management of their condition in partnership with their health professionals. However, this may not be possible as appointments are not always scheduled according to necessity but instead at a fixed frequency. Remote monitoring technology has the potential to generate patient data but without intelligent systems capable of analysing the data and offering advice, more data just increase the person’s dependency on clinical staff for its interpretation. Decision-support systems that can give people more autonomy in the management of their condition can therefore benefit both the affected person and clinicians. We propose the use of Dynamic Bayesian Networks built from expert knowledge to interpret data and support decision-making, offering advice to patients suffering from a chronic condition. We argue that expert knowledge is needed as well as data to build such a decision-support system as the data that would be required to use machine learning will never be available in the current clinical system with all treatment decisions made at appointments scheduled at fixed intervals. We illustrate the methodology using a case study in Gestational Diabetes.
Comparison of Investment Direction and Efficiency Analysis of China’s Rural Poverty Alleviation Fund

2020 is the year of decisive victory in poverty alleviation. How to allocate poverty alleviation funds scientifically is the key to whether we can win this battle against poverty. This paper analyzes the input-output efficiency of China’s rural poverty alleviation funds by constructing a principal component estimation model, and draws the following conclusions: Generally speaking, the output efficiency of national poverty alleviation funds is increasing year by year, but it has achieved poverty alleviation in infrastructure services such as personal clothing, food, housing and transportation. The marginal effect is getting weaker and weaker. The proportion of funds invested in health poverty alleviation and education poverty alleviation is relatively large, and the poverty alleviation effect achieved is relatively significant. Finally, relevant countermeasures and suggestions are put forward for the direction of poverty alleviation funds.
The evolution and consequences of digital rights management in relation to online music streaming

Abstract Streaming services now provide the dominant way in which music is distributed and consumed online. Digital rights management (DRM) lies at the heart of this trend and has evolved alongside a movement from copy-based to streaming-based consumption. This shift poses a number of new and unique issues. Music streaming services have changed the nature of the product offered, with musical content becoming de-bundled and reduced to a series of permissions covered by DRM and associated licences, leaving users trapped in a permission-based system. This may create tension with copyright law principles regarding personal ownership and exhaustion of rights in relation to secondary markets, but through analysing relevant US and European case law it can be demonstrated that there is little, if any, legal opportunity for digital secondary markets to emerge. There are also further specific consequences which may affect artists relating to musical diversity and the composition of popular music and, also, consequences regarding the changing nature of the Internet itself. In this context copyright remains centrally important, but only in establishing the initial proprietary rights that enable subsequent DRM and licence-based online exploitation, indicative of a re-establishment of record industry power that is now allied to streaming platforms.
An Ego Network Embedding Model via Neighbors Sampling and Self-attention Mechanism

In recent years, deep neural networks on graph-structured data have achieved impressive performance. However, few existing models can embed a large-scale graph efficiently and effectively with lower memory requirements and higher generalization capability to accommodate directed graphs. In this paper, we propose a novel graph neural network, STAN, which uses a sampling scheme i.e., Smote-Top-K, to sample a node's neighbors and adopts a self-attention mechanism to attend over the neighbors. The Smote-Top-K and the self-attention mechanism also allow STAN to train a node and its sampled neighbors in a batch. The node and its neighbors in a batch form an ego network, so we also call STAN as an ego network embedding model. The Smote-Top-K and the self-attention mechanism make STAN achieve better results and the batch training make STAN train with higher gradient computation efficiency and lower memory requirements. Meanwhile, STAN can accommodate directed graphs and larger-scale graphs. This is validated through experiments on the widely-used AMiner dataset. And the experiments on three benchmark datasets, i.e., Cora, Pubmed and PPI, demonstrate that STAN outperforms state-of-the-art models in GCN, GraphSAGE, FastGCN and GATs by improving accuracy about 6.5% with comparable training time.
Four Barriers to Facilitating 21st Century Competencies Through Digitalization

The purpose of this chapter is to identify and offer solutions based on four barriers to facilitating 21st century competencies through digitalization. The first barrier includes the technological challenges faced by families, including devices and reliable broadband internet. The second barrier explores the training and support needed by teachers in implementing digital tools and instructional technology. The third barrier is a pedagogical shift from teacher-centered to student-centered teaching and learning, especially in remote environments where this approach is needed for student engagement. The fourth barrier is training for parents and families to become familiar with the technology and digital tools that their children are using. Practical solutions backed by research are presented by the author for consideration by teachers, administrators, and the school community. Prompts for reflection and discussion based on identified barriers and real-life examples are presented by the author at the conclusion of the chapter.
Convolutional Sliding Window Based Model and Synthetic Dataset for Fast Feature Detection

Recent feature detection and matching algorithms are based on deep learning models. However, the image feature patches used to train these models are the result of handcrafted detectors. On the other hand, feature detection with these models requires a lot of processing time due to the use of a sliding window. In this article, we propose a new convolutional sliding window based CNN model for fast feature detection, as well as a new synthetic image dataset of the features, used to train the model, in order to avoid the use of traditional detectors. The model is evaluated on the dataset and compared to other architectures in terms of detection performance and processing time. We also propose a method to extract feature descriptors directly from the detection model, and evaluate the descriptor invariance by image feature matching.
Multi-agent Ludo Game Collaborative Path Planning based on Markov Decision Process

The multi-agent path planning problem is usually addressed via the use of reinforcement learning methods. In this article, a new Lludo game path planning algorithm based on Q-learning was proposed, and a set of cooperation proprieties that the agents have to follow in order to reach the goal location. To accomplish that a Lludo game environment modeling is introduced according to the Markov decision process (MDP) principles. The main objective of this work is to increase agents’ cooperation rate with the aim of decreasing the execution time of the assigned tasks. To demonstrate the improvement produced by this proposal, the path planning algorithm is applied in comparison with a greedy strategy also based on Q-learning. During this comparison, the execution time as well as the agents’ reward acquisition were examined in different cases. The simulation results reflect the advantages provided by this new algorithm in comparison with related work.
A Defense Mechanism Against Transient Execution Attacks On SMT Processors

Transient execution attack does not affect the state of processor microarchitecture, which breaks the traditional definition of correct execution. It not only brings great challenges to the industrial product security, but also opens up a new research direction for the academic community. This paper proposes a defense mechanism for SMT processors against launching transient execution attacks using shared cache. The main structure includes two parts, a security shadow label and a transient execution cache. In the face of the side channel attacks widely used by transient execution attack, our defense mechanism adds a security shadow label to the memory request from the thread with high security requirement, so that the shared cache can distinguish the cache requests from different security level threads. At the same time, based on the record of security shadow label, the transient execution cache is used to preserve the historical data, so as to realize the repair of the cache state and prevent the modification of the cache state by misspeculated path from being exploited by attackers. Finally, the cache state is successfully guaranteed to be invisible to any attacker’s cache operations. This design only needs one operation similar to the normal memory access, thus reducing the memory access pressure. Compared with the existing defense schemes, our scheme can effectively prevent Spectre attack, and the overhead of performance is only 3.9%.
Factors Affecting the Sense of Scale in Immersive, Realistic Virtual Reality Space

In this study, we analyze and identify a proper scale value when presenting real world space and everyday objects in immerse VR. We verify the impact of usage of reference points in the form of common objects known to the user such as windows, doors and furniture in the sense of scale in VR. We also analyze user behavior (position, rotation, movement, area of interest and such) in the scale setting task. Finally, we propose optimal scale values for single objects presentation, architectural space with many points of references and a large scale space with less to no points of reference. The experiments were conducted on two groups: the Experts (architects) and Non-experts (common users) to verify the translation of real-world object size analysis skills into the same capacity in the virtual world. Confirmation of the significance of the pre-immersion in VR for a sense of scale accuracy is also described.
Impact of Cyberattacks on Electronic Patient Health/Medical Records

Digital health offers several benefits from different perspectives. Therefore, the transition from paper to meaningful cost-effective electronic health records (E-HR) is the focus of several countries. In the literature, E-HR functionalities are discussed extensively. It may be noted that security measures to curb potential cyberthreats in E-HR need further attention. The working of health monitoring needs a transformation from a reactive mode to a proactive and predictive mode using contemporary technologies. While implementing the E-HR security/privacy perspective is essential since the safety of the medical records is key to the safe life of the patient. This paper highlights security/privacy issues, cyberthreats to E-HR, the pros, and cons of contemporary technologies such as IoT networks and discusses meaningful E-HR so that patients and society significantly benefit as believed by experts and policymakers.
Adaptive Approach to Detect Optimal Edge Orientation Using the Variogram

This research work suggests an adaptive approach to image segmentation. The approach aims to analyze variogram properties such as the direction, the renewal period of texture pattern, and the object's size to adapt the optimal operator. The algorithms generate a directional edge map (DeMAP) using the variograms. It will guide and automatically adapt the optimal edge operator. Our method is tested on different image categories: Natural images, Controlled scenes, Textured images, and especially in medical images; blood cells images.
Removal of Noise in an Image using Boundary Detection Technique

Abstract Image data recorded by devices contain mistakes or noises due to geometry and brightness values of the pixels. These mistakes are rectified using proper mathematical models. Examples of models include contrast and edge enhancement, pseudo-colouring, shadow removal, noise filtering, sharpening, and magnifying. Applying filters to remove a noise may lead to loss of some important data like edges in the image. The proposed work removes the noise from the image using local neighbourhood processing and preserves the edges using boundary-based approach. The noise filtered image is tested against the parameter entropy to validate the result.
An Efficient Certificate-Based Signature Scheme in the Standard Model

Certificate-based cryptography optimizes certificate management of the traditional public key infrastructure (PKI) and overcomes the problems of the key escrow and the key distribution in identity-based cryptography (IBC). Currently, many certificate-based signature (CBS) schemes have been proposed in the random oracle model or standard model. However, all existing schemes in the standard model are quietly inefficient. In this paper, we propose an efficient certificate-based signature over bilinear groups in the standard model. Compared with the state-of-the-art constructions in the standard model, the proposed scheme is superior in both communication cost and computational overhead.
Security analysis of Subterranean 2.0

Abstract Subterranean 2.0 is a cipher suite that can be used for hashing, authenticated encryption, MAC computation, etc. It was designed by Daemen, Massolino, Mehrdad, and Rotella, and has been selected as a candidate in the second round of NIST’s lightweight cryptography standardization process. Subterranean 2.0 is a duplex-based construction and utilizes a single-round permutation in the duplex. It is the simplicity of the round function that makes it an attractive target of cryptanalysis. In this paper, we examine the single-round permutation in various phases of Subterranean 2.0 and specify three related attack scenarios that deserve further investigation: keystream biases in the keyed squeezing phase, state collisions in the keyed absorbing phase, and one-round differential analysis in the nonce-misuse setting. To facilitate cryptanalysis in the first two scenarios, we novelly propose a set of size-reduced toy versions of Subterranean 2.0: Subterranean-m. Then we make an observation for the first time on the resemblance between the non-linear layer in the round function of Subterranean 2.0 and SIMON’s round function. Inspired by the existing work on SIMON, we propose explicit formulas for computing the exact correlation of linear trails of Subterranean 2.0 and other ciphers utilizing similar non-linear operations. We then construct our models for searching trails to be used in the keystream bias evaluation and state collision attacks. Our results show that most instances of Subterranean-m are secure in the first two attack scenarios but there exist instances that are not. Further, we find a flaw in the designers’ reasoning of Subterranean 2.0’s linear bias but support the designers’ claim that there is no linear bias measurable from at most $$2^{96}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>96</mml:mn> </mml:msup> </mml:math> data blocks. Due to the time-consuming search, the security of Subterranean 2.0 against the state collision attack in keyed modes still remains an open question. Finally, we observe that one-round differentials allow to recover state bits in the nonce-misuse setting. By proposing nested one-round differentials, we obtain a sufficient number of state bits, leading to a practical state recovery with only 20 repetitions of the nonce and 88 blocks of data. It is noted that our work does not threaten the security of Subterranean 2.0.