Reviewerly/exHarmony huggingface dataset:
An asymmetric color-image cryptosystem based on spiral phase transformation and equal modulus decomposition

In this paper, we propose an asymmetric color-image cryptosystem based on spiral phase transform (SPT) and equal modulus decomposition (EMD). The original image is divided into three-channel components of R, G and B firstly. Then we encrypt these components by two random phase mask (RPMs) modulations, two spiral phase transformations, and an equal modulus decomposition. Finally, we obtain four encryption keys which are RPM1, RPM2, an EMD's angle and the order q of the SPT, and produce two private keys (PKs) which are PK1 and PK2. The proposed method has enhanced the key sensitivity of the order q with 100 times, while it maintains the advantages of simple structure, high speed and less data amount. The proposed method is highly robust to iteration attack and other attacks. Numerical simulation results show the effectiveness and feasibility of the proposed cryptosystem.
Utilizing Social Media in Vocabulary Enhancement among Primary ESL Learners

ICT is one of the main mediums used in and out of classroom since Malaysian education system focuses on 21 st century learning.This study aims to investigate how pupils utilize social media to improve their vocabulary and possible challenges faced when using social media as a medium to learn vocabulary.The respondents of this quantitative study comprise of 53 nine and ten-year-old students.The respondents were selected using purposive sampling method from a school in Batu Kikir, Negeri Sembilan.The questionnaire consist of 6 sections and the final section was divided into 3 parts according to the themes, the first part measured to see if students utilized the features available in social media, followed by challenges and finally motivation factor.The findings show that students utilize features on social media as an effective way to learn vocabulary and social media acts as a motivating factor for primary ESL learners to learn vocabulary.The result of this study could be useful for future research for English as Second Language Learners.Focus for further research can be about the challenges faced by students when using social media to develop vocabulary.
Load Balancing Network by using Round Robin Algorithm: A Systematic Literature Review

The use of load balance on a network will be very much needed if the network is an active network and is widely accessed by users. A reason is that it allows network imbalances to occur. Round Robin (RR) algorithm can be applied for network load balancing because it is a simple algorithm to schedule processes so that it can provide work process efficiency. Authors use the Systematic Literature Review (SLR) method in which it can be applied for criteria selection during papers search to match the title being raised. SLR is divided into five stages, namely formalization of questions, criteria selection, selection of sources, selection of search results, and quality assessment. By using SLR, it is expected that papers according to criteria and quality can be found.
Hybrid-TE: Hybrid Translation-Based Temporal Knowledge Graph Embedding

Knowledge graph embedding has become a promising method for knowledge graph completion. In this work, we propose Hybrid-TE, a hybrid translation-based temporal knowledge graph embedding, which combines two translation models TransD and HyTE for modeling both temporal and multirelational facts. Benefiting from two underlying models, Hybrid-TE first builds entity and relation embeddings in separate vector space for modelling multi-relational facts, and then explicitly learns time information by translational embedding on timespecific hyperplanes. We observe that a simple combination of two models does not lead to a satisfactory predictive precision. We therefore propose to project a triplet to all time-specific hyperplanes on which it is temporally valid. Besides, we also explore extra negative relation samplings that differ from positive samplings in relations. We conduct extensive experiments with real datasets on link prediction, relation prediction and temporal scope prediction. Experiments show significant improvements over previous time-insensitive or time-aware models.
Camera calibration from very few images based on soft constraint optimization

Camera calibration is a basic and crucial problem in photogrammetry and computer vision. Although existing calibration techniques exhibit excellent precision and flexibility in classical cases, most of them need from 5 to 10 calibration images. Unfortunately, only a limited number of calibration images and control points can be available in many application fields such as criminal investigation, industrial robot and augmented reality. For these cases, this paper presented a two-step calibration based on soft constraint optimization, which is motivated by "no free lunch" theorem and error analysis. The key steps include (1) homography estimation with weighting function, (2) Initialization based on a simplified model, and (3) soft constraint optimization in terms of reprojection error. The proposed method provides direct access to geometric information of the object from very few images. After extensive experiments, the results demonstrate that the proposed algorithm outperforms Zhang's algorithms from the point of view of the success ratio, accuracy and precision.
Special Issue on Mobile Robots Navigation

In recent years, the presence of mobile robots in diverse scenarios has considerably increased, to solve a variety of tasks [...]
3D CUBE Algorithm for the Key Generation Method: Applying Deep Neural Network Learning-Based

Current encryption systems are run with a hybrid mode in which symmetric and asymmetric key methods are mixed. This hybrid mode is devised to employ the fast processing speed of the symmetric key while circumventing the difficulty of providing services due to computational complexity of the asymmetric key method by limiting its use to secured key exchange. To implement a secured symmetric key-based cryptographic system to circumvent the crack problem due to the emergence of quantum computers in asymmetric key exchanges, the problem of sharing a key transferred in the most secure manner should be solved while symmetric keys in use are kept up-to-date. This paper proposes a three-dimensional (3D) cube algorithm that can be used by creating the up-to-date key in a symmetric key encryption system to provide security resistance in a quantum computer environment. More specifically, it presents a solution to the secured key sharing method that can minimize the damage due to the pre-shared key (PSK) leakage. This is done by using the method of inducing the symmetric key creation based on deep neural network learning without sharing the PSK between systems to securely maintain the key while creating and using the key that is variably used through the symmetric key encryption system of the 3D cube algorithm. Thus, the proposed algorithm secures confidentiality and integrity of data transferred over a network because information that can be obtained by malicious attackers is small (because the symmetric key used in encryption and decryption is induced without exchanging the PSK with the application of deep neural network learning).
Quantum State Interferography

Quantum State Tomography (QST) has been the traditional method for characterization of an unknown state. Recently, many direct measurement methods have been implemented to reconstruct the state in a resource efficient way. In this letter, we present an interferometric method, in which, any qubit state, whether mixed or pure, can be inferred from the visibility, phase shift and average intensity of an interference pattern using a single shot measurement -- hence, we call it Quantum State Interferography. This provides us with a "black box" approach to quantum state estimation, wherein, between the incidence of the photon and extraction of state information, we are not changing any conditions within the set-up, thus giving us a true single shot estimation of the quantum state. In contrast, standard QST requires at least two measurements for pure state qubit and at least three measurements for mixed state qubit reconstruction. We then go on to show that QSI is more resource efficient than QST for quantification of entanglement in pure bipartite qubits. We experimentally implement our method with high fidelity using the polarisation degree of freedom of light. An extension of the scheme to pure states involving $d-1$ interferograms for $d$-dimensional systems is also presented. Thus, the scaling gain is even more dramatic in the qudit scenario for our method where in contrast, standard QST, without any assumptions, scales roughly as $d^2$.
Knowledge Graph Enhanced Third-Party Library Recommendation for Mobile Application Development

With the prevalence of smart terminal devices and the rapid development of the mobile Internet, mobile application markets become increasingly prosperous.Third-party libraries have played an essential role in mobile application development.These libraries can shorten development time, increase development efficiency, and improve development quality.Currently, a large number of third-party libraries have been published, which puts a heavy burden on developers in selecting appropriate libraries.Towards this issue, in this paper, we propose a novel third-party library recommendation approach by integrating topic modeling and knowledge graph techniques.In the topic modeling component, we extract topics from textual application descriptions and make recommendations based on libraries used by applications that share similar topics with the new application to develop.In the knowledge graph component, we leverage knowledge graph to incorporate structured information of third-party libraries and applications, as well as the interaction information of applications and libraries for the recommendation.Experiments conducted on a real-world dataset show that our proposed approach outperforms several state-of-the-art approaches in terms of recommendation performance.
A Modularized Neural Network with Language-Specific Output Layers for Cross-Lingual Voice Conversion

This paper presents a cross-lingual voice conversion framework that adopts a modularized neural network. The modularized neural network has a common input structure that is shared for both languages, and two separate output modules, one for each language. The idea is motivated by the fact that phonetic systems of languages are similar because humans share a common vocal production system, but acoustic renderings, such as prosody and phonotactic, vary a lot from language to language. The modularized neural network is trained to map Phonetic PosteriorGram (PPG) to acoustic features for multiple speakers. It is conditioned on a speaker i-vector to generate the desired target voice. We validated the idea between English and Mandarin languages in objective and subjective tests. In addition, mixed-lingual PPG derived from a unified English-Mandarin acoustic model is proposed to capture the linguistic information from both languages. It is found that our proposed modularized neural network significantly outperforms the baseline approaches in terms of speech quality and speaker individuality, and mixed-lingual PPG representation further improves the conversion performance.
Evidence for depression and schizophrenia in speech prosody

We developed automatic computational tools for the monitoring of pathological mental states -including characterization, detection, and classification.We show that simple temporal domain features of speech may be used to correctly classify up to 80% of the speakers in a two-way classification task.We further show that some features strongly correlate with certain diagnostic evaluation scales, suggesting the contribution of such acoustic speech properties to the perception of an apparent mental condition.
Teleporting quantum information encoded in fermionic modes

Quantum teleportation is considered a basic primitive in many quantum information processing tasks and has been experimentally confirmed in various photonic and matter-based setups. Here, we consider teleportation of quantum information encoded in modes of a fermionic field. In fermionic systems, superselection rules lead to a more differentiated picture of entanglement and teleportation. In particular, one is forced to distinguish between single-mode entanglement swapping, and qubit teleportation with or without authentication via Bell inequality violation, as we discuss here in detail. We focus on systems subject to parity superselection where the particle number is not fixed, and contrast them with systems constrained by particle number superselection which are relevant for possible practical implementations. Finally, we analyze the consequences for the operational interpretation of fermionic mode entanglement and examine the usefulness of so-called mixed maximally entangled states for teleportation.
Smartphone Identification via Passive Traffic Fingerprinting: A Sequence-to-Sequence Learning Approach

Passive cyber-security attacks do not require any modification of the data stream generated by the victim, nor the creation of a false statement; in particular, those attacks based on statistical analysis aim at acquiring sensible information by just analyzing traffic patterns. Our work sits on the conjecture that the PDCCH, which is transmitted in clear text, may be effectively used to statistically characterize the traffic generated by a smartphone in standby mode. Through this statistical signature, the attacker may then infer whether an unknown traffic pattern is generated by the victim user's terminal, guessing if the victim is in a certain geographical area, and in turn gaining the ability to track the victim's movements and/or to profile their habits. In this work, we propose a data collection and processing framework that successfully obtains such signatures. User data patterns (transport block sizes and communications direction) are retrieved by analyzing the mobile network scheduling. Hence, a sequence-to-sequence learning framework to extract smartphone signatures from passive traffic is put forward, and is experimentally validated using a dataset of 40 user traces, successfully identifying up to 90 percent of the users.
A Property Graph Data Model for a Context-Aware Design Assistant

[Context] The design of a product requires to satisfy a large number of design rules so as to avoid design errors. [Problem] Although there are numerous technological alternatives for managing knowledge, design departments continue to store design rules in nearly unusable documents. Indeed, existing propositions based on basic information retrieval techniques applied to unstructured engineering documents do not provide good results. Conversely, the development and management of structured ontologies are too laborious. [Proposition] We propose a property graph data model that paves the way to a context-aware design assistant. The property graph data model is a graph-oriented data structure that enables us to formally define a design context as a consolidated set of five sub-contexts: social, semantic, engineering, operational IT, and traceability. [Future work] Connected to or embedded in a Computer Aided Design (CAD) environment, our context-aware design assistant will extend traditional CAD capabilities as it could, for instance, ease: (1) the retrieval of rules according to a particular design context, (2) the recommendation of design rules while a design activity is being performed, (3) the verification of design solutions, (4) the automation of design routines, etc.
Impact of Governance on Financial Development: evidence from West Africa

The study explores the impacts of governance on financial development in West African countries.Data for the study were obtained from the World Bank Database covering the period from 2006-2017. The variables for governance comprises of the six world governance indicators while financial development was represented by ratio of bank deposit to GDP and domestic credit to private sector. The control variables included in the model are interest rate, inflation, GDP per capita and trade openness. The findings from the study revealed that political stability and absence of violence, and regulatory quality have significant effects on both proxies of financial development. Also, voice and accountability, trade openness and interest rate show significances on the ratio bank deposit to GDP while government effectiveness (negative), rule of law, and control of corruption show significances on domestic credit to private sector. Thus, improving the quality of governance through strengthening legal and institutional frameworks, enforcement of standards and empowering of supervisory agencies, introducing an efficient regulatory environment to facilitate financial inclusion will play significant roles in the pace of financial development in the West Africa region. Keywords: Governance, Financial Development and West Africa
Developing Technical Creativity in Future Engineering Educators

The dynamics of scientific and technological progress incorporates new content into the goals and objectives of special education in training specialists as creative and thinking individuals.As a result, it leads to specific problems that must be solved immediately.There is a need to rethink theoretical and psychoeducational foundations and accumulated practical experience of higher education institutions.The research aims to prove theoretically and to verify the effectiveness of pedagogical conditions for developing technical creativity in future engineering educators experimentally.The pedagogical conditions for developing technical creativity in future engineering educators are as follows: creating appropriate content and nature of teachers' activities; providing a creative environment for developing technical creativity.It must be noted that 261 students were involved in the experiment (a bachelor's degree): the control group (CG) consists of 134 students and the experimental group (EG) -of 127 students.Relevant control tests and questionnaires were employed during the experimental stage, which allowed assessing the effectiveness of the proposed activities.According to the results of the calculations, the statistics of the criterion is Тobs.> Тcrit.(14.94>11.34).Thus, with a probability of 0.99, one can argue that the level of technical creativity in EG students differs from the level in CG.Indeed, the high level is 29.92% G and the average level in 53.54%.At the same time, the high and average levels of CG students are 16.42% and 47.76% respectively.The analysis of experimental work shows the expediency of the justified and verified pedagogical conditions for developing the technical creativity in future engineering educators.
Radio Environment Map Construction Using Super-Resolution Imaging for Intelligent Transportation Systems

Radio environment map (REM) has emerged as a crucial technology to improve the robustness of intelligent transportation systems (ITS) by enhancing network planning and spectrum resource utilization. To construct a precise REM, optimizing deployment of sensor nodes and increasing spatial interpolation accuracy are two main directions. Given the deployment of sensor nodes, high resolution (HR) spatial interpolation would still bring about huge computing overhead, which is not practical for realtime applications. In order to improve the efficiency and accuracy of REM construction, we propose a super-resolution (SR) based REM construction method, which is composed of Kriging interpolation, dictionary learning and random forest. In our method, both low resolution (LR) and HR REM image sets are generated and trained to obtain a random forest model. With spectrum data from the limited number of sensor nodes, a SR REM can be acquired by the proposed method. Simulation results demonstrate that our method can greatly shorten the construction time of REM while maintaining high accuracy.
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we present Quadtree Generating Networks (QGNs), a novel approach able to drastically reduce the memory footprint of modern semantic segmentation networks. The key idea is to use quadtrees to represent the predictions and target segmentation masks instead of dense pixel grids. Our quadtree representation enables hierarchical processing of an input image, with the most computationally demanding layers only being used at regions in the image containing boundaries between classes. In addition, given a trained model, our representation enables flexible inference schemes to trade-off accuracy and computational cost, allowing the network to adapt in constrained situations such as embedded devices. We demonstrate the benefits of our approach on the Cityscapes, SUN-RGBD and ADE20k datasets. On Cityscapes, we obtain an relative 3% mIoU improvement compared to a dilated network with similar memory consumption; and only receive a 3% relative mIoU drop compared to a large dilated network, while reducing memory consumption by over 4×. Our code is available at https://github.com/kashyap7x/QGN.
Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people’s activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.